DETAILED ACTION
This Action is in response to Applicant’s amendment filed on June 8, 2026. Claims 15-21, 23, and 26 are now pending in the present application. This Action is made NON-FINAL.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Applicant’s claim for domestic priority under 35 U.S.C. 120 is acknowledged. However, the Application Data Sheet (ADS) reflects incorrect priority information (see below) as it has both priority applications under Foreign Priority Information rather than one application under Domestic Benefit/National Stage Information and one application under Foreign Priority Information. The first paragraph of the specification, however, reflects the correct priority information. A supplemental Application Data Sheet (ADS) reflecting the correct priority information must be filed. See MPEP 601.05(a) and 37 CFR 1.55 and 1.78.
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Drawings
Figures 1-5 should be designated by a legend such as --Prior Art-- because only that which is old is illustrated. See MPEP § 608.02(g).
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference numbers not mentioned in the description: Reference numbers 1012 and 1014 in figure 7 are not mentioned in the description.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Reference number 18 mentioned on paragraph 0116 is not shown in figures 9A and 9B.
The drawings are objected to because of the following minor informalities:
On figures 9A and 9B, replace “e.g. processing” with --e.g., processing--; and
On figure 11C, replace “tuneing” with --tuning--
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office Action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended”. If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the Examiner, the Applicant will be notified and informed of any required corrective action in the next Office Action. If a response to the present Office Action fails to include proper drawing corrections, corrected drawings or arguments therefor, the response can be held NON-RESPONSIVE and/or the application could be ABANDONED since the objections/corrections to the drawings are no longer held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Replace the current title with the following title to have all acronyms spelled out –
--PHYSYCAL LAYER (PHY) SIGNALING AND ADAPTIVE INFERENCE TIMES FOR ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AI/ML) ON THE PHYSICAL LAYER--;
b) On line 10 of paragraph 0003, replace “IoT” with --Internet of Things (IoT)-- after “stationary”;
c) On line 4 of paragraph 0090, line 2 of paragraph 0101, lines 9-11 and 16 of paragraph 0130, line 5 of paragraph 0172, line 11 of paragraph 0190, line 2 of paragraph 0209, and lines 6 and 8 of paragraph 0214, replace “e.g.” with --e.g.,--;
d) On line 4 of paragraph 0113, replace “form” with --from-- after “12”;
e) On line 16 of paragraph 0130, replace “A” with --a-- before “signaling”;
f) On line 1 of paragraph 0133, replace “An” with --an-- before “order”;
g) On line 8 of paragraph 0156, replace “indicting” with --indicating-- after “message”;
h) On line 3 of paragraph 0167, replace “At” with --A-- before “set”;
i) On line 5 of paragraph 0169, replace “case” with --case,-- before “feedback”;
j) On line 4 of paragraph 0178, replace “mode” with --mode,-- after “connectivity”;
k) On line 3 of paragraph 0181, replace “A” with --a-- before “partial”;
l) On line 4 of paragraph 0185, replace “mode e.g.” with --mode, e.g.,--;
m) On line 2 of paragraph 0190, replace “online i.e.” with --online, i.e.,--;
n) On line 9 of paragraph 0196, replace “as” with --As--;
o) On line 5 of paragraph 0213, replace “threshold” with --threshold.--; and
p) On line 15 of paragraph 0219, replace “network,” with --network.--.
Appropriate correction is required.
Allowable Subject Matter
The indicated allowability of claims 22 and 23 is withdrawn in view of the newly discovered and/or reconsidered references to Wang et al. (US 2024/0291612 A1) and Qualcomm (Summary#1 of General Aspects of AI/ML Framework). Rejections based on the newly cited references follow.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 15-19 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over
Gundogan et al. (US 2023/0095981 A1) in view of Wang et al. (US 2024/0291612 A1).
Consider claim 15, Gundogan et al. disclose a user device, UE, of a wireless communication network, the wireless communication network using one or more Artificial Intelligence / Machine Learning, AI/ML, models for one or more use cases, wherein the UE is to use one or more of the AI/ML models, and wherein the UE is to signal to the wireless communication network an inference time the UE requires for executing the one or more of the AI/ML models (Subsequent to receiving the machine learning model and/or benchmarking data, at 3013, the UE 102 runs the benchmarking data on the machine learning model (e.g., executes the machine learning model) and generates machine learning model performance data and/or a benchmarking report…subsequent to generating all the outputs for the sample dataset, the UE 102 prepares a report of machine learning model performance data. The benchmarking report may include details with regard to model accuracy, model inference time, energy consumption of the machine learning model and/or the like, paragraph 51).
However, Gundogan et al. fail to disclose wherein the UE is to signal to the wireless communication network the inference time for a certain AI/ML model in case the inference time allows executing the certain AI/ML model in accordance with a processing time constraint associated with the use case for which the certain AI/ML model is used.
