Patent Application 17560300 - METHODS AND DEVICES FOR A COLLABORATION OF - Rejection
Appearance
Patent Application 17560300 - METHODS AND DEVICES FOR A COLLABORATION OF
Title: METHODS AND DEVICES FOR A COLLABORATION OF AUTOMATED AND AUTONOMOUS MACHINES
Application Information
- Invention Title: METHODS AND DEVICES FOR A COLLABORATION OF AUTOMATED AND AUTONOMOUS MACHINES
- Application Number: 17560300
- Submission Date: 2025-05-13T00:00:00.000Z
- Effective Filing Date: 2021-12-23T00:00:00.000Z
- Filing Date: 2021-12-23T00:00:00.000Z
- National Class: 703
- National Sub-Class: 001000
- Examiner Employee Number: 99685
- Art Unit: 2188
- Tech Center: 2100
Rejection Summary
- 102 Rejections: 1
- 103 Rejections: 8
Cited Patents
No patents were cited in this rejection.
Office Action Text
DETAILED ACTION A summary of this action: Claims 1-25 have been presented for examination. This action is non-Final. Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process or mathematical concept without significantly more. Step 1: Claims 1-21 are directed to a device, which is a machine and is a statutory category invention. Claims 22-23 are directed to a non-transitory computer-readable medium, which is a manufacture and a statutory invention. Claims 24-25 are directed to a system, which is a machine and a statutory invention. Therefore, claims 1-25 are directed to patent eligible categories of invention. Claim 1 Step 2A, Prong 1: Independent claims 1, 22 and 24 similarly recite an abstract idea because the claims are derived from Mental Processes based on concepts performed in the human mind or with the aid of pencil and paper or in the alternative Mathematical Concepts using mathematical relationships, mathematical formulas or equations, or mathematical calculations. Claims 1, 22, and 24 similarly recite a processor configured to determine a layout for a plurality of automated machine clusters to be deployed in an environment based on a plurality of operation policies and an input task, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. Accordingly, the limitation covers mental processes of assessing predefined environments and associated operation polices for the plurality of autonomous machines as described in [0092] of the specification. Claims 1, 22, and 24 similarly recite a processor configured wherein each operation policy provides a policy to operate one or more automated machines of one of the plurality of automated machine clusters for a trained task based on one or more policy parameters as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind, covers mental processes of assessing each operation policy for a trained task based on policy parameters as described in [0091] of the specification. Claims 1, 22, and 24 similarly recite a processor configured to adjust the one or more policy parameters of at least one of the plurality of operation policies based on the determined layout in the environment and the input task, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. Accordingly, the limitation covers mental processes of assessing the adjustment of policy parameters of one of the plurality operation polices based on the determined layout found in a predefined environment and input task as described in [0093] of the specification. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper. Dependent claims 2-21, 23, and 25 further narrow the abstract ideas, identified in the independent claims. See analysis below. Step 2A, Prong 2: The judicial exception is not integrated into a practical application. Claim 1 recites the additional element of “processor” as in independent claims 1, 22, and 24, and dependent claims 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 17, 18, 21, and 23, “automated machines” as in independent claims 1, 22, and 24 and dependent claims 4, 5 , 7, 8, 10, 12, 14, 16, 18, 20, 21, and 25, “user interface” as in dependent claim 3, “input” as in dependent claims 5, 15, 16, and 18, “output” as in dependent claims 5, 8, 15, and 23, “graphical neural network” as in dependent 8, “conveyor belt” as in dependent claim 10, “edge computing device” as in dependent claim 19, “controller” as in dependent claim 20, “plurality of sensors” as in dependent claim 21, “non-transitory computer-readable medium” as in dependent claim 22 and dependent claim 23, “system” as in independent claim 24 and dependent claim 25, “memory” as in independent claim 24, “device” as in independent claim 24 and dependent claim 25, this limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). Alternatively, this additional element merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)). The limitation of configured to receive information indicating the input task, in dependent claim 2, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not integrate a judicial exception into a practical application. The limitation of configured to select the plurality of operation policies based on the indicated input task, in dependent claim 2, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f). The limitation of communicatively coupled to receive the input task, in independent claim 3, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not integrate a judicial exception into a practical application. The limitation of configured to receive an input and provide an output comprising at least one generated layout, in independent claim 5, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not integrate a judicial exception into a practical application. The limitation of configured to select one of the plurality of generated layouts as the determined layout, in dependent claim 11, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f). The limitation of configured to select the one of the plurality of generated layouts based on at least one of estimated routes for the one or more automated machines, in dependent claim 12, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f). The limitation of configured to receive an input comprising the one or more policy parameters of the respective operation policy and provide an output indicating one or more intermediate policy parameters, in dependent claim 15, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not integrate a judicial exception into a practical application. The limitation of configured to provide instructions to deploy the plurality of automated machine clusters according to the one or more final policy parameters for each operation policy, in independent claim 20, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f). The limitation of configured to control each automated machine cluster according to the one or more final policy parameters for the respective operation policy and the respective operation policy, in dependent claim 20, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not integrate a judicial exception into a practical application. The limitation of configured to deploy each automated machine cluster according to the determined layout, in dependent claim 20, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f). The limitation of communicatively coupled to receive sensor data, in dependent claim 21, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not integrate a judicial exception into a practical application. The limitation of configured to obtain environment data representing the environment based on the received sensor data, in dependent claim 21, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f). The limitation of configured to provide instructions to the one or more automated machines based on the environment data, in dependent claim 21, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f). The limitation of wherein the one or more instructions further cause the processor to adjust the one or more policy parameters of each operation policy by using a machine learning model configured to receive an input comprising the one or more policy parameters of the respective operation policy and provide an output indicating one or more intermediate policy parameters, in independent claim 23, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not integrate a judicial exception into a practical application. The limitation of configured to store an operation policy and one or more policy parameters for each of the plurality of automated machine clusters, in independent claim 24, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not integrate a judicial exception into a practical application. The limitation of configured to transmit sensor data to the device., in independent claim 25, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not integrate a judicial exception into a practical application. Dependent claims 2-21, 23 and 25 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above. The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application. Therefore, the dependent claims do not integrate the claimed invention into a practical application. Step 2B: The claims do not amount to significantly more. The judicial exception does not amount to significantly more. Claim 1 recites the additional element of “processor” as in independent claims 1, 22, and 24, and dependent claims 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 17, 18, 21, and 23, “automated machines” as in independent claims 1, 22, and 24 and dependent claims 4, 5 , 7, 8, 10, 12, 14, 16, 18, 20, 21, and 25, “user interface” as in dependent claim 3, “input” as in dependent claims 5, 15, 16, and 18, “output” as in dependent claims 5, 8, 15, and 23, “graphical neural network” as in dependent 8, “conveyor belt” as in dependent claim 10, “edge computing device” as in dependent claim 19, “controller” as in dependent claim 20, “plurality of sensors” as in dependent claim 21, “non-transitory computer-readable medium” as in dependent claim 22 and dependent claim 23, “system” as in independent claim 24 and dependent claim 25, “memory” as in independent claim 24, “device” as in independent claim 24 and dependent claim 25, this limitation does not amount to significantly more because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). Alternatively, this additional element merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)). The limitation of configured to receive information indicating the input task, in dependent claim 2, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not amount to significantly more. The limitation of configured to select the plurality of operation policies based on the indicated input task, in dependent claim 2, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f) and does not amount to significantly more. The limitation of communicatively coupled to receive the input task, in independent claim 3, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not amount to significantly more. The limitation of configured to receive an input and provide an output comprising at least one generated layout, in independent claim 5, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not amount to significantly more. The limitation of configured to select one of the plurality of generated layouts as the determined layout., in dependent claim 11, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f) and does not amount to significantly more. The limitation of configured to select the one of the plurality of generated layouts based on at least one of estimated routes for the one or more automated machines, in dependent claim 12, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f) and does not amount to significantly more. The limitation of configured to receive an input comprising the one or more policy parameters of the respective operation policy and provide an output indicating one or more intermediate policy parameters, in dependent claim 15, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not amount to significantly more. The limitation of configured to provide instructions to deploy the plurality of automated machine clusters according to the one or more final policy parameters for each operation policy, in independent claim 20, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f) and does not amount to significantly more. The limitation of configured to control