DETAILED ACTION
This is in response to the amendment filed on January 15th 2026.
Response to Arguments
Applicant’s arguments, see pg. 9-10, filed 1/15/26, with respect to the rejection(s) of claim(s) 1, 9 and 17 under 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Chitale et al. US 2017/0091071 A1.
Applicant’s remaining arguments regarding the 103 rejection, pg. 11, are moot in view of the new ground of rejection.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-2, 9-10 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Weingarten et al. US 2019/0052659 A1 in view of Chitale et al. US 2017/0091071 A1.
Regarding claim 1, Weingarten discloses;
logically grouping edge locations of a telecommunication network into different groups based on characteristics of the edge locations (group/cluster endpoints based on similar or different characteristics – abstract, Figs. 1, 14, paragraphs 67, 90-91 and 101-102);
identifying, for each of the edge locations, operation baselines of artificial intelligence (AD) and/or machine learning (ML) models over a predetermined period of deployment of the AI and/or ML models on the edge locations of the telecommunication network (determine baseline for AI and/or machine learning model – paragraphs 17, 65, 71, 101-102, 111, 118, 121, 167 and Fig. 2);
estimating, based on the operation baselines, future operation efficiencies of the AI and/or ML models deployed on the edge locations (assess future activity to determine if it is within the baseline – paragraph 118, also see paragraph 112 which discloses assessing future activity of the AI agents “based on the baseline”; and make predictions based on collected data – paragraph 121);
wherein estimating the future operation efficiencies of the AI and/or ML models deployed on the edge locations includes identifying … trends of development of conditions in the edge locations (identify “patterns” at edges which are equivalent to “trends” under the BRI – see paragraphs 5, 21, 67-68, 91 and 121);
determining a first update for increasing a first of the future operation efficiencies of the AI and/or ML models (update configuration based on baseline – paragraphs 71, 82, 87; update model – paragraphs 171, 174 and Fig. 8); and
causing the first update to be performed within the telecommunication network (the system is dynamic – paragraphs 6-31; update endpoints – paragraphs 87, 174).
Weingarten generally discloses reporting data (e.g. user interface displays reporting violations, number or percentages, etc. – see paragraph 177) but does not explicitly disclose identifying the trends in domain condition reports. But this is taught by Chitale as a software prediction system that uses predictive models to analyze trends, patterns and information from current developments and generate a report identifying the trends, patterns and conditions (Figs. 2-3, abstract, paragraphs 13, 18).
It would have been obvious to one of ordinary skill in the art to modify Weingarten to estimate operation efficiencies of software on edge locations by identifying trends of conditions in a condition report as taught by Chitale for the purpose of improving or updating edge software deployments. Chitale suggests that by using models, automated analysis is performed which predicts faster and offers improvements like taking action earlier (paragraphs 14, 19).
Regarding claim 2, Weingarten discloses the first update includes reconfiguring models of the AI and/or ML models (update models – paragraph 174), wherein the first update includes redistributing at least some of the edge locations within the telecommunications network (system is dynamic – abstract, thus it changes over time by definition; also see paragraph 82 which teaches updating groupings; paragraph 95 teaches endpoints change locations because they are mobile, e.g. laptop; and paragraph 114 which teaches an “elastic edge” in response to recently deployed agents).
Regarding claim 9, it is a computer program product/computer readable storage medium that corresponds to the method of claim 1; therefore, it is rejected for the same reasons.
Regarding claim 10, it corresponds to previously presented dependent claim 1; thus it is rejected for the same reasons.
Regarding claim 17, it is a system claim that corresponds to the method of claim 1. Weingarten also discloses a system comprising a processor and computer-readable storage media to perform the method of claim 1 (Fig. 5, paragraphs 133-137). Thus, the claim is rejected for the same reasons.
Regarding claim 18, it corresponds to claim 2; therefore it is rejected for the same reasons.
Claim(s) 3-4, 11-12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Weingarten and Chitale in view of Falkenberg US 2023/0065410 A1.
Regarding claim 3, Weingarten discloses wherein the characteristics of the edge locations include whether a majority of users of a given one of the edge locations are using (monitor what percentages of users – paragraph 177). The combination of Weingarten and Chitale does not explicitly disclose using ultra reliable low-latency communications (uRLLC), but this is taught by Falkenberg as a prediction based system that determines and supports using URLLC (paragraphs 41 and 86). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Weingarten and Chitale with uRLLC as taught by Falkenberg. This is merely the combination of a well-known technique (e.g. defined in 3GPP) according to its established function in order to yield a predictable result.
