Patent Application 17461351 - ARTIFICIAL INTELLIGENCE SYSTEM FOR CONTROL TOWER

Title: ARTIFICIAL INTELLIGENCE SYSTEM FOR CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM MANAGING LOGISTICS SYSTEM

Application Information

Invention Title: ARTIFICIAL INTELLIGENCE SYSTEM FOR CONTROL TOWER AND ENTERPRISE MANAGEMENT PLATFORM MANAGING LOGISTICS SYSTEM
Application Number: 17461351
Submission Date: 2025-05-12T00:00:00.000Z
Effective Filing Date: 2021-08-30T00:00:00.000Z
Filing Date: 2021-08-30T00:00:00.000Z
National Class: 705
National Sub-Class: 330000
Examiner Employee Number: 76190
Art Unit: 3628
Tech Center: 3600

Rejection Summary

102 Rejections: 1
103 Rejections: 0

Cited Patents

No patents were cited in this rejection.

Office Action Text

DETAILED ACTION
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 .
Status of Claims
Due to communications filed 2/6/25, the following is a non-final office action. Claims 1-20 are pending in this application and are rejected as follows. The previous rejection has been modified to reflect claim amendments. Prosecution has been re-opened.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.

The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.

Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Due to a 112(f) invocation caused by the “machine learning system”, “artificial intelligence system”, and “digital twin system”, Examiner suggests entering an amendment to correct the following limitations: “a machine learning system” needs to be amended to recite “a machine learning system, comprising of one or more processors; “an artificial intelligence system” needs to be amended to recite “an artificial intelligence system, comprising of the one or more processors,”; and “a digital twin system” ” needs to be amended to recite “a digital twin system, comprising of the one or more processors,”. This amendment will remove the 112(f) invocation

The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.

The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.

Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01.
In this case, the disclosure of "a machine learning system", "an artificial intelligence system", and "a digital twin system" invokes a 112(f).
As per independent claim 1, it recites, "a machine learning system that trains a machine-learned model..." However, no corresponding structure/algorithm was disclosed by the specification on how the training is performed. Mere reference to a general purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting "software" without providing detail about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239; Finisar, 523 F.3d at 1340-41, 86 USPQ2d at 1623.
Dependent claims 2-20 inherit the deficiency noted for claim 1.

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) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wolfgang’s “Digital Twins for decision making in Complex Production and Logistic Enterprises”.

As per claim 1, Wolfgang discloses:

a machine learning system that trains a machine-learned model that outputs a logistics design recommendation given a respective set of input features relating to a specific respective logistics system, wherein the machine learning system trains the machine-learned model based on training data sets that define features of logistics systems and outcomes of the logistics systems, (Wolfgang: Pg. 263-264, “In production and logistic enterprises Digital Twins will enable to improve operations, asset management, operational efficiency, maintenance repair and operation and insights into how products and processes can be improved.”; Pg. 265, “Aggregate – The real-time data have to be send into a data repository, processed and prepared for the analytics.”; Pg. 266, “Machine Learning anomaly detectors – Machine learning algorithms can be used, ranging from multivariate multi-level survival models to baseline asset risk, to classification techniques like logistic regression, decision trees, random forest methods, neural networks and clustering methodologies. These models are usually derived using healthy and fault data based on a database of historical sensor and configuration data.”; Pg. 268, “4.4 Machine learning is based on data, either from structured data and increasingly also from unstructured data without explicitly being programmed. Based on data and algorithm it is referred to as supervised learning, where the algorithm is trained using examples where the input data and the correct answers are known and in unsupervised learning, where the algorithm must discover patterns in the data on its own, and rein[1]forced learning, where the algorithm is rewarded or penalized for the actions it takes based on trial and error.“

an artificial intelligence system that receives a request for logistics system design and determines a logistics system design recommendation based on the machine- learned model and the request, (Wolfgang: Pg. 264, “With Artificial Intelligence based capabilities Digital Twins offer for specific questions advanced product, production or logistic simulation models, which allows to test drive several alternatives in a virtual environment before decisions are applied to the real-world system.”; Pg. 265, “Insight – Based on the analysed data, models for decision making are created. Significant differences in the performance of the Digital Twin model and the physical world indicate potentials for improvement. Simple Digital Twin models visualizes these differences in order to enable recommendations.”; Pg. 268, “4.3 Advanced Digital Twins employ Artificial Intelligence technologies that leverage data from equipment to generate insights and deeper understanding of operating environments. These includes unstructured data analytics, multi-modal data analytics, component analytics, pattern recognition, learning models, knowledge networks etc. [22]. One sub-field of AI is machine learning.”; and

