DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is in response to the communication filed on 11/25/2024. Claims 1-20 are pending.
Examiner Note
The examiner is here to serve, to assist, and to help applicant to the very best of his ability. The Primary Patent Examiner position is a position of serving and it is an honor to externally serve the applicant and attorney and to internally serve junior examiners and supervisors. The goal of the examiner is to work with and assist applicant to move cases along as efficiently as possible.
Applicant is encouraged to call examiner to schedule an interview if applicant has any questions about this action, wants to discuss any possible paths forward, has proposed amendments to the claims to run by the examiner, or for any other issues that applicant would like to discuss.
Examiner can normally be reached at (571) 270-3863 or michael.keller@uspto.gov, Monday-Friday, from about 6 AM - 10 PM EST and if your call is missed examiner will try to return call quickly, thank you.
Priority
This application is effectively filed 7/8/2024. The assignee of record is Lenovo (Singapore) Pte Limited. The listed inventor(s) is/are: POURAHMADI, Vahid; Nangia, Vijay; Kothapalli, Venkata Srinivas; Hindy, Ahmed.
Claim Objections
Claim 15 is objected to because of the following informalities: the claim recites the limitation “the UE” (Pg 4, Ln 2). There is insufficient antecedent basis for this limitation in the claim. It appears that “the UE” may be a typo of “a UE.” For purposes of examination “the UE” will be interpreted to be “a UE.”
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.
Claims 18-20 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 18 recites the limitation "second encoded data.” There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites “the first set of data” however claim 18 introduced a “first set of information” and a “set of test samples” not “a first set of data.” It is unclear what claim 19’s first set of data is referring back to. There is insufficient antecedent basis for this limitation in the claim.
Claim 20 recites to “the input data.” There is insufficient antecedent basis for this limitation in the claim.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin (IEEE Communications Society, Xingqin Lin, An Overview of AI in 3GPP’s RAN Release 18: Enhancing Next-Generation Connectivity?, posted on 3/25/2024, retrieved from https://www.comsoc.org/publications/ctn/overview-ai-3gpps-ran-release-18-enhancing-next-generation-connectivity, on 8/3/2026; hereinafter NPL1. Examiner citations are referring to the PDF document provided with the first office action from August 2026, thank you) in view of Jayasinghe Laddu (US 20240281348 A1, filed 1/25/2024; hereinafter Jay).
For Claim 1, NPL1 teaches a user equipment (UE) for wireless communication (NPL1 Pg 2), comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the UE to (NPL1 Pg 2 Examiner notes that UE and gNB comprise memory and processor as understood by one of ordinary skill in the art, thank you
Please see screenshot of NPL1 Fig. 1 below, thank you:
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implement a first encoder of a two-sided model that has been trained by a set of reference samples (NPL1 Pg 7 A typical input to an AI/ML model for the spatial-domain or temporal downlink beam prediction is layer 1 reference signal received power (L1-RSRP) measurements of beams within ‘Set B.’ A typical output from the AI/ML model is the predicted top-K beams in ‘Set A.’ The AI/ML model training and inference can reside at the gNB side or at the UE side. In scenarios where AI/ML inference occurs at the UE side, the UE needs to report its predicted beam(s) to the gNB. Alternatively, when AI/ML inference takes place at the gNB side, the UE is required to report its L1-RSRP measurements for the beams within ‘Set B’ to the gNB.
Please see NPL1 screenshot of Fig. 2 below, thank you:
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determine, using a first set of information and at least one of the first encoder or input data, at least one possible source of error associated with the two-sided model (NPL1 Pg 5-6 Using a two-sided AI/ML model for the air interface introduces a multitude of challenges. The first challenge involves the training of the two-sided AI/ML model. Within this context, 3GPP has investigated three types of training that involve varying degrees of collaboration between the network and the UE, as illustrated in Fig. 1. The training complexity inherent in a two-sided AI/ML model for the air interface is further compounded by the necessity for multi-vendor interoperability and compatibility. The CSI decoder located at the gNB needs to be compatible with different CSI encoders at the UEs, and vice versa. In scenarios where a common CSI decoder model is utilized for multiple CSI encoder models, the network-side training entity—under training type 1 or 2—must coordinate with UE vendors for joint training efforts. The release of a new UE type could potentially trigger retraining across all vendors. Similar challenges exist in training type 1 or 2 when a shared CSI encoder model is used for multiple CSI decoder models. These issues can be mitigated in training type 3. In particular, if a common CSI decoder model is used for multiple CSI encoder models (or vice versa), the retraining due to the release of a new UE type can be confined to involve only the associated UE vendor and the network vendor due to the separate training nature in training type 3.