In the same field of endeavor, Wang et al. disclose wherein the UE is to signal to the wireless communication network the inference time for a certain AI/ML model (terminal device/UE transmits first information indicating capability related to an AI data processing model to the network device 220 (see figures 2 and 3 and paragraphs 0004, 0042, and 0097), the AI data processing model having an inference/application phase and the UE capability can indicate the processing time to infer the output (see paragraphs 0034, 0043 and 0046)) in case the inference time allows executing the certain AI/ML model in accordance with a processing time constraint (AI/ML model application and CSI reporting depend on UE capability and timing and the UE may support the first phase for a predetermined time/duty cycle and may drop reports when timing exceeds UE-reported capability (see paragraphs 0070 and 0087 – this disclosure implicitly suggest or at the very least make obvious to signal the inference-time capability when the timing allows supported AI/ML execution)) associated with the use case for which the certain AI/ML model is used (AI/ML use cases including CSI feedback, beam management, and RS overhead reduction, and specifically integrates AI/ML with the CSI-RS/CSI reporting framework (see paragraphs 0030, 0034-0036, 0087, and 0088)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Gundogan et al.’s UE-based machine-learning model benchmarking and reporting system to incorporate Wang et al.’s wireless AI/ML processing-time and CSI computation delay requirements, such that the UE signals the inference time for a particular AI/ML model to the wireless communication network only when that inference time permits execution of the model within the processing time constraint associated with the relevant wireless use case. A person of ordinary skill in the art would have been motivated to combine these teachings because both references address UE-side execution of AI/ML models in wireless communication systems and the need for the network to account for UE-specific AI/ML execution performance. Incorporating Wang et al.’s AI/ML-model-based processing time constraints into Gundogan et al.’s UE benchmarking/reporting framework would have predictably allowed the network to determine whether a given AI/ML model can be executed by the UE within the timing requirements of the relevant wireless use case before relying on that model. This would improve latency compliance, avoid stale or untimely AI/ML outputs, reduce unnecessary signaling of unusable model performance information, and assist the network in selecting, tuning, or replacing AI/ML models based on whether the UE can meet the applicable processing time requirement.
Thus, the combination represents the predictable use of Wang et al.’s known wireless AI/ML processing-time constraint technique in Gundogan et al.’s known UE AI/ML inference-time reporting and model optimization framework to achieve the expected benefit of ensuring that only AI/ML models capable of meeting the applicable wireless use-case timing requirements are reported, selected, or used (See MPEP § 2143 and KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398 (2007)).
Consider claim 16, and as applied to claim 15 above, Gundogan et al. fail to specifically disclose wherein the UE is to signal the inference time to at least one of a gNB, a UE and a relay UE.
In the same field of endeavor, Wang et al. disclose wherein the UE is to signal the inference time to at least one of a gNB, a UE and a relay UE (network device 220 which receives the capability that indicate the processing time to infer the output is a base station and the communication system 200 can be a fifth generation (5G) communication system hence the base station could be a gNB (see figures 2 and 3 and paragraphs 0038, 0039, and 0046)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wang et al. with the teachings of Gundogan et al. in order to internalizing end-to-end AI support functions in a 5G communication environment.
Consider claim 17, and as applied to claim 15 above, Gundogan et al., as modified by
Wang et al., disclose wherein the UE is to signal the inference time - in response to a transfer of the one or more of the AI/ML models from a network entity of the wireless communication network to the UE, or - in response to an activation of the one or more of the AI/ML models and/or AI/ML functionality from a network entity of the wireless communication network to the UE, or- in response to a request from a network entity of the wireless communication network, or - when accessing the wireless communication network, in case the UE is preconfigured with the one or more AI/ML models (Subsequent to receiving the machine learning model and/or benchmarking data, at 3013, the UE 102 runs the benchmarking data on the machine learning model (e.g., executes the machine learning model) and generates machine learning model performance data and/or a benchmarking report…subsequent to generating all the outputs for the sample dataset, the UE 102 prepares a report of machine learning model performance data. The benchmarking report may include details with regard to model accuracy, model inference time, energy consumption of the machine learning model and/or the like - figure 3 and paragraph 51).
Consider claim 18, and as applied to claim 17 above, Gundogan et al., as modified by Wang et al., disclose wherein the network entity of the wireless communication network transferring the AI/ML model or requesting the inference time comprises one or more of the following: - a further UE, or a Relay UE, or a Remote UE, - a Radio Access Network, RAN, entity, - a Core Network, CN, entity (see figure 3).
Consider claim 19, and as applied to claim 15 above, Gundogan et al., as modified by Wang et al., disclose wherein the UE is to - determine the inference time, or - receive the inference time from the wireless communication network (Subsequent to receiving the UE capability data, at 3009, the NWDAF/MDAS 304 selects and/or tunes a machine learning model and benchmarking data associated therewith based at least in part on the UE capability data. For example, a size of the benchmarking data should align with the UE hardware capabilities (e.g. memory and CPU/GPU), paragraph 48).