each automated machine cluster according to the one or more final policy parameters for the respective operation policy and the respective operation policy, in dependent claim 20, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not amount to significantly more. The limitation of configured to deploy each automated machine cluster according to the determined layout, in dependent claim 20, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f) and does not amount to significantly more. The limitation of communicatively coupled to receive sensor data, in dependent claim 21, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not amount to significantly more. The limitation of configured to obtain environment data representing the environment based on the received sensor data, in dependent claim 21, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f) and does not amount to significantly more. The limitation of configured to provide instructions to the one or more automated machines based on the environment data, in dependent claim 21, only amounts to mere instructions to apply as it only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished MPEP 2106.05(f) and does not amount to significantly more. The limitation of wherein the one or more instructions further cause the processor to adjust the one or more policy parameters of each operation policy by using a machine learning model configured to receive an input comprising the one or more policy parameters of the respective operation policy and provide an output indicating one or more intermediate policy parameters, in independent claim 23, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not amount to significantly more. The limitation of configured to store an operation policy and one or more policy parameters for each of the plurality of automated machine clusters, in independent claim 24, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not amount to significantly more. The limitation of configured to transmit sensor data to the device., in independent claim 25, can be viewed as merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not amount to significantly more. Dependent claims 2-21, 23 and 25 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above. The additional elements have been considered both individually and as an ordered combination in to determine whether they amount to significantly more. Therefore, the dependent claims do not amount to significantly more. Therefore, the claims as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.” The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims. Dependent claim 2 recites “configured to train one of the plurality of operation policies based on the input task,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claim 4 recites “wherein the input task further comprises information indicating at least one of a plurality of tasks, input task parameters, a number of automated machines for each automated machine cluster, an automated machine type for each of the automated machine clusters, or the environment,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claim 5 recites “configured to determine the layout based on a machine learning model,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claim 5 recites “configured to generate a plurality of layouts based on an input information indicating at least one of dimensions of the environment, features of the plurality of automated machine clusters, constraints between the plurality of automated machine clusters, a target performance metric for the each automated machine cluster, safety requirements of the plurality of automated machine clusters, and an image representing the environment, which is directed to “Mental Processes.” Dependent claim 6 similarly recites “configured to generate the plurality of layouts using a generative neural network configured to provide a plurality of generated layouts based on the input information,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claim 6 recites “wherein the generative neural network comprises a generative adversarial network model,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claim 7 recites “wherein the generated plurality of layouts comprises information indicating at least one of a location, a centroid, or a pose for the each one of the plurality of automated machine clusters,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claim 8 recites “configured to estimate the interactions using a graphical neural network configured to provide an output comprising an adjacency matrix indicating interactions between the plurality of automated machine clusters,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claim 9 recites “configured to estimate a performance index for each one of the plurality of generated layouts based on a performance function comprising parameters with respect to the estimated interactions,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claim 10 recites “wherein the performance function comprises one or more parameters comprising an indication of at least one of inter-cluster distances between each one of the plurality of automated machine clusters, path lengths for the one or more automated machines configured to transport a material for each one of the plurality of automated machine clusters, a sequence for each one of the plurality of automated machine clusters, a weight of an object, a speed of a conveyor belt transporting the object, robotic manipulation parameters, a period of time defining a duration of a partial task, an order of multiple partial tasks, or deadlines for the partial tasks,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claim 11 recites “configured to determine a performance score for each one of the plurality of generated layouts using a machine learning model,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claims 12 similarly recite “based on at least one of estimated routes for the one or more automated machines, estimated routes for the one or more automated machines estimated to transport a material, or work sequences of automated machines,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claims 13 similarly recite “configured to adjust the one or more policy parameters of the plurality of operation policies based on the determined layout in the environment and the input task,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claims 14 similarly recite “wherein the one or more policy parameters comprise information indicating coordinates for the one or more automated machines of the respective automated machine cluster to perform a task,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claims 15 similarly recite “configured to adjust the one or more policy parameters of each operation policy by using a machine learning model,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claims 16 similarly recite “wherein the input of the machine learning model further comprises information indicating at least one of dimensions of the environment, features of the respective automated machine cluster, a target performance metric for the respective automated machine cluster, safety requirements of the respective automated machine cluster, constraints between the respective automated machine cluster and other ones of the plurality of automated machine clusters, an image representing the determined layout, assigned tasks for the respective automated machine cluster, or the adjacency matrix,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claims 17 similarly recite “configured to train each operation policy by a reinforcement learning model using the one or more intermediate policy parameters to obtain one or more final policy parameters,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claims 18 similarly recite “configured to determine a state based on the determined layout and the one or more intermediate policy parameters to train each operation policy,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claims 18 similarly recite “wherein the determined state comprises a state information indicating at least one of location of the one or more automated machines of the plurality of automated machine clusters, a beginning and a final positions for each automated machines of the plurality of automated machine clusters, a status of each automated machines for the plurality of automated machine clusters indicating whether the respective automated machine is loaded or unloaded, a status of each automated machine clusters indicating whether the respective automated machine cluster is occupied or available, vector positions of the each automated machines of the plurality of automated machine clusters,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Dependent claims 19 similarly recite “wherein the device is an edge computing device or an edge computing node,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.” Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim(s) 1, 7, 22, and 24 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by ASATSU (WO 2020138485 A1), herein, ASATSU. Claim 1 Claim 1 is rejected because ASATSU teaches a processor configured to: determine a layout for a plurality of automated machine clusters to be deployed in an environment based on a plurality of operation policies and an input task, ASATSU ([Description] | EPU Search Report Reference [0044], [0045], [Figure 3] “The work plan creation device 10 is, for example, a computer including a processor (processor) such as a CPU and a storage device such as a memory that stores a program (work plan creation program).”) See also ASATSU ([Description] | EPU Search Report Reference [0066], [0067], [Figure 4] “When the calculation of the motion of each of the robots R1 to R14 is completed (the calculation of the robot motion is completed), in the subsequent step S130, the robot layout calculation unit 26 of the arithmetic device 20 calculates the robot layout (determine a layout).”) See also ASATSU ([Description] | EPU Search Report Reference [0041], [0067], [Figure 1] “In the case of the present embodiment, the plurality of robots R1 to R14 (plurality of automated machine clusters to be deployed in an environment) are articulated welding robots, and a work tool T, which is a clamp-type welding gun, is mounted on the tip thereof. The robots R1 to R14 may have the same work ability (for example, workable range, loadable welding gun weight, etc.) or different work ability.”) See also ASATSU ([Description] | EPU Search Report Reference [0053] “Specifically, in step S200, the work distribution changing unit 32 determines that there is no feasible robot placement in the work distribution (based on a plurality of operation policies) calculated by the work distribution calculating unit 22 in step S110 or that a work plan with an evaluation value equal to or larger than a predetermined value is created. Since it does not exist, the work distribution calculated in step S110 is changed.” See also ASATSU ([Figure 4].) See also ASATSU ([Description] | EPU Search Report Reference [0041] “For example, in the case of a robot that has a plurality of robot placement positions for work that can work at the assigned work locations (an input task), the placement position that does not cause inter-robot interference is determined from among the plurality of robot placement positions.”) ASATSU also teaches wherein each operation policy provides a policy to operate one or more automated machines of one of the plurality of automated machine clusters for a trained task based on one or more policy parameters ASATSU ([Description] | EPU Search Report Reference [0041], [0053], [0054] “The production line PL (one or more automated machines) shown as an example in FIG. 1 includes a plurality of robots R1 to R14 (plurality of automated machine clusters) that perform welding work (trained task) on a work W that is the body of an automobile. The production line PL is composed of three stations S1 to S3. The stations S1 to S3 are places where the work on the work W is executed, and when all the work on the station S1 is completed, the work W is transported to the station S2 and the work