The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This office action is in response to Applicant’s submission filed on 2 July 2024.
Claims 1-21 are pending.
Claims 1-21 are rejected under 35 U.S.C. 112(b) as indefinite.
Claims 1-13 are rejected for double patenting.
Claim 21 is objected to.
There is no art rejection for claims 1-21.
Claim Objections
Claim 21 is objected to because of the following informalities: the claim recites "model retaining" (line reciting "the at least one processor is configured to generate the supplemental information for model retaining or model finetuning"). This term appears to be a typographical error for "model retraining" (as used consistently elsewhere, e.g., claims 18–20 As written, "model retaining" is either indefinite or, if given its plain meaning, inconsistent with the rest of the claim set. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1–13 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of U.S. Patent No. 12,131,242 B1. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are anticipated by, or would have been obvious over, the reference claims, as explained below.
The instant application is a continuation of Application No. 18/461,473, now U.S. Patent No. 12,131,242 B1 ("the '242 patent"). Both name the same inventors and are commonly owned by Armada Systems Inc.
Claim 1. Instant claim 1 recites an apparatus with at least one memory and at least one processor coupled to it, configured to perform every step recited in claim 1 of the '242 patent. The table compares the two claims.
Instant Application:
18/762,094
US-PATENT:
12,131,242 B2
Claim 1:
An apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor configured to: receive a user input indicative of a request to deploy a simultaneous localization and mapping (SLAM) machine learning workload to a containerized edge data center unit at an edge location, wherein the containerized edge data center unit implements the SLAM machine learning workload locally at the edge location to provide routing and control instructions to one or more autonomous robotic devices connected to a local edge network implemented by the containerized edge data center unit; obtain a pre-trained SLAM machine learning model corresponding to the request, wherein the pre-trained SLAM machine learning model is configured to determine routing and control instructions for the one or more autonomous robotic devices based on image data and sensor data obtained from corresponding edge sensors associated with the containerized edge data center unit and connected to the local edge network; adjust, a subset of parameters of the pre-trained SLAM machine learning model to perform model optimization for the containerized edge data center unit, wherein the model optimization is based on the request and information indicative of the corresponding edge sensors associated with the containerized edge data center unit; transmit the pre-trained SLAM machine learning model to the containerized edge data center unit; receive, from the containerized edge data center unit, one or more batch uploads of inference results information associated with the routing and control instructions generated by the containerized edge data center unit with the pre-trained SLAM machine learning model for controlling the one or more autonomous robotic devices; generate an updated SLAM machine learning model by retraining or finetuning the pre-trained SLAM machine learning model based on the inference results information and inference features further included in the one or more batch uploads; and transmit the updated SLAM machine learning model to the containerized edge data center unit, wherein transmission of the updated SLAM machine learning model is responsive to receiving the one or more batch uploads of inference results information.
Claim 1:
A method comprising: receiving a user input indicative of a request to deploy a simultaneous localization and mapping (SLAM) machine learning workload to a containerized edge data center unit at an edge location, wherein the containerized edge data center unit implements the SLAM machine learning workload locally at the edge location to provide routing and control instructions to one or more autonomous robotic devices connected to a local edge network implemented by the containerized edge data center unit; obtaining a pre-trained SLAM machine learning model corresponding to the request, wherein the pre-trained SLAM machine learning model is configured to determine routing and control instructions for the one or more autonomous robotic devices based on image data and sensor data obtained from corresponding edge sensors associated with the containerized edge data center unit and connected to the local edge network; adjusting, a subset of parameters of the pre-trained SLAM machine learning model to perform model optimization for the containerized edge data center unit, wherein the model optimization is based on the request and information indicative of the corresponding edge sensors associated with the containerized edge data center unit; transmitting the pre-trained SLAM machine learning model to the containerized edge data center unit; receiving, from the containerized edge data center unit, one or more batch uploads of inference results information associated with the routing and control instructions generated by the containerized edge data center unit with the pre-trained SLAM machine learning model for controlling the one or more autonomous robotic devices; an updated SLAM machine learning model by retraining or finetuning the pre-trained SLAM machine learning model based on the inference results information and inference features further included in the one or more batch uploads; and transmitting the updated SLAM machine learning model to the containerized edge
Claims 2-13
Claims 2-13
Claim Rejections - 35 USC § 112
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.
