CTNF 18/672,072 CTNF 89309 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim(s) 1, 3, 5-12, 14, and 16-20 , is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kaya et al. (US 2022/0190883 A1) . Regarding claims 1 and 12, Kaya discloses: a communication apparatus ( fig.1 depicts a wireless communications network, and par.[0005] describes determining a beam sequence or beam selection sequence, par.[0034] describes a beam management method corresponding to par.[0005]), comprising a processor (fig.12 which depicts a processor element 1204) , wherein the processor is coupled to a memory (fig.12 element 1206 which depicts a memory) configured to store non-transitory instructions; and the processor is configured to execute the non-transitory instructions thereby enabling the communication apparatus to implement operations comprising, performing a communication method: a communication method (fig.1 depicts a wireless communications network, and par.[0005] describes determining a beam sequence or beam selection sequence, par.[0034] describes a beam management method corresponding to par.[0005]) , comprising: determining, X pieces of downlink measurement (par.[0034 - 0035] describes performing measurement for one or more received SSB beams/SSB resources and/or CSI-RS) based on X downlink signals (par.[0034-0035] describes the received SSB which are transmitted on the downlink. That is a UE may receive from a base station, a one or more SSB and/or CSI-RS which correspond to a one or more beams. The UE can perform measurements on the SSB which are received) from an access network device (par.[0034 – 0035 and 0037] teaches a base station, which is an access network device, see fig.1 BS element 134, and the associated disclosure of fig.1 and element 134) , where X is an integer greater than 1 (par.[0034 – 0035 and 0037] describes a one or more SSB which are received at the UE, the UE may select a beam or a set of beams based on the received SSB) ; determining a first downlink and a second downlink signal (par.[0034-0035 and 0037] which describes the SSB/PBCH and/or CSI-RS which comprises a one or more downlink signals. Par.[0076] the UE/UEs may send measurement reports based on the reception of downlink signalling from the base station. The UE’s receive and select a one or more reference signals corresponding to a one or more beams and forward the reporting to the base station) , wherein the first downlink and the second downlink signal are downlink signals in the X downlink signals in the X downlink signals (as discussed above, the UE can receive a one or more downlink signals such as SSB and/or CSI-RS which are reference signals used for beam management from the network device, par.[0034 – 0035, and 0037]) , the first downlink signal is determined based on an output of a first prediction model (fig.5 element 440, and par.[0075] which recites, in part, “Request a measurement report for one or more beams listed in predicted future beam sequence for UE”) , an input of the first prediction model is determined based on Y pieces of downlink measurement information in the X pieces of downlink measurement information (fig.5 element 420, and par.[0073] which recites, in part, “training a Beam Sequence Model for Prediction. This step may include training the beam sequence model (e.g., neural network) at the BS/TRP with (or based on) data gathered from or with respect to multiple UEs, e.g., to allow the beam sequence model to provide or output a predicted future beam sequence for one or more UEs.” That is, the base station transmits a plurality of downlink signal SSB/CSI-RS which correspond with the X downlink signals, the base station may use Y SSB/CSI-RS reports to train the prediction model, which is used for transmitting the first downlink signal based on the previous reports associated with the Y measurement information from the X measurement information) , the second downlink signal is determined based on the X pieces of downlink measurement information (as discussed above in par.[0034 – 0035, and 0037] the UE is capable of reception of a plurality of SSB/CSI-RS which comprise X received signals separate from the signals used to train the machine learning algorithm) , and Y is an integer less than or equal to X and greater than 0 (par.[0073] wherein the UE can receive X SSB/CSI-RS and the UE and/or BS utilizes specific reports which correspond to specific signal, such as best beam measurement reports. Also par.[0070] which recites, in part, “Thus, TRP A, after receiving a measurement report(s) from the UE indicating beam 1, and then subsequently beam 2 as the best (e.g., highest RSRP beams), may obtain (from the beam sequence model) a predicted future beam sequence for the UE that indicates (e.g., with 75% probability)”) ; and sending first information or second information to the access network device based on the first downlink and the second downlink signal (as discussed above, the UE can send measurement reports of best beams, which are considered the first information as described with reference to par(s).[0073 – 0075] additionally, the UE can report a plurality of beams during initial training without segregating the best beams, as discussed par.[0072] and fig.4 ); and wherein the first information is usable to indicate that the first prediction model is to be updated, or the first information is usable to indicate the second downlink signal and the Y pieces of downlink measurement information (par.[0042] which recites, in part, “First, for example, it may be desirable to improve performance of beam switching decisions by a BS, e.g., to allow a BS to more consistently select an appropriate (or even a best) beam prior to a connection or performance drop.” And par.