is executed there (one or more policy parameters). When all the work in the station S2 is completed, the work W is transported to the station S3 and the work is executed there (one or more policy parameters). When all the work in the station S3 is completed, all the work in the production line PL is completed, and the work W is transported to another place such as another production line (one or more policy parameters).”) ASATSU also teaches adjust the one or more policy parameters of at least one of the plurality of operation policies based on the determined layout in the environment and the input task ASATSU ([Description | EPU Search Report Reference [0097], [Figure 4] “On the other hand, when it is determined in step S180 that there is no feasible robot arrangement is not the occurrence of inter-robot interference (one of the plurality of operation policies), or in step S160 it is determined that there is no work plan having an evaluation value equal to or greater than a predetermined threshold value (one of the plurality of operation policies). In this case, in step S200, the work distribution changing unit 32 of the arithmetic device 20 changes (adjusts) the plurality of work locations (one or more policy parameters) WP distributed to each of the plurality of robots R1 to R14 (changes work distribution).”) See also ASATSU ([Description | European Search Report [0097] “The figure which shows the work order (one or more policy parameters) of the some work place shown in FIG. 5A, and the movement path (at least one of the plurality of operational policies) of a work tool Diagram showing robot layout (based on the determined layout) Diagram showing interference between robots (in the environment) Figure showing an example of robot motion (input task) correction (adjust). The figure which shows another example of the revision (adjust) of robot movement (input task). Accordingly, claim 1 is rejected based on this reference. Claim 7 Claim 7 is rejected because it is the system embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1. ASATSU anticipates adjust the one or more policy parameters of at least one of the plurality of operation policies based on the determined layout in the environment and the input task ASATSU ([Description | EPU Search Report Reference [0097], [Figure 4].) See claim 1. Claim 22 Claim 22 is rejected because it is the non-transitory computer-readable medium embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1. The ASATSU reference anticipates a non-transitory computer-readable medium comprising one or more instructions which, if executed by a processor, cause the processor to: ASATSU ([Description | European Search Report [0045] , [0045], [Figure 3] “The work plan creation device 10 is, for example, a computer including a processor such as a CPU and a storage device such as a memory that stores a program (work plan creation program). When the processor is driven (executed by a processor) according to the program, the processor causes (causes the processor to) the work distribution calculation unit 22, the robot motion calculation unit 24, the robot layout calculation unit 26, the work plan evaluation unit 28, the robot motion correction unit 30, and the work distribution change unit 32.”) Accordingly, claim 22 is rejected based on this reference. Claim 24 Claim 24 is rejected because it is the system embodiment of claim 1 with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1. ASATSU anticipates a memory configured to store an operation policy and one or more policy parameters for each of the plurality of automated machine clusters ASATSU ([Description | European Search Report Reference [0044], [0045], [Figure 3] “The work plan creation device 10 is, for example, a computer including a processor such as a CPU and a storage device such as a memory that stores a program (work plan creation program).”) Accordingly, claim 24 is rejected based on this reference. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claim(s) 2, is rejected under are rejected under 35 U.S.C. 103 as being unpatentable over ASATSU (WO 2020138485 A1), herein ASATSU, in view of WANG (WO2013120243 A1), herein WANG. Claim 2 Claim 2 is rejected because ASATSU anticipates claim 1. ASATSU does not explicitly teach wherein the processor is further configured to receive information indicating the input task. However, WANG teaches wherein the processor is further configured to receive information indicating the input task WANG ([Description | pdf page 5 of 13] “According to another embodiment of the present invention, the processing module 220 (processor) further includes (configured): a rule management unit 221. The rules management unit 221 receives the automation rules (receives information) from the acquisition module 210 and is used to add or delete the automation rules (indicating the input task).”) WANG also teaches wherein the processor is further configured to select the plurality of operation policies based on the indicated input task WANG ([Description | pdf 5 of 13] “Referring to FIG. 3, in the scheme for implementing automatic capacity adjustment, the rule execution unit 222 includes: a trigger subunit 310 and an invocation subunit 320. The triggering sub-unit 310 (configured to select) selects the automatic processing policy (plurality of operation policies) according to the triggering condition, and compares the real-time feature parameter with the preset threshold of the triggering condition (based on the indicated input task).”) WANG also teaches wherein the processor is configured to train one of the plurality of operation policies based on the input task WANG ([Description | pdf page 11 of 13] “A number of instructions (configured to train) are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps (one of the plurality of operation policies) of the methods described in various embodiments (based on the input task) of the present invention.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of WANG with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. WANG would modify ASATSU wherein the processor is further configured to receive information indicating the input task. The benefits of doing so provide any person skilled in the art to easily think of changes or substitutions within the technical scope of the present invention. (WANG [pdf page 11 of 13]). Claim(s) 3-5, 13-16, 23, and 25 are rejected under are rejected under 35 U.S.C. 103 as being unpatentable over ASATSU in view of KENNEDY (Handbook of Nature-Inspired and Innovative Computing| Integrating Classical Models with Emerging Technologies | Section 3.3 Some Sophisticated Responses), herein KENNEDY. Claim 3 Claim 3 is rejected because ASATSU anticipates claim 1. ASATSU does not explicitly teach wherein the processor is communicatively coupled to a user interface to receive the input task. However, KENNEDY teaches wherein the processor is communicatively coupled to a user interface to receive the input task KENNEDY ([pdf page 25 of 737] “the major strategies for using NOWs in collaborative (communicatively coupled) computations center around three loosely coordinated scheduling mechanisms—workstealing, cyclestealing, and worksharing—that, respectively, from the foci of the following three subsections.”) See also KENNEDY ([Section 2.2 Algorithmic Challenges and Responses | pdf page 20 of 737] “Two related major approaches have been developed for scheduling computations on parallel computing platforms, when the computation’s intertask dependencies are represented by a computation-dag—a directed acyclic graph (user interface), each of whose arcs (x → y) betokens the dependence of task y on task x; sources never appear on the right-hand side of an arc; sinks never appear on the left-hand side. The first such approach is to cluster a computation-dag’s tasks into “blocks” whose tasks are so tightly coupled that one would want to allocate each block (receiving an input task) to a single processor (processor is communicatively coupled) to obviate any communication when executing these tasks.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the processor is further configured to receive information indicating the input task. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). Accordingly, claim 3 is rejected based on the combination of these references. Claim 4 Claim 4 is rejected because ASATSU anticipates claim 1. ASATSU does not explicitly teach wherein the input task further comprises information indicating at least one of a plurality of tasks, input task parameters, a number of automated machines for each automated machine cluster, an automated machine type for each of the automated machine clusters, or the environment. However, KENNEDY teaches wherein the input task further comprises information indicating at least one of a plurality of tasks, input task parameters, a number of automated machines for each automated machine cluster, an automated machine type for each of the automated machine clusters, or the environment KENNEDY ([pdf page 25 of 737] “The message of the preceding analysis becomes clear only when one performs the same exercise (input task) with the system (3.1), which characterizes a “workstealing system” (comprises of information) in which there is no workstealing (indicating an input task parameter). For that system, one finds that pl = λl, indicating that, in the limiting state (environment), tasks (input task) are being completed at rate λ (input task parameter).”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the input task further comprises information indicating at least one of a plurality of tasks, input task parameters, a number of automated machines for each automated machine cluster, an automated machine type for each of the automated machine clusters, or the environment. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). Accordingly, claim 4 is rejected based on the combination of these references. Claim 5 Claim 5 is rejected because ASATSU anticipates claim 1. ASATSU teaches wherein the processor is configured to determine the layout based on a machine learning model ASATSU ([Description | pdf page 5 of 10] “As shown in FIG. 5A, for example, a plurality of work locations WP1 to WP6 are distributed to the robot R1 by the work distribution calculation unit 22. In this case, the robot motion calculation unit 24 calculates the motion of the robot R1 (processor is configured) based on a predetermined condition (determine the layout). For example, the operation of the robot R1 is calculated so that the work time required until the work for all the plurality of work locations WP1 to WP6 in one work W is completed is minimized. See also ASATSU ([Description | pdf page 5 of 10] “When the calculation of the motion (machine learning model) of each of the robots R1 to R14 is completed, in the subsequent step S130, the robot layout calculation unit (processor) 26 of the arithmetic device 20 calculates the robot layout (determine a layout).”) ASATSU does not explicitly teach configured to receive an input and provide an output comprising at least one generated layout, wherein the processor is configured to generate a plurality of layouts based on an input information indicating at least one of dimensions of the environment, features of the plurality of automated machine clusters, constraints between the plurality of automated machine clusters, a target performance metric for the each automated machine cluster safety requirements of the plurality of automated machine clusters, and an image representing the environment. However, KENNEDY teaches configured to receive an input and provide an output comprising at least one generated layout KENNEDY ([Section 2 DEFINITIONS AND FORMALIZATION | pdf page 94 of 737] We also define a specification of a deterministic computation as a description that, when executed (configured to), would transform the given input object space (receive an input) into a desired output object space (provide an output) satisfying the prescribed attributes (at least one generated layout).”) KENNEDY also teaches wherein the processor is configured to generate a plurality of layouts based on an input information indicating at least one of dimensions of the environment KENNEDY ([Section 3.3.1 Cluster Computing via Workstealing | pdf page 27 of 737] The performance/behavior of the algorithm is then analyzed by positing a process for generating the inputs (input information) that trigger state changes (at least one of the dimensions of the environment).”) KENNEDY also teaches features of the plurality of automated machine clusters, constraints between the plurality of automated machine clusters WANG ([Description | pdf page 3 of 13] “For example, by templating (features) a virtual machine cluster (machine clusters), large-scale deployment without human intervention (automated) can be achieved, which greatly speeds up the deployment of virtual machine clusters.”) KENNEDY also teaches a target performance metric for the each automated machine cluster KENNEDY ([Section 8 Measuring and Predicting Other Resource Characteristics | pdf page 609 of 737] “This last example also illustrates the nature of the predictions that application schedulers (automated machine cluster) require. Rather than the mean time to failure(a target performance metric), which is a useful metric in many industrial engineering contexts, the scheduler must estimate how long a resource will be available until the probability of failure exceeds some specified threshold.”) KENNEDY also teaches safety requirements of the plurality of automated machine clusters, and an image representing the environment KENNEDY ([Section 4.4 MAVHome Smart Home] “Another example is the DOE’s Accelerated Strategic Computing Initiative (ASCI), which applies advanced capabilities in scientific and engineering computing to one of the most complex challenges in the nuclear era—maintaining the performance, safety, and reliability.