CTNF 18/636,682 CTNF 82210 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 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 – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-4, 7-9, 12, 13, 15, 28 are rejected under 35 U.S.C. 102( a)(1 ) as being unpatentable by Vahdat et al. (U.S. Patent Application Number: 2020/0057961) . Consider claim 1 ; Vahdat discloses an apparatus configured for wireless communications, comprising: one or more memories (par. 37, lines 1-10); and one or more processors coupled to the one or more memories (par. 37, lines 1-10), the one or more processors being configured to cause the apparatus (par. 37, lines 1-10) to: obtain a first configuration that indicates to predict at least one channel property (e.g. channel gain) (par. 50, lines 1-11), based at least in part on perception information [based on par. 35 in the specification this can be device location (par. 169, lines 1-13)], using a first machine learning (ML) model (par. 50, lines 1-11); communicate (par. 49, lines 1-12), via at least one communication channel (par. 49, lines 1-12), based at least in part on a prediction of one or more channel properties associated with the at least one communication channel (par. 50, lines 1-11), wherein the prediction of the one or more channel properties is obtained via the first ML model (par. 50, lines 1-11); and send an indication of one or more performance metrics (par. 75, lines 1-11) associated with predicting the one or more channel properties via the first ML model (par. 50, lines 1-11). Consider claim 2 ; Vahdat discloses obtain one or more reference signals [e.g. optical signals (par. 31, lines 1-9)]; and to send the indication of the one or more performance metrics (par. 75, lines 1-11), the one or more processors (par. 37, lines 1-10) are configured to cause the apparatus (e.g. amplifier) to send the indication of the one or more performance metrics (par. 75, lines 1-11) based at least in part on one or more measurements of the one or more reference signals [e.g. optical signals (par. 31, lines 1-9)]. Consider claim 3 ; Vahdat discloses to send the indication of the one or more performance metrics (par. 75, lines 1-11), the one or more processors (par. 37, lines 1-10) are configured to cause the apparatus (e.g. amplifier) to send the indication of the one or more performance metrics (par. 75, lines 1-11) based at least in part on a comparison between the one or more measurements of the one or more reference signals (e.g. input signals such as optical signals) and the prediction of the one or more channel properties (e.g. channel gain) (par. 44, lines 4-14; par. 50, lines 1-11; par. 54, lines 1-7). Consider claim 4 ; Vahdat discloses obtain a second configuration that indicates to report the one or more performance metrics (par. 75, lines 1-11) associated with the first ML model [e.g. the report leads to further ML training (par. 76, line 1 – par. 77, line 5)]. Consider claim 7 ; Vahdat discloses obtain a second configuration that indicates one or more states that trigger communication of training data associated with the first ML model (par. 49, lines 1-12; par. 54, lines 1-7). Consider claim 8 ; Vahdat discloses obtain one or more reference signals [e.g. optical signals (par. 31, lines 1-9)]; and send training data associated with the first ML model in response to at least one state of the one or more states being detected (par. 49, lines 1-12; par. 54, lines 1-7), the training data being based at least in part on one or more measurements of the one or more reference signals [e.g. input signals such as optical signals (par. 44, lines 4-14; par. 50, lines 1-11; par. 54, lines 1-7)]. Consider claim 9 ; Vahdat discloses obtain a second ML model trained based on the training data [e.g. further ML training (par. 76, line 1 – par. 77, line 9)]; and obtain an indication to predict the at least one channel property [e.g. channel gain (par. 50, lines 1-11)], based at least in part on the perception information [based on par. 35 in the specification this can be device location (par. 169, lines 1-13)], using the second ML model (par. 57, lines 22-28; par. 76, line 1 – par. 77, line 9). Consider claim 12 ; Vahdat discloses send the indication of the one or more performance metrics (par. 77, lines 1-10), the one or more processors (par. 37, lines 1-10) are configured to cause the apparatus (e.g. amplifier) to send the indication of the one or more performance metrics (par. 77, lines 1-10; par. 78, lines 3-4) based at least in part on the prediction of the one or more channel properties (e.g. channel gain) satisfying a threshold (par. 50, lines 1-11; par. 77, lines 1-10). Consider claim 13 ; Vahdat discloses provide, to the first ML model (par. 50, lines 1-11), input data comprising the perception information [based on par. 35 in the specification this can be device location (par. 49, lines 1-12; par. 169, lines 1-13)]; and obtain (par. 50, lines 1-11), from the first ML model (par. 50, lines 1-11), output data comprising the prediction of the one or more channel properties (e.g. channel gain) associated with the at least one communication channel (par. 50, lines 1-11). Consider claim 15 ; Vahdat discloses send training data associated with the first ML model (par. 50, lines 1-11), wherein the training data comprises one or more of: the perception information, an indication of a channel property of a communication channel (e.g. channel loading) (par. 50, lines 1-11), or translation information for the perception information; and obtain the first ML model trained based on the training data (par. 49, line 1 – par. 50, line 11; par. 54, lines 1-7). Consider claim 28 ; Vahdat discloses a method for wireless communications by an