A challenge in the legacy CSI reporting framework of NR pertains to a temporal lag between the time to which the reported CSI corresponds and the moment at which the gNB actually employs the CSI report. This time delay leads to a situation where the reported CSI becomes outdated, a phenomenon commonly referred to as channel aging. The pace at which the reported CSI becomes outdated is amplified by higher UE speeds. This concern becomes particularly pronounced in the context of multi-user multiple-input multiple-output (MU-MIMO) in massive MIMO deployments. The performance of MU-MIMO has been observed to deteriorate when UEs move at medium to high speeds.); and
… indicating the at least one possible source of error to a network entity (NPL1 Pg 10-11 Interoperability and testability are critical considerations in the development and deployment of standardized features within cellular networks, including AI/ML-based schemes. 3GPP RAN working group 4 (RAN4), responsible for setting performance requirements and defining test procedures, is investigating the interoperability and testability aspects for validating AI/ML-based performance enhancements. The incorporation of AI/ML into the air interface introduces significant challenges to the existing requirements and testing framework. AI/ML models are data-driven and often lack physical interpretations, rendering the prediction of their performance difficult. In this section, we highlight the key areas under development in 3GPP.
The scope of 3GPP RAN4 requirements and testing for AI/ML-based features encompasses a range of vital elements, including inference, LCM procedures, data generation and collection, and generalization verification. Core requirements include the performance monitoring procedure, functionality/model management procedure, and the corresponding latency and interruption requirements. 3GPP considers a reference block diagram for testing AI/ML-based features, as illustrated in Fig. 3. Within this framework, the device under test (DUT) can be either UE or gNB. The reference block diagram covers both one-sided and two-sided models. In the latter case, the test equipment incorporates a companion AI/ML model to perform joint inference with the model at the DUT. However, the methodology for devising a reference AI/ML model within the testing equipment to effectively test the performance of the corresponding AI/ML model within the DUT remains an ongoing topic of discussion.), wherein the first set of information comprises at least one of:
a subset of a set of reference samples used to train the first encoder (NPL1 Pg 6-7 reference signals), the subset of the set reference samples used to train the first encoder and a corresponding latent representation generated by a reference encoder, a set of information regarding a decoder associated with the first encoder (NPL1 Pg 5-6), and a set of information regarding a reference encoder (NPL1 Pg 5-6).
NPL1 does not explicitly teach transmit a message indicating the at least one possible source of error to a network entity.
However, Jay teaches transmit a message indicating the at least one possible source of error to a network entity (Jay ¶ 0099 When the UE uses a limited set of beam measurements (set B) as the input of an ML model and the ML model predicts the best beams from a set of beams (Set A) that is not measured fully by the UE beams, the probability of having errors during the inference stage of the model can be high, especially when changes on radio parameters/conditions occur in the network.
Please see screenshot of Jay Fig. 2A below, #8
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Jay and NPL1 are analogous art because they are both related to use of AI/ML within beam management.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the beam prediction techniques of Jay with the system of NPL1 to support ML model monitoring (Jay ¶ 0101).
For Claim 2, NPL1-Jay teaches the UE of claim 1, wherein the at least one processor is further configured to cause the UE to receive the first set of information from the network entity (NPL1 Fig. 2, Pg 6-7).
For Claim 3, NPL1-Jay teaches the UE of claim 1, wherein the at least one processor is further configured to cause the UE to measure a radio channel, and wherein the input data is associated with the measurement of the radio channel (NPL1 Pg 8).
For Claim 4, NPL1-Jay teaches the UE of claim 3, wherein the input data comprises at least one of a measured channel matrix of the radio channel and a precoder for the radio channel (NPL1 Pg 5).