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the processor is configured to generate a plurality of layouts based on an input information indicating at least one of dimensions of the environment. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). Accordingly, claim 5 is rejected based on the combination of these references. Claim 13 Claim 13 is rejected because ASATSU anticipates claim 1. ASATSU teaches wherein the processor is configured to adjust the one or more policy parameters of the plurality of operation policies based on the determined layout in the environment and the input task ASATSU ([Description | pdf page 5 of 10] “If the robot is allowed to change (adjust) over multiple types of work tools (plurality of operation policies) when creating a work plan for a robot, the distribution of work points (one or more policy parameters) for each robot will be calculated on the assumption that the robot will change over multiple types of work tools. You may have. By changing (adjusting) over the work tools (plurality of operation policies) of multiple types, the work capacity of the robot is improved. This increases the choices of the robot when allocating the work place to the robot…As shown in FIG. 5A, for example, a plurality of work locations WP1 to WP6 are distributed to the robot R1 by the work distribution calculation unit 22. In this case, the robot motion calculation unit 24 calculates the motion of the robot R1 (processor is configured) based on a predetermined condition (determine the layout). For example, the operation of the robot R1 is calculated so that the work time required until the work for all the plurality of work locations WP1 to WP6 in one work W is completed is minimized. See also ASATSU ([Description | pdf page 5 of 10] “When the calculation of the motion (machine learning model) of each of the robots R1 to R14 is completed, in the subsequent step S130, the robot layout calculation unit (processor) 26 of the arithmetic device 20 calculates the robot layout (determine a layout).”) ASATSU does not explicitly teach configured to receive an input and provide an output comprising at least one generated layout, wherein the processor is configured to generate a plurality of layouts based on an input information indicating at least one of dimensions of the environment, features of the plurality of automated machine clusters, constraints between the plurality of automated machine clusters, a target performance metric for the each automated machine cluster safety requirements of the plurality of automated machine clusters, and an image representing the environment. However, KENNEDY teaches configured to receive an input and provide an output comprising at least one generated layout We also define a specification of a deterministic computation as a description that, when executed (configured to), would transform the given input object space (receive an input) into a desired output object space (provide an output) satisfying the prescribed attributes (at least one generated layout).”) KENNEDY also teaches wherein the processor is configured to generate a plurality of layouts based on an input information indicating at least one of dimensions of the environment KENNEDY ([Section 3.3.1 Cluster Computing via Workstealing | pdf page 27 of 737] The performance/behavior of the algorithm is then analyzed by positing a process for generating the inputs (input information) that trigger state changes (at least one of the dimensions of the environment).”) KENNEDY also teaches features of the plurality of automated machine clusters, constraints between the plurality of automated machine clusters WANG ([Description | pdf page 3 of 13] “For example, by templating (features) a virtual machine cluster (machine clusters), large-scale deployment without human intervention (automated) can be achieved, which greatly speeds up the deployment of virtual machine clusters.”) KENNEDY also teaches a target performance metric for the each automated machine cluster KENNEDY ([Section 8 Measuring and Predicting Other Resource Characteristics | pdf page 609 of 737] “This last example also illustrates the nature of the predictions that application schedulers (automated machine cluster) require. Rather than the mean time to failure(a target performance metric), which is a useful metric in many industrial engineering contexts, the scheduler must estimate how long a resource will be available until the probability of failure exceeds some specified threshold.”) KENNEDY also teaches safety requirements of the plurality of automated machine clusters, and an image representing the environment KENNEDY ([Section 4.4 MAVHome Smart Home] “Another example is the DOE’s Accelerated Strategic Computing Initiative (ASCI), which applies advanced capabilities in scientific and engineering computing to one of the most complex challenges in the nuclear era—maintaining the performance, safety, and reliability.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the processor is configured to generate a plurality of layouts based on an input information indicating at least one of dimensions of the environment. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). Accordingly, claim 13 is rejected based on the combination of these references. Claim 14 Claim 14 is rejected because ASATSU anticipates claim 1. ASATSU does not explicitly teach wherein the one or more policy parameters comprise information indicating coordinates for the one or more automated machines of the respective automated machine cluster to perform a task. However, KENNEDY teaches wherein the one or more policy parameters comprise information indicating coordinates for the one or more automated machines of the respective automated machine cluster to perform a task KENNEDY ([Section 6.1.3 Protein Explorer | pdf page 549 of 737] “The cluster nodes will transmit the coordinates (comprise information indicating coordinates) and the other data of particles (one or more policy parameters) for the molecular dynamics simulation to the special-purpose engines (automated machines), which then calculate the nonbonded forces such as Coulomb force and van der Walls force between particles (one or more policy parameters) before returning the results to the hosts. In other words, the special- purpose engines (automated machines) only focus on computing the most complex portion (respective automated machine cluster to perform a task) of the simulation, that is, calculating the nonbonded forces. All the coordination and other calculations are handled by the cluster nodes themselves (respective automated machine cluster to perform a task).”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the one or more policy parameters comprise information indicating coordinates for the one or more automated machines of the respective automated machine cluster to perform a task. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). Accordingly, claim 14 is rejected based on the combination of these references. Claim 15 Claim 15 is rejected because ASATSU anticipates claim 1. ASATSU teaches wherein the processor is configured to adjust the one or more policy parameters of each operation policy ASATSU ([Description | pdf page 5 of 10] “If the robot is allowed to change (adjust) over multiple types of work tools (plurality of operation policies) when creating a work plan for a robot, the distribution of work points (one or more policy parameters) for each robot will be calculated on the assumption that the robot will change over multiple types of work tools. You may have. By changing (adjusting) over the work tools (plurality of operation policies) of multiple types, the work capacity of the robot is improved. This increases the choices of the robot when allocating the work place to the robot.”) ASATSU does not explicitly teach by using a machine learning model configured to receive an input comprising the one or more policy parameters of the respective operation policy and provide an output indicating one or more intermediate policy parameters. However, KENNEDY teaches by using a machine learning model configured to receive an input comprising the one or more policy parameters of the respective operation policy and provide an output indicating one or more intermediate policy parameters KENNEDY ([Section 6.1.3 Protein Explorer | pdf page 549 of 737] “We also define a specification (indicating one or more intermediate policy parameters) of a deterministic computation (using a machine learning model) as a description that, when executed (configured to), would transform the given input object space (receive an input) into a desired output object space (provide an output) satisfying the prescribed attributes (one or more policy parameters of the respective operation policy). The main feature of the general rule-based paradigm is the specification (indicating one or more intermediate policy parameters) of the program: G(R, A)(M) = If there exists elements a, b, c, . . . in an object space M such that an interaction rule R (a, b, c, . . . ) involving elements a, b, c is applicable, then G(R, A)((M-{a, b, c,. . }) + A(a, b, c,. . . )); else M. Here M denotes the initial object space. This is a multiset or a bag in which a member can have multiple occurrences [14]. The sign “−” denotes the removal (annihilation) of the interacted elements; it is the multiset difference. The sign “+” denotes the insertion (or creation) of new elements after the action A; this is multiset union. Note that R is a condition text (or an interaction condition that is a Boolean) that determines when some of the elements of the object space Mcan interact. The function A is the action text that describes the result of this interaction.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the one or more policy parameters comprise information indicating coordinates for the one or more automated machines of the respective automated machine cluster to perform a task. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). Accordingly, claim 15 is rejected based on the combination of these references. Claim 16 Claim 16 is rejected because it is system embodiment of claim 5 with similar limitations to claim 5, and is such rejected using the same reasoning found in claim 5. Additionally, KENNEDY teaches machine learning model KENNEDY ([Section 4.4 MavHome Smart Home | pdf page 632 of 737] “In [7], we defined a smart environment as one that is able to acquire and apply knowledge about its inhabitants and their surroundings in order to adapt to the inhabitants and meet the goals of comfort and efficiency. These capabilities rely upon effective prediction and intelligent decision making (modeling) with the help of such technologies as robotics, wireless and sensor networking, mobile computing, databases, machine learning (machine learning) and multimedia technologies.” Accordingly, claim 16 is rejected based on these reference. Claim 23 Claim 23 is rejected because it is the computer readable medium embodiment of claim 15 with similar limitations to claim 15, and is such rejected using the same reasoning found in claim 15. Additionally, ASATSU teaches the claim limitation a non-transitory computer-readable medium comprising one or more instructions which, if executed by a processor, cause the processor to: ASATSU ([Description | European Search Report [0045] , [0045], [Figure 3] “The work plan creation device 10 is, for example, a computer including a processor such as a CPU and a storage device such as a memory that stores a program (work plan creation program). When the processor is driven (executed by a processor) according to the program, the processor causes (causes the processor to) the work distribution calculation unit 22, the robot motion calculation unit 24, the robot layout calculation unit 26, the work plan evaluation unit 28, the robot motion correction unit 30, and the work distribution change unit 32.”) Accordingly, claim 23 is rejected based on this reference. Claim 25 Claim 25 is rejected because ASATSU anticipates claim 24. ASATSU does not explicitly teach wherein the plurality of automated machines are configured to transmit sensor data to the device. However, KENNEDY teaches wherein the plurality of automated machines are configured to transmit sensor data to the device KENNEDY ([Section 2 Interconnection Technologies and Communication Software | pdf page 529 of 737] “Clusters need to incorporate fast interconnection technologies in order to support high-bandwidth and low-latency interprocessor communication between cluster nodes. Slow interconnection technologies had always been a critical performance bottleneck for cluster computing. Today, improved network technologies (plurality of automated machines) help realize the construction of more efficient clusters. Selecting a cluster interconnection network technology depends on several factors, such as compatibility with the cluster hardware and operating system, price, and performance. There are two metrics to measure performance for interconnects: bandwidth and latency (sensor data). Bandwidth is the amount of data that can be transmitted (configured to transmit sensor data) over the interconnect hardware in a fixed period of time, while latency (sensor data) is the time needed to prepare and transmit data (configured to transmit data) from a source node (device) to a destination node (device).”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the plurality of automated machines are configured to transmit sensor data to the device. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). Accordingly, claim 25 is rejected based on the combination of these references. Claim(s) 17 and 20 is rejected under are rejected under 35 U.S.C. 103 as being unpatentable over ASATSU in view of KENNEDY, and in further view of BU (A Reinforcement Learning Approach to Online Web Systems Auto-configuration), herein BU. Claim 17 Claim 17 is rejected because the combination of ASATSU and KENNEDY teaches claim 15. The combination of ASATSU and KENNEDY does not explicitly teach wherein the processor is further configured to train each operation policy by a reinforcement learning model using the one or more intermediate policy parameters to obtain one or more final policy parameters. However, BU teaches wherein the processor is further configured to train each operation policy by a reinforcement learning model using the one or more intermediate policy parameters to obtain one or more final policy parameters BU ([Section 4.1 Policy Initialization] “After getting all the training data, we run (processor is further configured) an offline reinforcement learning process (train each operation policy by a reinforcement learning model) showed in Algorithm 1 to generate an initial Q-value table (obtain one or more final policy parameters) for online learning. In implementation, we set α = 0.1, γ = 0.9, £ = 0.1 (one or more final policy parameters) for the offline training. Algorithm 2 gives the pseudo-code (one or more intermediate policy parameters) of the policy initialization algorithm.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of BU with ASATSU and KENNEDY as the references deal with methods and devices for a collaboration of automated and autonomous machines. BU would modify ASATSU and KENNEDY wherein the processor is further configured to train each operation policy by a reinforcement learning model using the one or more intermediate policy parameters to obtain one or more final policy parameters. The benefits of doing so can drive the RAC system reinforcement learning approach into a near-optimal configuration setting in less than 25 trial-anderror iteration. (BU [Introduction]). Accordingly, claim 17 is rejected based on the combination of these references. Claim 20 Claim 20 is rejected because the combination of ASATSU, KENNEDY, and BU teaches claim 17. ASATSU does not teach wherein the controller is further configured to deploy each automated machine cluster according to the determined layout or wherein a controller configured to provide instructions to deploy the plurality of automated machine clusters according to the one or more final policy parameters for each operation policy. However, KENNEDY teaches wherein the controller is further configured to deploy each automated machine cluster according to the determined layout KENNEDY ([Section 2.4 Adaptive Systems | pdf page 401 of 737] “With sufficient automation (i.e., real-time synthesis provided by PLDs), evolvable hardware (controller) has the potential to adapt autonomously to changes in its environment (according to the determined layout). This ability can be very useful in situations where real-time manual control over systems is not possible, such as on deep space missions. It could be particularly useful when unexpected conditions are encountered (according to the determined layout). Stoica et al. have noted that current lack of validation for online evolutionary systems means that critical spacecraft control systems, and other mission-critical systems, cannot currently be placed under evolutionary control [104]. Greenwood and Song have proposed using evolutionary techniques (determined layouts) in conjunction with formal verification techniques (determined layouts) to circumvent this problem [22]; however, to date only noncritical systems such as sensor processing systems (automated machine cluster) have been explored, for example, adaptive data compression systems (automated machine cluster) [15]. Other systems that could benefit from the ability to autonomously evolve (configured to deploy) are power management systems (automated machine cluster) and controller deployment mechanisms (automated machine cluster) for booms, antennae, etc. () [91].”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the controller is further configured to deploy each automated machine cluster according to the determined layout. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). The combination of ASATSU and KENNEDY does not explicitly teach a controller configured to provide instructions to deploy the plurality of automated machine clusters according to the one or more final policy parameters for each operation policy. However, BU teaches a controller configured to provide instructions to deploy the plurality of automated machine clusters according to the one or more final policy parameters for each operation policy BU ([Section 3.1 Parameter Selection and Auto-Configuration] “The decision maker runs a RL algorithm and produces a state-action table, called Q-value table. A state is defined as a configuration (providing instructions) of the selected parameters (one or more final policy parameters). Possible actions include increasing, decreasing their values or keeping unchanged (each operation policy); see the next section for details. Based on the dynamically updated Q table, the configuration controller generates (controller configured to provide instructions) the configuration policy and reconfigures the whole system (deploys the plurality of automated machine clusters) if necessary.”) BU also teaches wherein the controller is further configured to control each automated machine cluster according to the one or more final policy parameters for the respective operation policy and the respective operation policy BU ([Section 3.1 Parameter Selection and Auto-Configuration] “The agent consists of three key components: performance monitor, decision maker, and configuration controller. The performance monitor passively measures the web system performance at a predefined time interval (we set it to 5 minutes in experiments), and sends the information to RL-based decision maker (respective operation policy). The only information the decision maker needs is the application level performance such as response time or throughput (respective operation policy). It requires no OS-level or hardware level information for portability. The decision maker runs a RL algorithm and produces a state-action table, called Q-value table. A state is defined as a configuration of the selected parameters (one or more final policy parameters). Possible actions include increasing, decreasing their values or keeping unchanged; see the next section for details. Based on the dynamically updated Q table, the configuration controller generates the configuration policy (according to the one or more final policy parameters) and reconfigures the whole system (controller is further configured to control each automated machine cluster) if necessary. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of BU with ASATSU and KENNEDY as the references deal with methods and devices for a collaboration of automated and autonomous machines. BU would modify ASATSU and KENNEDY wherein the processor is further configured to train each operation policy by a reinforcement learning model using the one or more intermediate policy parameters to obtain one or more final policy parameters. The benefits of doing so can drive the RAC system reinforcement learning approach into a near-optimal configuration setting in less than 25 trial-anderror iteration. (BU [Introduction]). Accordingly, claim 20 is rejected based on the combination of these references. Claim(s) 18 is rejected under are rejected under 35 U.S.C. 103 as being unpatentable over ASATSU in view of KENNEDY, in view of BU, in further view of PARK (Centroid Neural Network for Unsupervised Competitive Learning), herein PARK, in further view of RAHMAN (Scheduling Automated Transport Vehicles for Material Distribution), herein RAHMAN, and in further view of VISHWANADHAM (Performance Modeling of Automated Systems), herein VISHWANADHAM. Claim 18 Claim 18 is rejected because the combination of ASATSU, KENNEDY, and BU teaches claim 17. The ASATSU does not explicitly teach wherein the processor is configured to determine a state based on the determined layout and the one or more intermediate policy parameters to train each operation policy. However, KENNEDY teaches wherein the processor is configured to determine a state based on the determined layout and the one or more intermediate policy parameters to train each operation policy KENNEDY ([Section 2.2 Algorithmic Challenges and Response | Network Emulations | pdf pages 23-24] “We mention one final, unique use of embedding-based emulations. In [115], a suite of embedding-based algorithms is developed in order to (processor configured to) endow (determine) a multiprocessor with a capability (a state) that would be prohibitively expensive to supply in hardware (based on the determined layout). The gauge (at least one location) of a multiprocessor (one or more automated machines) is the common width of its CPU and memory bus (plurality of automated machine clusters). A multiprocessor can be multigauged (one or more intermediate policy parameters) if, under program control, it can dynamically change its (apparent) gauge (to train each operation policy). (Prior studies had determined the algorithmic value (determining a state) of multigauging, as well as its prohibitive expense (state information) [53, 143].) Using an embedding-based approach that is detailed in [114], the algorithms of [115] efficiently endow a multiprocessor architecture with a multigauging capability.”) KENNEDY also teaches a beginning and a final positions for each automated machines of the plurality of automated machine clusters KENNEDY ([Section 3.1 K-Means Clustering | pdf page 242 of 737] “The K-means algorithm partitions a group of n vectors xj: j = 1 (beginning position),...,n (final position) into c groups Gi:i = 1,...,c, and finds a cluster center in each group (plurality of automated machine clusters) such that a cost function of dissimilarity measure is minimized [19,20]. To achieve this outcome, let’s assume that PNG media_image1.png 149 791 media_image1.png Greyscale The partitioned groups are typically defined by a c × n binary membership matrix U, where the elements uij are 1 if the jth data point xj belongs to group i and 0 otherwise.