[0073] which recites, in part, “the training can make use of the beam indices of the previously used beams (or make use of the measurement reports indicating the best beam(s) for the UE) at every 10 ms interval for a period of a few seconds to predict the future beam indices likely to be used (or that should be used to improve performance) by the user/UE in the next few hundred ms, for example.” As can be seen, the UE report best beams, which are indication to update the prediction model to improve the performance of the prediction model in future beam allocation scenarios, par.[0087]) ; and the second information is usable to indicate that the first prediction model is trusted, the second information is usable to indicate that the first prediction model does not need to be updated, or the second information is usable to indicate that the second information does not comprise the Y pieces of downlink measurement information (fig.4 element 440 element 4, par.[0076] which recites, in part, “Alternatively, the serving beam, used by the BS or used by the UE, may be used to determine a past beam sequence for a UE. The serving beams (beams used by a UE or BS for communication) may often be the same as the best reported (e.g., highest RSRP) beams,”. In this way when the beams reported are already or previously indicated as the best serving beams, then this is an indication that the prediction model is trusted) . Regarding claims 3 and 14, Kaya discloses: wherein the sending the first information or the second information to the access network device comprises sending the second information to the access network device in response to the first downlink signal and the second downlink signal meeting a second condition, the second condition comprises: the first downlink signal and the second downlink signal are an identical downlink signal; or the first downlink signal and the second downlink signal are not an identical downlink signal, but the difference information between the first downlink signal and the second downlink signal is less than or equal to a first threshold (fig.4 element 440 element 4, par.[0076] which recites, in part, “Alternatively, the serving beam, used by the BS or used by the UE, may be used to determine a past beam sequence for a UE. The serving beams (beams used by a UE or BS for communication) may often be the same as the best reported (e.g., highest RSRP) beams,”. In this way when the beams reported are already or previously indicated as the best serving beams, then this is an indication that the prediction model is trusted). Regarding claim 5 and 16, Kaya discloses: training the first prediction model based on the Y pieces of downlink measurement information and the second downlink signal, to obtain thereby obtaining a second prediction model (par.[0084] and FIG. 8 is a diagram illustrating training of a beam sequence model for prediction (step 2) according to an example embodiment. A beam sequence model 816 is provided and may be trained based on the samples or beam index sequences output or provided from step 1 c , according to an illustrative example embodiment.) ; and sending third information to the access network device, wherein the third information indicates is useable to indicate the second prediction model or indicates information about the second prediction model (par.[0095] which recites “At 1020, the BS may set infrequent periodic beam reporting (with much less frequency than what would be required without the prediction) for each UE to ensure that the prediction is working correctly”) . Regarding claim 6 and 17, Kaya discloses: sending the third information to the access network device in response to variation information between the second prediction model and the first prediction model being greater than or equal to a second threshold (Par.[0098] “Thus, in such a case, the BS may request a new measurement report with the most likely candidate beams, e.g., since the predicted future beam sequence does not provide a clear best beam. Instead of choosing in a greedy way the most likely beam, the neural network (beam sequence model) may output or provide the indices for most probable beams and the BS may trigger measurement reports for the beams that have probabilities above a threshold, i.e., when there is no single predicted beam index with high probability (1028).”. As can be seen when a first beam report and a second beam report (interpreted as prediction models) have a probability greater than a threshold, the system will trigger additional measurement reports to train the prediction model) . Regarding claims 7 and 18, Kaya discloses: wherein the information about the second prediction model comprises the variation information between the second prediction model and the first prediction model (Par.[0098] “Thus, in such a case, the BS may request a new measurement report with the most likely candidate beams, e.g., since the predicted future beam sequence does not provide a clear best beam. Instead of choosing in a greedy way the most likely beam, the neural network (beam sequence model) may output or provide the indices for most probable beams and the BS may trigger measurement reports for the beams that have probabilities above a threshold, i.e., when there is no single predicted beam index with high probability (1028).”