a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation and one or more physical asset digital twins of physical assets, wherein the digital twin system: executes a logistics simulation based on the logistics environment digital twin and the one or more physical asset digital twins, issues a logistics system design request from the artificial intelligence system based on a state of the logistics simulation, (Wolfgang: Pg. 263, “Digital Twins are virtual clones of real assets or processes. As the physical source is changing, the data from the physical asset or process are collected in real-time and replicated into the virtual equivalent in order to improve operative decision making.”; Pg. 265, “Aggregate – The real-time data have to be send into a data repository, processed and prepared for the analytics. The technologies of data aggregation and processing have evolved tremendously over the last years and allow nowadays to create massively scalable architectures with greater agility.”; Pg. 265, “Insight – Based on the analysed data, models for decision making are created. Significant differences in the performance of the Digital Twin model and the physical world indicate potentials for improvement. Simple Digital Twin models visualizes these differences in order to enable recommendations. More advanced Digital Twin models use iterative simulation to analyse with the virtual model the possible impact of changes for the real world in order to generate insights for decision making.”; Also see Fig. 4 of Wolfgang);
adjusts the state of the logistics simulation based on the logistics system design recommendation output by the artificial intelligence system in response to the logistics system design request, (Wolfgang: Pg. 265, “Act – The knowledge and recommendations from the insights step can be fed back to the physical world in order to transform the real enterprise. The insights may feed directly into actuators for movement and control, or into software systems on a higher level for operational or supply chains behaviour. With this interaction Digital Twins complete a closed loop connection from the physical world to the virtual model and back to the physical world.”).
As per claim 2, Wolfgang discloses:
wherein the digital twin system outputs a graphical representation of the environment digital twin to a display, whereby a user views the simulation via the display, (See Fig 3).
As per claim 3, Wolfgang discloses:
wherein the digital twin system outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to determine the logistics system design recommendation based on the simulation outcome, (Pg 264. “the monitoring and analysis of real-time data and advanced product, production or logistic simulation models allow to improve design, controls and strat-egies [15]. by use of simulation models the Digital twins can be used to operate the enterprise or parts of it in advance and to test drive several alternatives in a virtual environment before a decision is applied to the real-world system [16]. with artificial intelligence based capabil-ities Digital twins offer for specific questions advanced product, production or logistic simulation models, which allows to test drive several alternatives in a virtual environment before decisions are applied to the real-world system [17].”; Pg. 265, Insight – Based on the analysed data, models for decision making are created. Significant differences in the performance of the Digital Twin model and the physical world indicate potentials for improvement. Simple Digital Twin models visualizes these differences in order to enable recommendations. More advanced Digital Twin models use iterative simulation to analyse with the virtual model the possible impact of changes for the real world in order to generate insights for decision making).
As per claim 4, Wolfgang discloses: wherein the artificial intelligence system receives the request from a logistics design system that designs logistics systems, wherein the request includes one or more logistics factors corresponding to a proposed logistics solution of an organization, (Pg. 264 (3) with artificial intelligence based capabil-ities Digital twins offer for specific questions advanced product, production or logistic simulation models, which allows to test drive several alternatives in a virtual environment before decisions are applied to the real-world system [17]).
As per claim 5, Wolfgang discloses: wherein the logistics factors include one or more of: a type of product corresponding to the proposed logistics solution, one or more features of the type of product, a location of a manufacturing site, a location of a distribution facility, a location of a warehouse, a location of a customer base, proposed expansion areas of the organization, and supply chain features, (Pg. 265, 3.3 Data Quality and reliability- Data are the key for the success of Digital twins and any artificial intelligence use case. it is crucial to determine the type, quantity, and quality of data These data are coming directly from smart products...).