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein a beginning and a final positions for each automated machines of the plurality of automated machine clusters. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). The combination of ASATSU, KENNEDY, and BU do not explicitly teach vector positions of the each automated machines of the plurality of automated machine clusters. However, PARK teaches vector positions of the each automated machines of the plurality of automated machine clusters PARK ([Section V. The CNN Algorithm] “The CNN algorithm is based on the conventional-means algorithm and finds the centroid of data (vector positions) in corresponding clusters (automated machine clusters) at each presentation of data vectors (each of the automated machines). Instead of calculating the centroids of the clustered data (plurality of automated machine clusters) for every presentation of data, the CNN algorithm updates their weights only when the status of the output neuron for presenting data has changed.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of PARK with ASATSU, KENNEDY, and BU as the references deal with methods and devices for a collaboration of automated and autonomous machines. PARK would modify ASATSU, KENNEDY, and BU wherein the generated plurality of layouts comprises information indicating at least one of a location, a centroid, or a pose for the each one of the plurality of automated machine clusters. The benefits of doing so allows the centroid, or conditional expectation, to minimize the mean-squared error of the vector quantization where the CNN finds locally optimal synaptic vectors for each datum presented and consequently converges to the centroids of clusters much faster than conventional algorithms. (PARK [Abstract]). The combination of ASATSU, KENNEDY, BU, and PARK do not explicitly teach a status of each automated machine clusters indicating whether the respective automated machine cluster is occupied or available. However, RAHMAN teaches a status of each automated machine clusters indicating whether the respective automated machine cluster is occupied or available RAHMAN ([Section 3. Problem formulation and mathematical model | 3.1 Problem Formation | pdf page 6 of 17] “Constraint 6: The constraints for blocking the warehouse or machine for loading and unloading respectively: assume that, at a given time, ptth point is either a material pickup or delivery point. Let us also assume that from the time tlsi to the time tlf i, the ptth waypoint is occupied by the ith ATV. Therefore, at a time interval [tlsi, tlf i], the ptth point is occupied by only one ATV.”) PNG media_image2.png 207 526 media_image2.png Greyscale It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of RAHMAN with ASATSU, KENNEDY, BU, and PARK as the references deal with methods and devices for a collaboration of automated and autonomous machines. RAHMAN would modify ASATSU, KENNEDY, BU, and PARK wherein the processor is configured to select the one of the plurality of generated layouts based on at least one of estimated routes for the one or more automated machines, estimated routes for the one or more automated machines estimated to transport a material, or work sequences of automated machines. The benefits of doing so allows ATV-based material distribution system to transfer the right amount of the right material to the right place at the right time. (RAHMAN [Introduction]). The combination of ASATSU, KENNEDY, BU, PARK and RAHMAN do not explicitly teach a status of each automated machines for the plurality of automated machine clusters indicating whether the respective automated machine is loaded or unloaded. However, VISHWANADHAM teaches a status of each automated machines for the plurality of automated machine clusters indicating whether the respective automated machine is loaded or unloaded VISHWANADHAM ([Section 2.5 | page 85 | ¶ 1] “An AGV system is a computer controlled material handling system (plurality of automated machine clusters) comprising several microprocessor-controlled driverless vehicles (each automated machines) each of which can automatically perform loading, route selection, and unloading (indicating whether the respective automated machine is loading or unloading).”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of VISHWANADHAM with ASATSU, KENNEDY, BU, PARK, and RAHMAN as the references deal with methods and devices for a collaboration of automated and autonomous machines. VISHWANADHAM would modify ASATSU, KENNEDY, BU, PARK, and RAHMAN vector positions of the each automated machines of the plurality of automated machine clusters. The benefits of doing so allows reduction in processing time, inventory and tooling where the overall manducating lead time and handling times are also reduced. (VISHWANADHAM [page 99]). Accordingly, claim 18 is rejected based on the combination of these references. Claim(s) 6 is rejected under are rejected under 35 U.S.C. 103 as being unpatentable over ASATSU in view of KENNEDY, and in further view of CHAE (US 20210200239 A1), herein CHAE. Claim 6 Claim 6 is rejected because the combination of ASATSU and KENNEDY teaches claim 5. ASATSU does not explicitly teach wherein the generative neural network comprises a generative adversarial network model. However, KENNEDY teaches wherein the generative neural network comprises a generative adversarial network model KENNEDY ([pdf page 31 of 737] “cycle-stealing is viewed as a game against a malicious adversary who seeks to interrupt the borrowed workstation in order to kill all work in progress and thereby minimize the amount of work produced during a cycle-stealing opportunity. (In these studies, cycles are stolen from one workstation at a time, so the enterprise is unaffected by the presence or absence of heterogeneity.) Clearly, cycle-stealing within the described adversarial model can accomplish productive work only if the metaphorical “malicious adversary” is somehow restrained from just interrupting every period when the cycle-donor is doing work for the cycle-stealer, thereby killing all work done by the donor.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the generative neural network comprises a generative adversarial network model. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). The combination of ASATSU and KENNEDY do not explicitly teach wherein the processor is configured to generate the plurality of layouts using a generative neural network configured to provide a plurality of generated layouts based on the input information. However, CHAE teaches teach wherein the processor is configured to generate the plurality of layouts using a generative neural network configured to provide a plurality of generated layouts based on the input information CHAE ([0199] “The learning processor 240 is configured to play the same role as the AI processor 131 and is so denoted to differentiate between the processor included in the AI server 200 and the processor included in the AI device 100. Thus, the learning processor 240 may be configured to have the same functions and effects as those of the AI processor (generating the plurality of layouts) 131. Hence, the learning processor 240 may train the ANN 231a (provide a plurality of generated layouts) using learning data (based on input information). The learning model may be equipped and used in the AI server 200 of the artificial neural network (using generative neural network) or, as described above, may be equipped and used, in the form of the AI processor 131 equipped in the AI device 100, in an external device.”) See also CHAE ([0211] The AI server 200 may train an ANN according to a machine learning algorithm, on behalf of each AI device 100a to l00e and may directly store a learning model or transfer the learning model to the AI devices 100a to 100e.”) See also CHAE ([0218] The robot 100a may obtain status information about the robot 100a using sensor information (based on input information) obtained from various kinds of sensors, detect (recognize) the ambient environment and objects, generate map data (generate a plurality of layouts), determine a driving route and plan, determine a response to the user's interaction, or determine operations.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of CHAE with ASATSU and KENNEDY as the references deal with methods and devices for a collaboration of automated and autonomous machines. CHAE would modify ASATSU and KENNEDY wherein the processor is configured to generate the plurality of layouts using a generative neural network configured to provide a plurality of generated layouts based on the input information. The benefits of doing so may machine-learn the deployment formation of the robot (S170) and generate a formation for more efficiently deploying the robot later. (CHAE [0478]). Accordingly, claim 6 is rejected based on the combination of these references. Claim(s) 7-11 are rejected under are rejected under 35 U.S.C. 103 as being unpatentable over ASATSU in view of KENNEDY, in view of CHAE, and in view of PARK. Claim 7 Claim 7 is rejected because the combination of ASATSU, KENNEDY and CHAE teaches claim 6. The combination of ASATSU, KENNEDY, CHAE do not explicitly teach wherein the generated plurality of layouts comprises information indicating at least one of a location, a centroid, or a pose for the each one of the plurality of automated machine clusters. However, PARK teaches wherein the generated plurality of layouts comprises information indicating at least one of a location, a centroid, or a pose for the each one of the plurality of automated machine clusters PARK ([Abstract] “The proposed learning algorithm called the centroid neural network (CNN) estimates centroids of the related cluster groups in training date.”) See also PARK ([Conclusion] “The CNN algorithm based on the -means clustering algorithm is proposed. The proposed CNN algorithm has a strong connection with some of the conventional unsupervised learning algorithms. In order to obtain lower energy, the CNN dynamically allocates the synaptic weights to the clusters with high energy. Even though the CNN algorithm is not new when we consider the -means algorithm, the CNN provides us with a new interpretation of the -means algorithm and the relationships with some of the conventional algorithms. While applying the CNN algorithm to several problems, the CNN successfully converges to suboptimal solutions. Any undesirable local minimum problem (information indicating at least one of a location) was not observed in our CNN experiments performed with many different data sets (plurality of generated layouts). The CNN algorithm is applied to several problems such as simple 2-D data problems and image compression problems. When compared with conventional clustering algorithms such as Kohonen’s self-organizing map and Kosko’s differential competitive learning on these problems, the proposed CNN algorithm produces comparable results with less computational effort and is free of the optimum parameter selection problem.”) See also PARK ([Section VII Experiments and Results] “Initial weights (plurality of automated machine clusters) are clustered around the center (centroid) of the plot. At each epoch of the training stage, the presentation order of the input data was randomized for both algorithms in order to generalize the results. With both algorithms, weights gradually spread out until the weight distribution approximates the uniform distribution. Fig. 5 shows the final location of weights for SOM and CNN. The results shown in Fig. 5 are quite similar with minor differences in final energy levels.