Patent Application 18024884 - Predicting Performance of a Localization-Related

Title: Predicting Performance of a Localization-Related Device

Application Information

Invention Title: Predicting Performance of a Localization-Related Device
Application Number: 18024884
Submission Date: 2025-05-15T00:00:00.000Z
Effective Filing Date: 2023-03-06T00:00:00.000Z
Filing Date: 2023-03-06T00:00:00.000Z
National Class: 455
National Sub-Class: 456100
Examiner Employee Number: 81681
Art Unit: 2632
Tech Center: 2600

Rejection Summary

102 Rejections: 1
103 Rejections: 0

Cited Patents

No patents were cited in this rejection.

Office Action Text

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 .

Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.

The abstract of the disclosure is objected to because: (1) The 1st sentence repats the title (2) The 1st sentence uses the phrase “It is provided …”, which can be implied (see underlined text above). A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).

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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.

Claims 31 – 50 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by
Kattepur et al. (US 20180276049; which has been provided in the International Search Report).

Regarding claim 31, Kattepur discloses a method performed by a predictor device for facilitating predictions related to localization and/or mapping performance of one or more devices of a device network (Fig. 1, element 100; [0041]; wherein the predictor device is interpreted as element 100, which performs that estimation), the method comprising:
detecting that a first localization-related device has joined the device network ([0037] discloses networked robots; wherein the first localization-related device is interpreted as a robot and joining the network is inherent in a “networked robot”; Fig. 3 shows “ …mobile robots”; As per Applicants specification, [0050] states “The mobile localization-related devices 2a-b could also be implemented using a smartphone, a tablet computer, and autonomously mobile device (e.g., robot),…”);
obtaining a first set of properties of the first localization-related device (Fig. 2, block 202; [0046] discloses “…dynamically profile, at step 202, computational tasks on a development testbed for a-priori estimation of computational time and energy requirements…”; wherein obtaining the 1st set of properties is interpreted as forming the profile);
determining, based on the first set of properties, a predictive model for the first localization-related device (Fig. 2, block 204; [0061] discloses “… estimate performance across multiple hardware devices …”; [0060], Equation 4 and [0061], Equation 5 disclose predictive models for battery lifetime and run time ratio on processors);
and deploying the predictive model for the first localization-related device (Fig. 2, blocks 204, 206; [0063] discloses “…for executing the computational tasks on one or more deployment hardware based on benchmarks and parallel processing models …”).

Regarding claim 32, Kattepur discloses determining a predictive model comprises generating the predictive model by interpolating and/or extrapolating at least one known predictive model (Fig. 2, block 204; [0005]; [0063]) based on the following:
the first set of properties of the first localization-related device ([0063], page 5, left column, 70% down the column discloses “1. Input: Robot/UAV sensor data set;..”,
and device properties associated with the at least one known predictive model ([0063] discloses “ …the computational time and the energy requirements is further based on number of cores and rated frequency of the CPU associated with the one or more deployment hardware…”).

Regarding claim 33, Kattepur discloses adapting the predictive model for the first localization-related device based on observed performance of the first localization-related device while the first localization-related device is active in the device network (Fig. 2, block 202 discloses “dynamically profiling …”; wherein adapting the predictive model is interpreted as dynamically profiling. Active in the network is inherent since of out of network communication would not be possible).

Regarding claim 34, Kattepur discloses the observed performance of the first localization-related device is based on one or more of the following metrics or indicators: estimation error, execution speed, and energy consumption ([0046] discloses “… at step 202, computational tasks on a development testbed for a-priori estimation of computational time and energy requirements for executing the computational tasks …”; wherein execution speed is related to computations time. Energy consumption is also related to battery life: Fig. 9; [0030]).

Regarding claim 35, Kattepur discloses determining a predictive model comprises selecting a type of predictive model according to the first set of properties of the first localization-related device (Fig. 5 shows robot with single core CPU and 1GB RAM which would be properties of the 1st localization-related device; [0100] discloses “By making use of performance benchmarking tools, processing times on heterogeneous robot/Fog/Cloud deployment hardware (FIG. 5) are estimated with varying data sizes as well.”).

Regarding claim 36, Kattepur discloses the predictive model is deployed in one or more of the following: the first localization-related device, and a model aggregator configured to predict performance of a plurality of localization-related devices based on respective predictive models (Fig. 3 discloses “Public/Private/hybrid Cloud”; [0045] discloses “Periodic snapshots or analytics on data are stored in a cloud data center. While the cloud may be used for long term analysis and goal setting, the geographically distributed set of mobile services make use of fog nodes for computation.”; wherein the model aggregator is interpreted as the cloud which predicts performance for multiple robot devices).

Regarding claim 37, Kattepur discloses the first set of properties comprises hardware properties of the first localization-related device, including one or more of the following: random-access memory (RAM) size, number of processor cores, processor type, processor model, and energy source (Fig. 5 shows robot with single core CPU and 1GB RAM which would be properties of the 1st localization-related device; page 5, right column, Table II also shows robot properties).

Regarding claim 38, Kattepur discloses the predictive model provides one or more of the following outputs: an application quality metric, an application performance metric, an application robustness metric, an application efficiency metric, a predicted energy consumption, and a predicted mapping error ([0097] discloses “The importance of accurate profiling and estimation of computation times/energy consumptions may be noted in setting these runtime rules.”).

Regarding claim 39, Kattepur discloses the predictive model has one or more of the following inputs: a current task, a previous task, a metric of a resource availability, and a current resource allocation for localization and/or mapping (Fig. 5 shows robot with single core CPU and 1GB RAM; page 5, right column, Table II also shows robot properties; wherein a metric of a resource availability is interpreted as the # of CPUs, RAM, etc.).

Regarding claim 40, Kattepur discloses wherein one or more of the following applies:
the device network is configured to be used for simultaneous localization and mapping (SLAM) processing ([0068]; [0074]);
and the predictive model is used for workload allocation among localization-related devices of the device network.

Claim 41 is similarly analyzed as claim 31. Memory and processor are disclosed by Kattepur (Fig. 1, elements 102, 104).

Claim 42 is similarly analyzed as claim 32.

Claim 43 is similarly analyzed as claim 33.

Claim 44 is similarly analyzed as claim 34.

Claim 45 is similarly analyzed as claim 35.

Claim 46 is similarly analyzed as claim 36.

Claim 47 is similarly analyzed as claim 37.

Claim 48 is similarly analyzed as claim 38.

Claim 49 is similarly analyzed as claim 39.

Claim 50 is similarly analyzed as claim 40.

Other Prior Art Cited
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.

The following patents/publications are cited to further show the state of the art with respect to predicting performance of localization- related devices:

Byers et al. (US 20180183660) discloses Configuring Heterogeneous Computing Environments Using Machine Learning.
Davey et al. (US 20160253761) discloses Robotically Assisted Banking Automation and Insurance System.

Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADOLF DSOUZA whose telephone number is (571)272-1043. The examiner can normally be reached Mon - Fri 9 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chieh M Fan can be reached at 571-272-3042. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ADOLF DSOUZA/Primary Examiner, Art Unit 2632