As per claim 6, Wolfgang discloses: wherein the logistics design system provides outcome data relating to the logistics system design recommendation to the machine learning system, and the machine learning system reinforces the machine- learned model that are used to determine the logistics system design recommendation based on the outcome data, (Pg. 266, 3.3 “Machine Learning anomaly detectors – Machine learning algorithms can be used, ranging from multivariate multi-level survival models to baseline asset risk, to classification techniques like logistic regression” Pg 268, 4.3, Due to advances in computer processing power, nowadays machine learning can be integrated into the decision-making processes implemented into enterprise software systems [21]).
As per claim 7, Wolfgang discloses: wherein the artificial intelligence system determines the logistics system design recommendation to minimize delay times, (Pg 268, 4.3, Decision making can be improved significantly by use of artificial intelligence technologies...[20]...By use of advanced decision-making approaches the software will be enabled to contribute increasing levels of performance and productivity; Pg. 267-268, 4.2, “a particular strategy can be built up from several priority classes, filtering and sorting rules, that are defined as default rules in order to optimize the global system or at least to improve local requirements. examples for such rules are e.g. “earliest Due Date”, “critical ratio” (Job with the least time to due date divided by total remaining processing time) or “critical path”).
As per claim 8, Wolfgang discloses: wherein the artificial intelligence system determines the logistics system design recommendation to comply with regulatory requirements, (Pg. 267-268, 4.2, “a particular strategy can be built up from several priority classes, filtering and sorting rules, that are defined as default rules in order to optimize the global system or at least to improve local requirements. examples for such rules are e.g. “earliest Due Date”, “critical ratio” (Job with the least time to due date divided by total remaining processing time) or “critical path”).
As per claim 9, Wolfgang discloses: wherein the digital twin system:
receives the logistics system design recommendation output by the artificial intelligence system in response to the logistics system design request; and generates an updated logistics environment digital twin of the logistics environment that incorporates the logistics system design recommendation output by the artificial intelligence system in response to the logistics system design request, (Wolfgang: Pg. 264, “With Artificial Intelligence based capabilities Digital Twins offer for specific questions advanced product, production or logistic simulation models, which allows to test drive several alternatives in a virtual environment before decisions are applied to the real-world system.”; Pg. 265, “Insight – Based on the analysed data, models for decision making are created. Significant differences in the performance of the Digital Twin model and the physical world indicate potentials for improvement. Simple Digital Twin models visualizes these differences in order to enable recommendations.”; Pg. 268, “4.3 Advanced Digital Twins employ Artificial Intelligence technologies that leverage data from equipment to generate insights and deeper understanding of operating environments. These includes unstructured data analytics, multi-modal data analytics, component analytics, pattern recognition, learning models, knowledge networks etc. [22]. One sub-field of AI is machine learning.”); and
adjusts the state of the logistics simulation based on the logistics system design recommendation output by the artificial intelligence system in response to the logistics system design request by executing the logistic simulation based on the updated logistics environment digital twin and the one or more physical asset digital twins, (Wolfgang: Pg. 265, “Act – The knowledge and recommendations from the insights step can be fed back to the physical world in order to transform the real enterprise. The insights may feed directly into actuators for movement and control, or into software systems on a higher level for operational or supply chains behaviour. With this interaction Digital Twins complete a closed loop connection from the physical world to the virtual model and back to the physical world.”).
As per claim 10, Wolfgang discloses: wherein each of the one or more physical asset digital twins of physical assets comprises a digital representation of a physical object in the value chain system, (Page 263, “the goal of Digital twins is to monitor and improve equipment and processes in a virtual
environment. Digital twins are virtual clones of real assets or processes. as the physical source
is changing, the data from the physical asset or process are collected in real-time and replicated
into the virtual equivalent in order to improve operative decision making [12]”).

