CTNF 18/761,656 CTNF 72330 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 07-07-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, 8-9, 12-14, 18-22 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bhamri (2023/0198604) . Regarding claim 1, Bhamri discloses an apparatus (Figure 17, Base station 102) comprising at least one processor (1706) and at least one memory (1708) storing instructions that when executed by the at least one processor, cause the apparatus to: determine a mode for beam management, based on a configuration that indicates the mode for beam management (para 29, BS communications a beam management instruction as well as para 64, bit to indicate beam management mode). Bhamri discloses switch to the mode for beam management, and perform at least one measurement related to AI or ML beam management, while operating in the mode (para 30-31, instructing UE to perform beam management measurements including AI algorithm, and 64-66, use of signaling to denote the process). Bhamri discloses transmitting the at least one measurement to a test equipment or a network (para 32, the UE communicates the measured information to the base station in the network). Regarding claim 21, Bhamri discloses an apparatus (Figure 17, Base station 102) comprising at least one processor (1706) and at least one memory (1708) storing instructions that when executed by the at least one processor, cause the apparatus to: determine a configuration that indicates the mode for beam management (para 29, BS communications a beam management instruction as well as para 64, bit to indicate beam management mode). Bhamri discloses receiving from a user equipment (UE 104), at least one measurement related to AI or ML beam management, based on the mode for beams management ((para 30-31, instructing UE to perform beam management measurements including AI algorithm, and 64-66, use of signaling to denote the process as well as para 32, the UE communicates the measured information to the base station in the network). Bhamri discloses selecting an accuracy target for the mode for beam management (para 78, 9,1 and 143, the AI parameters can indicate a minimum accuracy of beam information) and determine whether the accuracy target is satisfied based on the measurement (para 78 – the accuracy is based on confidence levels after run through the AI algorithm – in particular, note para 113 – AI model will generate a beam which is inferred to meet the best communication channel, based on configuration of the algorithm upon the received data, Figure 20, operations 2004 and Figure 21, operations 2102/2104). Regarding claim 22, Bhamri discloses a method comprising: determining a mode for beam management, based on a configuration that indicates the mode for beam management (para 29, BS communications a beam management instruction as well as para 64, bit to indicate beam management mode). Bhamri discloses switching to the mode for beam management, and performing at least one measurement related to AI or ML beam management, while operating in the mode (para 30-31, instructing UE to perform beam management measurements including AI algorithm, and 64-66, use of signaling to denote the process). Bhamri discloses transmitting the at least one measurement to a test equipment or a network (para 32, the UE communicates the measured information to the base station in the network). Regarding claims 2-3, Bhamri discloses wherein the mode comprises one of an AI/ML mode or a legacy mode (para 64, AI-beam management field 204 indicates AI mode or legacy/non-AI enabled mode) and that the AI/ML mode comprises at least one of: spatial received power prediction (para 73, 82-83, 89-90, which beams provide the best RSRP) as well as downlink transmit receive beam pairs prediction (para 111-112, the best two beam pairs are selected based on RSRP) . Regarding claims 8-9, Bhamri discloses wherein the at least one measurement is used as input for beam prediction using AI or ML (para 77-78, inferring beam information (i.e. prediction) and a measurement is a layer 1 RSRP measurement (para 73, 83, 89-90). Regarding claim 12, Bhamri discloses the instructions cause the apparatus to transmit to the test equipment or network, an indication that the apparatus ins operating in the mode for beam management (Para 63-70, use of master information block/system information block signaling to denote AI beam management being performed, also para 108, msg3 using PUCCH/PUSCH control messages). Regarding claims 13-14, 20, Bhamri discloses wherein the at least one measurement satisfies a measurement accuracy requirement (para 78 – a minimum accuracy/confidence level can be designated as related to measurements for the AI model) and the requirement comprises a value (in this case, 90% accuracy/confidence interval). Regarding claims 18-19, Bhamri discloses the instructions cause the apparatus to train an AI/ML model from beam management based on the at least one measurement (Figure 20, receiving measurement information and configuring operation of the AI algorithm, i.e. training). And receiving an indication that the at least one measurement satisfies a measurement accuracy requirement (para 78 – a minimum accuracy/confidence level can be designated as related to measurements for the AI model) Claim Rejections - 35 USC § 103 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. 07-21-aia AIA Claim (s) 4-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhamri in view of Zhu (2023/0075276) . Regarding claims 4-5, Bhamri discloses all the particular of the claim except for a command to switch to an AI/ML mode. Bhamri discloses that the network can command the UE to use an AI/ML mode if capable. However, Zhu teaches in an analogous art, a network signaling process (Figure 4) in which a command to switch machine learning models is sent between a network and user equipment (para 84, can exchange capabilities with the network and UE and network based signaling of ML models, as well as para 86/87 – upon a change, the network can switch the ML model the UE is processing). Zhu also teaches that the instructions detect a change in radio environment (which cell the UE is in, para 86) and determine the mode based on the change in radio conditions (select the appropriate ML model for the conditions). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to switch ML models by network command, as taught by Zhu, to the system of Bhamri in order to allow flexibility in using multiple models depending on communications circumstances. Regarding claims 6-7, Bhamri discloses that the change in radio conditions allows for performance improvement when utilizing the AI/ML mode (para 4- by utilizing AI-enabled beam management, the UE can experience reduced latency and overhead and increase signal quality which is a performance improvement) as well as the use of legacy modes (non-AI/ML capable UE, para 64) . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 10-11 and 15-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. 13-03-01 AIA The following is a statement of reasons for the indication of allowable subject matter: the claimed invention is directed to the use of an Artificial intelligence/machine learning model for beamforming or beam management. While the cited prior art of record (Bhamri, Zhu, Laddu) discloses the use of AI/ML models to optimize beam management, the prior art does not disclose nor fairly suggest where a measurement accuracy is satisfied when "an error associated with the measurement is within an AI/ML mode error bound or a layer 1 reference signal received power (L1-RSRP) measurement error bound" with respect to claims 10-11 and "the measurement accuracy requirements is satisfied for an AI/ML mode when the measurement is within a first tolerance value and a legacy mode for beam management when within a second tolerance value." . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Laddu (2025/0266891) discloses the use of machine learning models with beam management Taherzadeh (WO 2022/211931) discloses beam forming based on UE measurements. Marini (WO 2019/130267) discloses test measurements performed in a beam management mode where simulation is compared with actual RSRP signal levels from communication devices. Kotecha (2021/0368393) discloses models which predict the best quality of service in a beam communications network. Bai (12395881) discloses machine learning for interference optimization in a wireless network. Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM GEORGE TROST IV whose telephone number is (571)272-7872. The examiner can normally be reached Monday-Thursday 7a-4p, Fridays 7a-2p. 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, Charles Appiah can be reached at 571-272-7904. 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. WILLIAM GEORGE TROST IV Primary Patent Examiner Art Unit 2641 /WILLIAM G TROST IV/Primary Patent Examiner, Art Unit 2641 Application/Control Number: 18/761,656 Page 2 Art Unit: 2641 Application/Control Number: 18/761,656 Page 3 Art Unit: 2641 Application/Control Number: 18/761,656 Page 4 Art Unit: 2641 Application/Control Number: 18/761,656 Page 5 Art Unit: 2641 Application/Control Number: 18/761,656 Page 6 Art Unit: 2641 Application/Control Number: 18/761,656 Page 7 Art Unit: 2641
Prosecution Insights