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of PARK with ASATSU, KENNEDY, and CHAE as the references deal with methods and devices for a collaboration of automated and autonomous machines. PARK would modify ASATSU, KENNEDY, and CHAE wherein the generated plurality of layouts comprises information indicating at least one of a location, a centroid, or a pose for the each one of the plurality of automated machine clusters. The benefits of doing so allows the centroid, or conditional expectation, to minimize the mean-squared error of the vector quantization where the CNN finds locally optimal synaptic vectors for each datum presented and consequently converges to the centroids of clusters much faster than conventional algorithms. (PARK [Abstract]). Accordingly, claim 7 is rejected based on the combination of these references. Claim 8 Claim 8 is rejected because the combination of ASATSU, KENNEDY, CHAE, and PARK teaches claim 7. The combination of ASATSU and KENNEDY do not explicitly teach wherein the processor is configured to estimate the interactions using a graphical neural network configured to provide an output. However CHAE teaches wherein the processor is configured to estimate the interactions using a graphical neural network configured to provide an output CHAE ([0182],[0183] Further, the data learning unit 132 may further include a model estimator (processor configured to estimate) (not shown) to improve the analysis result (the interactions) of a neural network model (graphical neural network). The model estimator (processor configured to estimate) inputs estimation data (interactions) to a neural network model (using a graphical neural network), and when an analysis result output (configured to provide an output) from the estimation data does not satisfy a predetermined reference, it can make the model learning unit 134 perform learning again.”) CHAE also teaches comprising an adjacency matrix indicating interactions between the plurality of automated machine clusters CHAE ([0048] “FIG. 22 is a layout view illustrating robots deployed in a predetermined area at a second time.”) See also CHAE ([0430] “Referring to FIG. 22, the AI server 200 redeploys the robots R1, R2, R3, R4, RS, R6, R7, RS, and R9 (plurality of automated machine clusters) considering both the two factors (the distance between the peak point and each robot and the current workload of each robot) (interactions between plurality of automated machine clusters). In other words, the first peak point A2 609 at the second time t2 is positioned two unit quarters (adjacency matrix) to the right from the first peak point Al 607 at the first time t1.”) See also CHAE ([Figure 22].) PNG media_image3.png 716 646 media_image3.png Greyscale CHAE Figure 22 Matrix Reference It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of CHAE with ASATSU and KENNEDY as the references deal with methods and devices for a collaboration of automated and autonomous machines. CHAE would modify ASATSU and KENNEDY comprising an adjacency matrix indicating interactions between the plurality of automated machine clusters. The benefits of doing so may machine-learn the deployment formation of the robot (S170) and generate a formation for more efficiently deploying the robot later. (CHAE [0478]). Accordingly, claim 8 is rejected based on the combination of these references. Claim 9 Claim 9 is rejected because the combination of ASATSU, KENNEDY, CHAE and PARK teaches claim 8. The ASATSU does not explicitly teach wherein the processor is configured to estimate a performance index for each one of the plurality of generated layouts based on a performance function comprising parameters with respect to the estimated interactions. However KENNEDY teaches wherein the processor is configured to estimate a performance index for each one of the plurality of generated layouts based on a performance function comprising parameters with respect to the estimated interactions KENNEDY ([Section 3.3.2 Cluster computing via cycle-stealing | [pdf page 33 of 737 “The productive schedule (configured to estimate a performance index) S= t0, t1,… tm-1 = 0 is optimal (for each one of the plurality of generated layouts) for the differentiable life function Q (based on a performance function) if, and only if, for each period-index k ≥ 0 (performance metric), save the last, period-length tk (parameters with respect to the estimated interactions) is given by4 P(Tk ) = max (0, P(Tk - 1) + (tk-1 - c) P’ (Tk-1 - 1)).”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the processor is configured to estimate a performance index for each one of the plurality of generated layouts based on a performance function comprising parameters with respect to the estimated interactions. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). Accordingly, claim 9 is rejected based on the combination of these references. Claim 10 Claim 10 is rejected because the combination of ASATSU, KENNEDY, CHAE and PARK teaches claim 9. ASATSU teaches path lengths for the one or more automated machines configured to transport a material for each one of the plurality of automated machine clusters ASATSU ([Description | pdf page 4 of 10] “The production line PL shown as an example in FIG. 1 includes a plurality of robots R1 to R14 (one or more automated machines) that perform welding work on a work W that is the body of an automobile. The production line PL is composed of three stations S1 to S3. The stations S1 to S3 (plurality of machine clusters) are places where the work on the work W is executed, and when all the work on the station S1 is completed, the work W is transported (configured to transport a material) to the station S2 and the work is executed there. When all the work in the station S2 is completed, the work W is transported (configured to transport a material) to the station S3 and the work is executed there. When all the work in the station S3 is completed, all the work in the production line PL is completed, and the work W is transported (configured to transport a material) to another place such as another production line.”) ASATSU also teaches a sequence for each one of the plurality of automated machine clusters, a weight of an object, a speed of a conveyor belt transporting the object, robotic manipulation parameters, a period of time defining a duration of a partial task, an order of multiple partial tasks, or deadlines for the partial tasks ASATSU ([Abstract] “This robot operation plan creation method, which involves creating an operation plan for allocating a plurality of robots (plurality of automated machines) that comprise operation tools in at least one station and executing an operation on workpieces (weight of an object) at a plurality of operation locations (plurality of automated machine clusters), includes: an operation distribution calculation step for calculating a distribution (a speed transporting the object) of operation locations for each of the robots; a robot movement calculation step for calculating, on the basis of the operation distribution (an order of multiple partial tasks, or deadlines for the partial tasks) calculated in the operation distribution calculation step, an operation location operation sequence and an operation tool motion path that define robot movement for each of the robots (sequence for each one of the plurality of automated machine clusters); and a robot arrangement calculation step for calculating an arrangement position and an arrangement station (robotic manipulation parameters) with respect to the workpieces for each of the robots so that inter-robot interference (plurality of automated machine clusters) does not occur during execution of the robot movements calculated in the robot movement calculation step.”) See also ASATSU ([Description | pdf page 2 of 10] “A method is known for creating a work plan in which a plurality of robots are divided among a plurality of stations included in a production line to execute work on a plurality of work points (plurality of automated machine clusters) of a work. For example, in Patent Document 1, a work plan is created for executing, in a short time, the determination of a work location shared by each of a plurality of robots (plurality of automated machines) and the determination of an operation for performing work (a period of time defining a duration of a partial task) on the work location of each robot.”) ASATSU does not explicitly teach wherein the performance function comprises one or more parameters comprising an indication of at least one of inter-cluster distances between each one of the plurality of automated machine clusters. However, KENNEDY teaches wherein the performance function comprises one or more parameters comprising an indication of at least one of inter-cluster distances between each one of the plurality of automated machine clusters KENNEDY ([Section 7 ROBOT PATH PLANNING | pdf page 189 of 737] “Another complex scenario in which neural networks have been used with some promise is that of robot path planning. In this case, the robot tries to navigate its way to reach a target location. The situation can be made more complicated by adding obstacles in the environment or even other mobile robots (plurality of automated machine clusters). Normally, this situation is modeled as an optimization problem (an indication) in which some cost function (performance function) is minimized (e.g., minimize the distance (one or more parameters) that the robot needs to travel (inter-cluster distances between each plurality of automated machine clusters)) while satisfying certain constraints (e.g., no collisions) [64–66] (see Figures 5.30 and 5.31).”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU an indication of at least one of inter-cluster distances between each one of the plurality of automated machine clusters. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). Accordingly, claim 10 is rejected based on the combination of these references. Claim 11 Claim 11 is rejected because the combination of ASATSU, KENNEDY, CHAE and PARK teaches claim 8. ASATSU teaches wherein the processor is configured to select one of the plurality of generated layouts as the determined layout ASATSU ([Description | pdf page 2 of 10] “Therefore, the present invention includes not only work distribution to a plurality of robots and operation of each robot, but also selection of a placement position (configured to select) of each robot with respect to a work and a station (one of the plurality of generated layouts) where each robot is placed (as the determined layout), which saves space (the number of stations is small). The task is to create a robot work plan in a short time according to the demand such as a short cycle time.”) The ASATSU does not explicitly teach wherein the processor is configured to determine a performance score for each one of the plurality of generated layouts using a machine learning model. However, KENNEDY teaches wherein the processor is configured to determine a performance score for each one of the plurality of generated layouts using a machine learning model KENNEDY ([Section 1 Background | pdf page 59 of 737] “There are many data mining tasks and algorithms. These are often classified into four components [11]: ● Models (pattern structures): these model the underlying structures in a database. ● Score functions: the role is to decide how well the developed model (machine learning model) fits with the data. ● Optimization and search methods: these relate to the optimization of the score function (configured to determine a performance score) and searching over many models and structures (for each one of the plurality of generated layouts using a machine learning model). ● Data management strategies: These deal with efficient access and use of data during the search/optimization.