. As can be seen when a first beam report and a second beam report (interpreted as prediction models) have a probability greater than a threshold, the system will trigger additional measurement reports to train the prediction model). Regarding claims 8 and 19, Kaya discloses: receiving fourth information from the access network device, wherein the fourth information indicates is useable to indicate information about a third prediction model; and updating the first prediction model to the third prediction model (par.[0059] which recites, in part, “In the context of neural networks, the parameters may be initialized, often with random values, and a training optimizer iteratively updates the parameters, also referred to as weights, of the network to minimize error in the mapping function. In other words, during each round, or step, of iterative training the network updates the values of the parameters so that the values of the parameters eventually converge on the optimal values.”). Regarding claims 9 and 20, Kaya discloses: wherein the third prediction model is obtained by training the first prediction model based on the Y pieces of downlink measurement information and the second downlink signal (par.[0059] which describes optimizing the prediction model, and par.[0073, 0083] describes the downlink measurement information being sent to the network to provide input for prediction of beams) . Regarding claim 10, Kaya discloses: wherein the first prediction model is a same model as a prediction model configured in the access network device (par.[0067 – 0068] which recites, in part, “These predictions may be performed using artificial intelligence (AI) algorithms running on a BS or gNB, for example”) . Regarding claim 11, Kaya discloses: wherein the first prediction model is agreed on in a protocol or is configured by the access network device (par.[0067 – 0068] which recites, in part, “These predictions may be performed using artificial intelligence (AI) algorithms running on a BS or gNB, for example”) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim (s) 2, 4, 13, and 15 , is/are rejected under 35 U.S.C. 103 as being unpatentable over Kaya et al. as discussed in view of claim 1, and further in view of Gao et al. (US 2023/0075088 A1) . Regarding claims 2 and 13, Kaya discloses: wherein the sending the first information or the second information to the access network device comprises sending the first information to the access network device as discussed in the rejection of claim 1, but may not disclose: in response to the first downlink signal the second downlink signal meeting a first condition, the first condition comprises: difference information between the first downlink signal and the second downlink signal is greater than a first threshold. In an analogous art, the disclosure of Gao teaches: in response to the first downlink signal the second downlink signal meeting a first condition, the first condition comprises: difference information between the first downlink signal and the second downlink signal is greater than a first threshold (par.[0144] which recites, in part, “In some embodiments, the triggering condition may comprise a difference of the channel quality parameter, corresponding to a first RS, over a second RS being more than or equal to a threshold.”) . It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the instant application to combine the teachings of Kaya, with the disclosure of Gao. The motivation/suggestion would have been to allow the UE to send, autonomously to the network the measurement reports. Regarding claims 4 and 15, Gao discloses: wherein the difference information between the first downlink signal and the second downlink signal comprises an absolute value of a difference between first downlink measurement information corresponding to the first downlink signal and second downlink measurement information corresponding to the second downlink signal (as the disclosure of Gao teaches that the report is triggered based on a difference, this would measure or identify the absolute value between a first measured quantity vs a second measured quantity, without determining and compared to a threshold. For example, par.[0171] which recites, in part, “the difference between the channel quality parameter of the RS to be reported and the channel quality parameter of the initial RS is more than an offset value”. This language indicates an absolute value or absolute difference between a first value and second value) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Soldati et al. (US 2024/0204910 A1) “Inter-Node Exchange of Link Adaptation Assistance Information” Hyde et al. (US 2024/0298225 A1) “Using AI-Based Models for Network Energy Savings” Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMAAL HENSON whose telephone number is (571)272-5339. The examiner can normally be reached M-Thu: 7:30 am - 6:30 pm. 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, Derrick Ferris can be reached at (571)272-3123. 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. JAMAAL HENSON Primary Examiner Art Unit 2411 /JAMAAL HENSON/Primary Examiner, Art Unit 2411 Application/Control Number: 18/672,072 Page 2 Art Unit: 2411 Application/Control Number: 18/672,072 Page 3 Art Unit: 2411 Application/Control Number: 18/672,072 Page 4 Art Unit: 2411 Application/Control Number: 18/672,072 Page 5 Art Unit: 2411 Application/Control Number: 18/672,072 Page 6 Art Unit: 2411 Application/Control Number: 18/672,072 Page 7 Art Unit: 2411 Application/Control Number: 18/672,072 Page 8 Art Unit: 2411 Application/Control Number: 18/672,072 Page 9 Art Unit: 2411 Application/Control Number: 18/672,072 Page 10 Art Unit: 2411 Application/Control Number: 18/672,072 Page 11 Art Unit: 2411 Application/Control Number: 18/672,072 Page 12 Art Unit: 2411 Application/Control Number: 18/672,072 Page 13 Art Unit: 2411