Regarding claim 4, Weingarten discloses wherein the characteristics of the edge locations are selected from … whether a majority of users of a given one of the edge locations are using (monitor what percentages of users – paragraph 177). The combination of Weingarten and Chitale does not explicitly disclose using enhanced mobile broadband (eMBB) or massive machine type communications (mMTC), but this is taught by Falkenberg as a prediction based system that determines and supports using both eMBB and mMTC (see paragraph 41). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Weingarten and Chitale with eMBB or mMTC as taught by Falkenberg. This is merely the combination of a well-known technique (e.g. defined in 3GPP) according to its established function in order to yield a predictable result.
Regarding claims 11-12, they correspond to previously presented dependent claims 3-4 respectively. Thus, they are rejected for the same reasons.
Regarding claim 19, it corresponds to claim 3; thus it is rejected for the same reasons.
Claim(s) 5-8, 13-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Weingarten and Chitale in view of Jain et al. US 2024/0007414 A1.
Regarding claim 5, Weingarten discloses identifying the operation baselines of AI and/or ML models includes identifying for the edge locations, as discussed above. Weingarten does not explicitly disclose identifying extents of development of fulfillment of associated SLAs within a predetermined a amount of time. But this is taught by Jain as a system for optimizing edge networks, including measuring benchmarks related to SLA information/violations (see abstract, paragraphs 96, 220-225 and Fig. ID5_C).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Weingarten and Chitale to incorporate the SLA benchmark as taught by Jain for the purpose of managing edge devices. Weingarten is a dynamic system that can be updated in real-time. Jain teaches that by monitoring edge workloads, SLA violations/deviations can be detected so that compliance can be met or predicted which allows a cost function to be optimized (see paragraphs 226-230).
Regarding claim 6, Weingarten discloses wherein identifying the operation baselines of AI and/or ML models includes identifying, for the edge locations, extents of activity of
the AI and/or ML models over the predetermined period of deployment, wherein the extents of activity are based on central processing unit (CPU) utilization and/or graphics processing unit (GPU) utilization (determine processor behavior, monitor current processes running, CPU cycles, etc. – paragraphs 15, 66, 91-92).
Regrading claim 7, Weingarten discloses logically re-grouping the edge locations based on the identified trends (update groupings based on data/patterns – paragraphs 18, 82, 101); and correlating the re-grouped edge locations with historical groupings (monitor data for dynamic edge groups and compare to historical data – paragraph 177), wherein the estimated future operation efficiencies of the AI and/or ML models are based on the correlation (assessing future activity and making predictions is based on data – paragraphs 112, 118 and 121; correlating endpoint groups with historical information is simply a data association, so the future activity estimation/prediction taught by Weingarten also reads on the claimed feature of the estimated future operation being based on the correlation, because under the BRI, that includes making the future prediction/estimation based on data).
Weingarten does not explicitly disclose checking an extent of development of fulfillment of an associated SLA within a predetermined about of time, but this is taught by Jain as discussed above. The motivation to combine is the same.
Regarding claim 8, Weingarten discloses identifying trends of development of conditions in the edge locations includes causing predetermined generative AI models to … identify the trends (AI agent / ML model identify conditions / patterns – abstract, paragraphs 5, 66, 68, 90, 118 , Fig. 2). Weingarten also discloses the predetermined generative AI models are deployed on the centralized entity (AI techniques are employed by the central server – paragraph 17).
Weingarten does not explicitly disclose the domain condition reports but this is taught by Chitale as discussed above. Chitale further discloses the domain condition reports are generated by an receive on a centralized entity from a distributed entity (Fig. 1). The motivation to combine is the same.
Regarding claims 13-16, they correspond to claims 5-8 respectively; therefore they are rejected for the same reasons.
Regarding claim 20, it corresponds to claim 5 so it is rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Amini et al. US 2025/0317739 A1 discloses edge-based machine learning to establish baseline performance and update ML models to improve efficiency (abstract, Fig. 1, paragraphs 47, 118).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON D RECEK whose telephone number is (571)270-1975. The examiner can normally be reached Flex M-F 9-5.
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, Umar Cheema can be reached at 571-270-3037. 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.
/JASON D RECEK/Primary Examiner, Art Unit 2458
Prosecution Insights