As per claim 11, Wolfgang discloses: wherein the physical object is one or more of an asset, a device, a product, a package, a container, a vehicle, a ship, a container, a good, a product, or a component, (Page 263, “the goal of Digital twins is to monitor and improve equipment and processes in a virtual environment. Digital twins are virtual clones of real assets or processes. as the physical source
is changing, the data from the physical asset or process are collected in real-time and replicated
into the virtual equivalent in order to improve operative decision making [12]).
As per claim 21, Wolfgang discloses: wherein the one or more physical asset digital twins of physical assets includes a first physical asset digital twin corresponding to a package or container in which a product is shipped, and a second physical asset digital twin corresponding to a vehicle or other mode of transport utilized to ship the product, (Pg. 263, (3) The goal of Digital twins is to monitor and improve equipment and processes in a virtual environment. Digital twins are virtual clones of real assets or processes. as the physical source is changing, the data from the physical asset or process are collected in real-time and replicated into the virtual equivalent in order to improve operative decision making [12]; Pg. 269, (5) connect disparate systems such as backend business applications – Digital twin models can be used to connect with the backend business applications to achieve business outcomes in the context of supply chain operations including manufacturing, procurement, warehous-ing, transportation and logistics, field service, etc. [12]. Digital twins offer for specific questions an advanced product, production or logistic simulation model, which allows the monitoring and analysis of real-time data to improve design, controls and strategies”).
As per claim 13, Wolfgang discloses: wherein the logistics system design recommendation includes one or more of supply chain recommendations, storage and transport recommendations, proposed storage development, infrastructure recommendations, or combinations thereof, (Pg. 269, (5) Digital Twin models can be used to connect with the backend business applications to achieve business outcomes in the context of supply chain operations including manufacturing, procurement, warehousing, transportation and logistics, field service, etc. [12]).
As per claim 14, Wolfgang discloses:
wherein the supply chain recommendations include at least one of proposed suppliers, or implementations of a smart inventory systems that order on-demand parts from available suppliers, (Pg. 265-266 “3.3 Data Quality and Reliability - Data are the key for the success of Digital Twins and any Artificial Intelligence use case. It is crucial to determine the type, quantity, and quality of data. Real-Time Data are the key factor for Digital Twins. In recent years, real-time data generated across the manufacturing value chain have grown dramatically in volume and variety. These data are coming directly from 266 W. Kuehn, Int. J. of Design & Nature and Ecodynamics. Vol. 13, No. 3 (2018)
smart products, connected production equipment, core manufacturing processes, enterprise
IT systems, and external sources from customers or suppliers”).
As per claim 15, Wolfgang discloses:
wherein the storage and transport recommendations include at least one of proposed shipping routes, proposed shipping types, or proposed storage development, (Pg. 267, “nowadays increasing computation power and storage possibilities of big data engines, the improvement of analytics technologies and the integration of various data enables Digital twins to model much richer, less isolated and much more sophisticated and realistic models than ever before a Digital twin conceptual architecture has to be designed for flexibility and scalability in terms of the number of sensors and messages, applied analytics and processing in order to enable the architecture to evolve rapidly with growing demands [16].”).
As per claim 16, Wolfgang discloses: wherein the infrastructure recommendations include at least one of updates to machinery, adding cooled storage, or adding heated storage, (Pg. 264, (3.1) create – with multiple sensors various inputs from the physical process and its environ- ment are measured by use of integrated smart components. these measurements can be classified into the operational measurements sensing physical performance criteria of the asset and the measurement of environmental or external data affecting the operations of a physical asset. signals from the
sensors may be augmented with process-based information from systems such as the manufacturing
execution systems, enterprise resource planning systems, caD models and supply chains systems in
order to provide the Digital twin with a wide range of continually updating information to be used as input for the analysis).
As per claim 17, Wolfgang discloses:
wherein: (i) the digital twin system outputs a simulation outcome of the simulation to the machine learning system, (ii) the logistics design system provides outcome data relating to the logistics system design recommendation to the machine learning system, and (ili) the machine learning system reinforces the machine-learned model used to determine the logistics system design recommendation based on the simulation outcome and the outcome data, (Pg 264. “the monitoring and analysis of real-time data and advanced product, production or logistic simulation models allow to improve design, controls and strat-egies [15]. by use of simulation models the Digital twins can be used to operate the enterprise or parts of it in advance and to test drive several alternatives in a virtual environment before a decision is applied to the real-world system [16]. with artificial intelligence based capabil-
ities Digital twins offer for specific questions advanced product, production or logistic
simulation models, which allows to test drive several alternatives in a virtual environment
before decisions are applied to the real-world system [17].”;Pg. 265, Insight – Based on the analysed data, models for decision making are created. Significant differences in the performance of the Digital Twin model and the physical world indicate potentials for improvement. Simple Digital Twin models visualizes these differences in order to enable recommendations. More advanced Digital Twin models use iterative simulation to analyse with the virtual model the possible impact of changes for the real world in order to generate insights for decision making).
As per claim 18, Wolfgang discloses: wherein the artificial intelligence system determines the logistics system design recommendation to optimize one or more outcomes based on the simulation outcome and the outcome data, (Pg. 267-268, 4.2, “a particular strategy can be built up from several priority classes, filtering and sorting rules, that are defined as default rules in order to optimize the global system or at least to improve local requirements. examples for such rules are e.g. “earliest Due Date”, “critical ratio” (Job with the least time to due date divided by total remaining processing time) or “critical path”).
As per claim 19, Wolfgang discloses:
wherein the one or more outcomes include at least one of manufacturing times, manufacturing costs, shipping times, shipping costs, loss rate, environmental impact, compliance to a set of rules/regulations or combinations thereof, (Pg. 267-268, 4.2, “a particular strategy can be built up from several priority classes, filtering and sorting rules, that are defined as default rules in order to optimize the global system or at least to improve local requirements. examples for such rules are e.g. “earliest Due Date”, “critical ratio” (Job with the least time to due date divided by total remaining processing time) or “critical path”).
As per claim 20, Wolfgang discloses:
wherein the machine learning system reinforces the machine-learned model used to determine the logistics system design recommendation based on the simulation outcome and the outcome data by supplementing the training data sets with the simulation outcome and outcome data, (Wolfgang discloses: Pg 264. “the monitoring and analysis of real-time data and advanced product, production or logistic simulation models allow to improve design, controls and strat-egies [15]. by use of simulation models the Digital twins can be used to operate the enterprise or parts of it in advance and to test drive several alternatives in a virtual environment before a decision is applied to the real-world system [16]. with artificial intelligence based capabil-ities Digital twins offer for specific questions advanced product, production or logistic simulation models, which allows to test drive several alternatives in a virtual environment before decisions are applied to the real-world system [17].”).

Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Akiba Robinson whose telephone number is 571-272-6734 and email is Akiba.Robinsonboyce@USPTO.gov. The examiner can normally be reached on Monday-Thursday 6:30am-4:30pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor, Resha Desai can be reached on 571-270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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May 5, 2025
/AKIBA K ROBINSON/Primary Examiner, Art Unit 3628