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of KENNEDY with ASATSU as the references deal with methods and devices for a collaboration of automated and autonomous machines. KENNEDY would modify ASATSU wherein the processor is configured to estimate a performance index for each one of the plurality of generated layouts based on a performance function comprising parameters with respect to the estimated interactions. The benefits of doing so allows one to obtain almost all of the benefits of message-passing parallel computation while ignoring all aspects of the underlying interconnection network’s structure, save its end-to-end latency. (KENNEDY [Section Algorithmic Challenges and Responses | pdf page 24 of 737]). Accordingly, claim 11 is rejected based on the combination of these references. Claim(s) 12 is rejected under are rejected under 35 U.S.C. 103 as being unpatentable over ASATSU in view of KENNEDY, in view of CHAE, in view of PARK, and in view of RAHMAN. Claim 12 Claim 12 is rejected because the combination of ASATSU, KENNEDY, CHAE and PARK teaches claim 8. ASATSU teaches wherein the processor is configured to select the one of the plurality of generated layouts ASATSU ([Description | pdf page 2 of 10] “Therefore, the present invention includes not only work distribution to a plurality of robots and operation of each robot, but also selection of a placement position (configured to select) of each robot with respect to a work and a station (one of the plurality of generated layouts) where each robot is placed (as the determined layout), which saves space (the number of stations is small). The task is to create a robot work plan in a short time according to the demand such as a short cycle time.”) The combination of ASATSU, KENNEDY, CHAE, and PARK do not explicitly teach based on at least one of estimated routes for the one or more automated machines, estimated routes for the one or more automated machines estimated to transport a material, or work sequences of automated machines. However, RAHMAN teaches based on at least one of estimated routes for the one or more automated machines, estimated routes for the one or more automated machines estimated to transport a material, or work sequences of automated machines KENNEDY ([Section 3.1 Problem Formulation] “From Fig. 1, it can be seen that some of the ATVs (one or more of the automated machines) are carrying materials (estimated to transport a material) for delivery to the delivery points (estimated routes) while some ATVs (automated machines) are empty, returning to the warehouse to collect material for the next task (work sequences of automated machines). The guided path that an ATV (automated machines) follows in one round trip (estimated route), from the warehouse to the delivery points and back (estimated route), is referred to as a route. In order to allow maximum route flexibility of ATV systems, an ATV (automated machines) can take a more direct path by crossing the middle paths of the production floor as shown in Fig. 1. ATVs (automated machines) can travel in both directions on a route (i.e. A to B and B to A), without any collision (estimated routes to transport a material or work sequence of automated machines).”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of RAHMAN with ASATSU, KENNEDY, CHAE, and PARK as the references deal with methods and devices for a collaboration of automated and autonomous machines. RAHMAN would modify ASATSU, KENNEDY, CHAE, and PARK wherein the processor is configured to select the one of the plurality of generated layouts based on at least one of estimated routes for the one or more automated machines, estimated routes for the one or more automated machines estimated to transport a material, or work sequences of automated machines. The benefits of doing so allows ATV-based material distribution system to transfer the right amount of the right material to the right place at the right time. (RAHMAN [Introduction]). Accordingly, claim 12 is rejected based on the combination of these references. Claim(s) 19 and 21 is rejected under are rejected under 35 U.S.C. 103 as being unpatentable over ASATSU in view of KENNEDY, and in further view of WZOREK (GSM Technology as a Communication Media for an Autonomous Unmanned Aerial Vehicle), herein WZOREK. Claim 19 Claim 19 is rejected because ASATSU anticipates claim 1. The combination of ASATSU and KENNEDY do not explicitly teach wherein the device is an edge computing device or an edge computing node. However, WZOREK teaches wherein the device is an edge computing device or an edge computing node WZOREK ([Section 2.3 EDGE] “Enhanced Data rates for Global Evolution (EDGE) is the successor of GPRS. EDGE uses Linear 8-Phase Shift Key (8-PSK) modulation which is more bandwidth efficient than the GMSK modulation used in the GPRS standard. The two phase positions in GPRS, have been replaced by eight positions in EDGE. This means that instead of sending one bit per each symbol, it is possible to send three bits per each symbol increasing the data rate by a magnitude of three.”) See also WZOREK ([Abstract] “Data links, such as wireless Ethernet or radio modems (edge computing device or edge computing node) that use open frequency bands are often unreliable in urban areas due to interference from other users, spreading and reflections from terrain and buildings, etc. GSM and its related technologies GPRS, EDGE (edge computing device or edge computing node), 3GSM offer an interesting communications infrastructure for remotely accessing, controlling and interacting with UAVs (automated or autonomous machines) in an integrated and highly portable manner and offer the ability to interface to the WWW for additional information useful in mission achievement.”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of WZOREK with ASATSU and KENNEDY as the references deal with methods and devices for a collaboration of automated and autonomous machines. WZOREK would modify ASATSU and KENNEDY wherein the device is an edge computing device or an edge computing node. The benefits of doing so permits the operation of UAVs at large distances, out-of-sight from the ground operator, provides a good redundant system alternative in the case where other communication frequencies jam, and provides a means of indoor communication when using micro air vehicles inside building structures. (WZOREK [Conclusion]). Accordingly, claim 19 is rejected based on the combination of these references. Claim 21 Claim 21 is rejected because ASATSU anticipates claim 1. The combination of ASATSU and KENNEDY do not explicitly teach wherein the processor is communicatively coupled to a plurality of sensors to receive sensor data. However, WZOREK teaches teach wherein the processor is communicatively coupled to a plurality of sensors to receive sensor data ([Section 3.1 Hardware Platform | pdf page 7 of 15] “The WITAS 3 UAV platform (5) is a slightly modified Yamaha RMAX helicopter (Fig. 2). It has a total length of 3.6 m (including main rotor) and is powered by a 21 hp two-stroke engine with a maximum takeoff weight of 95 kg. The helicopter has a built-in attitude sensor (YAS) and an attitude control system (YACS). The hardware platform developed during the WITAS UAV project is integrated with the Yamaha platform as shown in Fig. 3. It contains three PC104 embedded computers (processor). The primary flight control (PFC) system (processor) runs on a PIii (700Mhz), and includes a wireless Ethernet bridge (communicatively coupled), a GPS receiver (receive sensor data), and several additional sensors (plurality of sensors) including a barometric altitude sensor. The PFC is connected to the YAS and YACS, an image processing computer and a computer for deliberative capabilities.”) See also WZOREK ([Figure 3].) PNG media_image4.png 446 451 media_image4.png Greyscale WZOREK Figure 3 Reference WZOREK also teaches wherein the processor is configured to obtain environment data representing the environment based on the received sensor data WZOREK ([Section 3.1 Hardware Platform | pdf page 7 of 15] “The software implementation is based on CORBA (Common Object Request Broker Architecture), which is often used as middleware for object-based distributed systems. It enables different objects or components to communicate with each other regardless of the programming languages in which they are written, their location on different processors or the operating systems (processor) they running on. A component can act as a client, a server or as both. The functional interfaces to components are specified via the use of IDL (Interface Definition Language). The majority of the functionalities which are part of the architecture can be viewed as CORBA objects or collections of objects, where the communication infrastructure is provided by CORBA facilities and other services such as real-time (representing the environment data) and standard event channels (representing environment data). This architectural choice provides us with an ideal development environment (obtain environment data) and versatile run-time system with built-in scalability, modularity, software relocatability on various hardware configurations (received sensor data), performance (real-time event channels and schedulers) (environment data), and support for plug-and-play software modules.”) See also WZOREK ([Figure 4].) PNG media_image5.png 270 553 media_image5.png Greyscale WZOREK Figure 4 Reference WZOREK also teaches wherein the processor is configured to provide instructions to the one or more automated machines based on the environment data WZOREK ([Section 1.1 Paper Outline] “In section 1, we described the concept of pushbutton missions. In this case, user interfaces designed to help control UAVs (one or more automated machines) generally do not require high bandwidth uplinks since only high-level goals or action commands (instructions) are sent to the UAV (automated machine) rather than large sequences of control signals to control the platform itself. The types of commands (provide instructions to the one or more automated machines) uplinked are sporadic and of the type, fly-to-point, take-off, hover-at-point, land, etc., while mission goals might be of the type, fly-to building and monitor the front of the building. In these cases, there is very little interaction between the ground operator and UAV, and consequently weaker constraints on latency bit rate and data package loss rate (based on the environment data).”) It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of WZOREK with ASATSU and KENNEDY as the references deal with methods and devices for a collaboration of automated and autonomous machines. WZOREK would modify ASATSU and KENNEDY wherein the processor is communicatively coupled to a plurality of sensors to receive sensor data. The benefits of doing so permits the operation of UAVs at large distances, out-of-sight from the ground operator, provides a good redundant system alternative in the case where other communication frequencies jam, and provides a means of indoor communication when using micro air vehicles inside building structures. (WZOREK [Conclusion]). Accordingly, claim 21 is rejected based on the combination of these references. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARTIN K VU whose telephone number is (703)756-5944. The examiner can normally be reached 7:30 am to 4:30 pm M-F. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached on 571-272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /M.K.V./Examiner, Art Unit 2188 /RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188