Consider claim 26, and as applied to claim 15 above, Gundogan et al., as modified by Wang et al., disclose wherein the UE is to receive from the wireless communication network a fall-back AI/ML model or information indicating to proceed according to a fall-back procedure to be used if the predefined processing time cannot be met by a currently used or requested to be used AI/ML model, or wherein the UE is (pre-)configured to use a fall-back procedure in case the processing time cannot be met by a currently used or requested to be used AI/ML model (the NWDAF/MDAS 304 may decide, based at least in part on the benchmarking report, whether to provide a new machine learning model. By way of example, the NWDAF/MDAS 304 may determine that the machine learning model accuracy is better than the service requirements but the inference time duration is higher. Accordingly, in the above example, the NWDAF/MDAS 304 may provide a less complex model to reduce the inference time duration, paragraph 52).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Gundogan et al. (US 2023/0095981 A1) in view of Wang et al. (US 2024/0291612 A1) as applied to claim 15 above, and further in view of Laddu et al. (US 2026/0032427 A1).
Consider claim 20, and as applied to claim 15 above, Gundogan et al., as modified by Wang et al., do not expressly disclose wherein the UE is to signal a number of instances of a certain AI/ML model and/or a number of AI/ML models the UE is able to handle in parallel.
In the same field of endeavor, Laddu et al. disclose wherein the UE is to signal a number of instances of a certain AI/ML model and/or a number of AI/ML models the UE is able to handle in parallel (the UE further reports the number of models supported in parallel, which model IDs can be parallel supported, and any associated considerations/restrictions for applying a parallel operation of the ML models, paragraph 71).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Laddu et al. with the teachings of Gundogan et al., as modified by Wang et al., in order for the network to use UE capability information to configure ML model parameters, decide/support model switching, or consider activating more than one model at a given time.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Gundogan et al. (US 2023/0095981 A1) in view of Wang et al. (US 2024/0291612 A1) as applied to claim 15 above, and further in view of Filoche et al. (US 2023/0275812 A1).
Consider claim 21, and as applied to claim15 above, Gundogan et al., as modified by Wang et al., do not expressly disclose wherein the UE is to select the inference time for a certain AI/ML model to be signaled from a set of configured or pre- configured inference times which the UE is able to achieve when executing the certain AI/ML model.
In the same field of endeavor, Filoche et al. disclose wherein the UE is to select the inference time for a certain AI/ML model to be signaled from a set of configured or pre- configured inference times which the UE is able to achieve when executing the certain AI/ML model (The server sends information regarding model split (number of chunks, size and ID of each chunk, expected inference time of each chunk on the target device, or reference inference time), paragraph 188).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Filoche et al. with the teachings of Gundogan et al., as modified by Wang et al., to adapt downloading of an AI/ML model to UE memory status.
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Gundogan et al. (US 2023/0095981 A1) in view of Wang et al. (US 2024/0291612 A1) as applied to claim 15 above, and further in view of Qualcomm (Summary#1 of General Aspects of AI/ML Framework).
Consider claim 23, and as applied to claim 15 above, Gundogan et al., as modified by Wang et al., fail to specifically disclose wherein the inference time for the certain AI/ML model is associated with a certain AI/ML model identity, ID, or functionality, and the UE is to report the AI/ML model ID only if the UE is able to meet the processing time constraint.
In the same field of endeavor, Qualcomm disclose the inference time for the certain AI/ML model is associated with a certain AI/ML model identity, ID, or functionality, and the UE is to report the AI/ML model ID only if the UE is able to meet the processing time constraint (see proposal 11 on page 34 and Panasonic proposal 4 on page 106).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Qualcomm with the teachings of Gundogan et al., as modified by Wang et al., in order to provide alternative means of AI/ML model reporting and selection through model ID.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
Appel et al. (U.S. Patent Application Publication # 2022/0188663 A1) disclose automated machine learning model selection.
Shen et al. (U.S. Patent Application Publication # 2022/0342713 A1) disclose an information reporting method, apparatus, device, and storage medium.
Suo et al. (U.S. Patent Application Publication # 2024/0334179 A1) disclose UE capability updating method, apparatus, and device.
Sun et al. (U.S. Patent Application Publication # 2024/0348510 A1) disclose a communication method, apparatus, communication device, and readable storage medium.
Any inquiry concerning this communication or earlier communications from the Supervisory Patent Examiner (SPE) should be directed to Rafael Pérez-Gutiérrez whose telephone number is (571)272-7915. The examiner can normally be reached Monday-Thursday from 6:15 am to 4:15 pm EST.
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Rafael Pérez-Gutiérrez
R.P.G./rpg
/Rafael Pérez-Gutiérrez/Supervisory Patent Examiner, Art Unit 2642
July 17, 2026
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