A claim is indefinite if, when read in light of the specification, it fails to inform, with reasonable certainty, those skilled in the art about the scope of the invention. Nautilus, Inc. v. Biosig Instruments, Inc., 110 USPQ.2d 1688, U.S. Supreme Court (2014).
Claims 1-21 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 pre-AIA the applicant regards as the invention.
Regrading claim 1,
(a)The limitation "obtained from corresponding edge sensors" lacks antecedent basis. The term "corresponding" presupposes a prior point of reference, but no edge sensors have been previously introduced in the claim. It is unclear what the recited edge sensors "correspond" to, the claim is therefore indefinite. Appropriate correction would be introducing this element as "a plurality of edge sensors" or "one or more edge sensors" on first recitation.
(b) The limitation "adjust, a subset of parameters" contains a grammatically anomalous comma that renders the scope of the "adjust" step unclear — specifically, whether "adjust" is intended to modify an antecedent element not otherwise recited, the claim is therefore indefinite. Clarification is required.
(c) The limitation "…inference features further included in the one or more batch uploads" introduces "inference features" without antecedent basis or definition. It is unclear whether "inference features" refers to intermediate feature representations generated during model inference (as distinct from "inference results information," separately recited), or some other data. The metes and bounds of this limitation cannot be determined, the claim is therefore indefinite.
Regarding claims 2-21, which depend on above rejected claim 1, are rejected for the same reason.
Regarding claim 2, the claim recites "a machine learning (ML) or artificial intelligence (AI) application" without establishing its relationship to the "SLAM machine learning workload" of claim 1. It is unclear whether "the ML or AI application" is the same thing as the previously recited SLAM machine learning workload, a component of it, or a separate element, the claim is therefore indefinite. Clarification of antecedent basis is required.
Regarding claim 11, the claim recites "the machine learning model" (final clause) without antecedent basis for that exact term; claim 1 establishes only "the pre-trained SLAM machine learning model." It is unclear whether these refer to the same element, the claim is therefore indefinite.
Regarding claim 14, the claim recites "wherein the request using a satellite internet constellation connectivity link..." This clause is grammatically incomplete (missing a verb, e.g., "is transmitted using"), rendering the scope of the limitation indeterminate, the claim is therefore indefinite.
Regarding claims 14-17, each of which recites "the edge data center unit," dropping the modifier "containerized" used consistently in claim 1 ("the containerized edge data center unit"). It is unclear whether "the edge data center unit" refers to the same element as "the containerized edge data center unit," or introduces a new, broader, antecedent-less element, the claim is therefore indefinite.
Regarding claims 15–17, 19, and 21, each of which recites "the pre-trained machine learning model," dropping "SLAM" from the term established in claim 1 ("the pre-trained SLAM machine learning model"). It is unclear whether this is the same model or a different one, the claims are therefore indefinite.
Regarding claims 18-21, each of which recites "the edge compute unit" — a term that does not appear anywhere in claim 1 or any of claims 2–17 (which consistently use "containerized edge data center unit"). "The edge compute unit" therefore lacks antecedent basis entirely and its scope cannot be determined. This deficiency recurs in claim 18 ("transmit the updated machine learning model to the edge compute unit"), claim 19 (two instances), claim 20, and claim 21 (two instances, including "one or more additional edge compute units different from the edge compute unit"). The claims are therefore indefinite.
Regarding claim 18,
(a)the claim recites "the one or more batch uploads of information," which does not match the antecedent term "the one or more batch uploads of inference results information" established in claim 1. It is unclear whether these are the same batch uploads or different data, the claim is therefore indefinite.
(b) the claim further recites "the inference performed by the edge data center unit" — "inference" as a performed act/step has no antecedent basis; claim 1 recites only that "routing and control instructions [were] generated... with the pre-trained SLAM machine learning model," not a step of "performing inference." The claim is therefore indefinite.