For Claim 5, NPL1-Jay teaches the UE of claim 1, wherein the at least one processor is further configured to cause the UE to transmit a request for the first set of information to the network entity based on an event triggered at the UE (NPL1 Fig. 2 P1 # 1).
For Claim 6, NPL1-Jay teaches the UE of claim 1, wherein the first set of information further comprises at least one of a threshold value for determining the at least one possible source of error, and instructions for transmitting the message indicating the at least one possible source of error to the network entity (Jay ¶ 0099).
For Claim 7, NPL1-Jay teaches the UE of claim 6, wherein the instructions for transmitting the message indicating the at least one possible source of error to the network entity comprises instructions for transmitting at least one of only the possible sources of error, a probability value associated with one or more possible source of error, and a similarity metric associated with one or more possible source of error (Jay ¶ 0043, 0049, 0099).
For Claim 8, NPL1-Jay teaches the of claim 1, wherein the message indicating the at least one possible source of error comprises at least one identifier representing a respective possible source of error (NPL1 Pg 3-4 provide identifiers for the process of NPL1 Pg 5-8).
For Claim 9, NPL1-Jay teaches the UE of claim 8, wherein the at least one possible source of error indicated by the message comprises at least one of: the first encoder (NPL1 Pg 6-7), a first decoder of the two-sided model, a communication link between the UE and the network entity, and data shift of the two-sided model.
For Claim 10, NPL1-Jay teaches the UE of claim 8, wherein the message indicating the at least one possible source of error further comprises at least one of: a probability that the at least one possible source of error is the source of error, and a similarity metric computed based on the first set of information received from the network entity (Jay ¶ 0038, 0099).
For Claim(s) 11 & 17, the claim(s) is/are substantially similar to claim 1 and therefore is/are rejected for the same reasoning set forth above.
For Claim(s) 12, the claim(s) is/are substantially similar to claim 2 and therefore is/are rejected for the same reasoning set forth above.
For Claim(s) 13, the claim(s) is/are substantially similar to claim 3 and therefore is/are rejected for the same reasoning set forth above.
For Claim(s) 14, the claim(s) is/are substantially similar to claim 4 and therefore is/are rejected for the same reasoning set forth above.
For Claim(s) 15, the claim(s) is/are substantially similar to claim 9 and therefore is/are rejected for the same reasoning set forth above.
For Claim(s) 16, the claim(s) is/are substantially similar to claim 10 and therefore is/are rejected for the same reasoning set forth above.
For Claim 18, teaches a user equipment (UE) for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the UE to (NPL1 Pg 2): implement a first encoder of a two-sided model that has been trained by a set of reference samples (NPL1 Pg 5-6); receive a first set of information comprising a set of test samples where the set of test samples are based on a subset of the set of reference samples used to train the first encoder from the network entity (NPL1 Pg 5-6); encode the set of test samples using the first encoder to create encoded data (NPL1 Pg 6-7).
NPL1 does not explicitly teach transmit the second encoded data to the network entity.
However, Jay teaches transmit the second encoded data to the network entity (Jay ¶ 0099, Fig. 2A).
Jay and NPL1 are analogous art because they are both related to use of AI/ML within beam management.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the beam prediction techniques of Jay with the system of NPL1 to support ML model monitoring (Jay ¶ 0101).
For Claim 19, NPL1-Jay teaches the UE of claim 18, wherein the at least one processor is further configured to cause the UE to measure a radio channel, and wherein the first set of data is associated with the measurement of the radio channel (NPL1 Pg 8).
For Claim 20, NPL1-Jay teaches the UE of claim 19, wherein the input data comprises at least one of a measured channel matrix of the radio channel and a precoder for the radio channel (NPL1 Pg 5).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed below, thank you:
i. US 20240056833 A1, PREDICTIVE BEAM MANAGEMENT WITH PER-BEAM ERROR STATISTICS
Please see PTO-892 for additional listing of relevant prior art made of record but not relied upon, thank you.
Conclusion
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/MICHAEL A KELLER/
Primary Patent Examiner, Art Unit 2418
Prosecution Insights