Regarding claims 19-21, which depend on above rejected claim 18, are rejected for the same reason.
Regarding claim 21, the claim recites "model retaining" (line reciting "the at least one processor is configured to generate the supplemental information for model retaining or model finetuning"). This term appears to be a typographical error for "model retraining" (as used consistently elsewhere, e.g., claims 18–20 As written, "model retaining" is either indefinite or, if given its plain meaning, inconsistent with the rest of the claim set.
Allowable Subject Matter
Claims 1-21 are allowed since when reading the claims in light of the specification, as per, MPEP §2111.01 or Toro Co. v. White Consolidated Industries Inc., 199F.3d 1295, 1301, 53 USPQ2d 1065, 1069, 1069 (Fed.Cir. 1999), none of the references of record alone or in combination disclose or suggest the combination of limitations specified in claims 1-21.
The following is an examiner’s statement of reasons for allowance:
In interpreting the claims, in light of the specification filed on 7/2/2024, the Examiner finds the claimed invention to be patentably distinct from the prior arts of record.
None of the cited prior art references, singly or in combination, fully teaches all limitations of independent claim 1.
Regarding the claims, the primary reason for allowance is the specific combination of pre-deployment adjustment of a subset of SLAM model parameters based on identified edge sensor configuration, combined with a closed-loop process wherein cloud-side retraining or finetuning of the SLAM model is triggered by batch-uploaded inference results and features, and the resulting updated model is redeployed to a containerized edge data center unit to control autonomous robotic devices over a local edge network.
The following are references close to the invention claimed:
Shama et al., US-PGPUB No. 20200327371A1 ("Intelligent Edge Computing Platform with Machine Learning Capability") [hereafter Shama] teaches a closed-loop edge platform where a cloud-trained model is "edge-ified" for constrained resources and periodically re-evaluated and updated using local sensor data. However, Shama does not teach the specific claimed combination of a containerized edge data center unit performing SLAM to generate routing/control instructions for autonomous robotic devices, with sensor-specific pre-deployment parameter adjustment.
Rajkumar et al., US Patent No. 10730181B1 ("Enhancing Robot Learning") [hereafter Rajkumar] teaches a server aggregating teachings from a fleet of robots, fine-tuning or fully retraining models, and pushing updated models fleet-wide on a periodic schedule. However, Jules does not teach the specific claimed combination of SLAM-based localization/mapping inference, containerized edge data center architecture, or sensor-informed pre-deployment model parameter optimization.
Chen et al., US Patent No. 11704577B1 ("High Performance Machine Learning Inference Framework for Edge Devices") [hereafter Chen] teaches containerized ML training instances and fleet-wide deployment tracking of models to robotic devices with sensors. However, Chen does not teach the specific claimed combination of SLAM-specific routing/control instruction generation and a batch-upload-triggered cloud retraining/finetuning loop tied to a local edge network.
Huang et al., "Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping," IEEE Internet of Things Journal, vol. 9, no. 15, pp. 14087–14102, 2022 [hereafter Huang] teaches a robot-edge-cloud SLAM architecture offloading raw robot data to edge servers for real-time map fusion, then to cloud for global merging. However, Huang does not teach sensor-specific pre-deployment parameter adjustment or a containerized edge unit generating robot control instructions with cloud-based model retraining feedback.
Ben Ali et al., "Edge-SLAM: Edge-Assisted Visual Simultaneous Localization and Mapping," Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services (MobiSys 2020), pp. 325–337 [hereafter Ben Ali] teaches splitting visual SLAM tracking and mapping between a mobile device and edge server to reduce onboard resource demands. However, Ben Ali does not teach a containerized edge data center controlling autonomous robots, pre-trained model parameter optimization for specific sensors, or cloud retraining triggered by batch inference uploads.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TSU-CHANG LEE whose telephone number is 571-272-3567. The fax number is 571-273-3567.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas, can be reached 571-272-2589.
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/TSU-CHANG LEE/
Primary Examiner, Art Unit 2128