apparatus, comprising: obtain a first configuration that indicates to predict at least one channel property (e.g. channel gain) (par. 50, lines 1-11), based at least in part on perception information [based on par. 35 in the specification this can be device location (par. 169, lines 1-13)], using a first machine learning (ML) model (par. 50, lines 1-11); communicate (par. 49, lines 1-12), via at least one communication channel (par. 49, lines 1-12), based at least in part on a prediction of one or more channel properties associated with the at least one communication channel (par. 50, lines 1-11), wherein the prediction of the one or more channel properties is obtained via the first ML model (par. 50, lines 1-11); and send an indication of one or more performance metrics (par. 75, lines 1-11) associated with predicting the one or more channel properties via the first ML model (par. 50, lines 1-11) . 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 of this title, 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 5, 6, 10, 11, 14, 16-27, 29 are rejected under 35 U.S.C. 103 as being unpatentable over Vahdat et al. (U.S. Patent Application Number: 2020/0057961) in view of Esswie (U.S. Patent Application Number: 2024/0430859) . Consider claim 5, as applied in claim 1 ; Vahdat discloses the claimed invention except: obtain a second configuration that indicates one or more states that trigger deactivation of the first ML model. In an analogous art Esswie discloses obtaining a second configuration that indicates one or more states that trigger deactivation of the first ML model [e.g. real-time performance metrics (par. 56, lines 3-9)]. It is an object of Vahdat’s invention to provide a method of training a machine learning model. It is an object of Esswie’s invention to provide a method of managing AI/ML models. Therefore, 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 teaching of Vahdat by including a deactivation, as taught by Esswie, for the purpose of effectively managing a telecommunications network. Consider claim 6, as applied in claim 5 ; Esswie discloses deactivate the first ML model in response to at least one state of the one or more states being detected [e.g. real-time performance metrics (par. 56, lines 3-9)]. Consider claim 10, as applied in claim 1 ; Vahdat discloses the claimed invention except: obtain a second configuration that indicates one or more states that trigger communication of an indication that the first ML model is incompatible with an environment in which the apparatus is positioned. In an analogous art Esswie discloses obtaining a second configuration (e.g. deactivation) that indicates one or more states that trigger communication of an indication that the first ML model is incompatible with an environment in which the apparatus is positioned (e.g. no location information) (par. 56, lines 3-9; par. 95, lines 1-5; par. 102, lines 5-15). It is an object of Vahdat’s invention to provide a method of training a machine learning model. It is an object of Esswie’s invention to provide a method of managing AI/ML models. Therefore, 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 teaching of Vahdat by including a deactivation, as taught by Esswie, for the purpose of effectively managing a telecommunications network. Consider claim 11, as applied in claim 10 ; Esswie discloses send the indication that the first ML model is incompatible with the environment in response to the one or more states being detected (e.g. no location information) (par. 56, lines 3-9; par. 95, lines 1-5; par. 102, lines 5-15); and obtain an indication to deactivate the first ML model (par. 56, lines 3-9). Consider claim 14, as applied in claim 13 ; Vahdat discloses the claimed invention except: search for the at least one communication channel among a plurality of communication channels based at least in part on the output data. In an analogous art Esswie discloses search for the at least one communication channel among a plurality of communication channels (par. 73, lines 12-21) based at least in part on the output data (par. 56, lines 3-9). It is an object of Vahdat’s invention to provide a method of training a machine learning model. It is an object of Esswie’s invention to provide a method of managing AI/ML models. Therefore, 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 teaching of Vahdat by including a plurality of channels, as taught by Esswie, for the purpose of efficiently providing services in a wireless system. Consider claim 16 ; Vahdat discloses predict at least one channel property (e.g. channel gain) (par. 50, lines 1-11), based at least in part on perception information [based on par. 35 in the specification this can be device location (par. 169, lines 1-13)], using a first machine learning (ML) model (par. 50, lines 1-11); based at least in part on a prediction of one or more channel properties associated with the at least one communication channel (par. 50, lines 1-11), wherein the prediction of the one or more channel properties is obtained via the first ML model (par. 50, lines 1-11); and predicting the one or more channel properties via the first ML model (par. 50, lines 1-11). Vahdat discloses the claimed invention except: an apparatus configured for wireless communications, comprising: one or more memories; and one or more processors coupled to the one or more memories, the one or more processors being configured to cause the apparatus to: send a first configuration; communicate with a user equipment, via at least one communication channel, obtain an indication of one or more performance metrics. In an analogous art Esswie discloses an apparatus (e.g. network RAN) configured for wireless communications (par. 56, lines 3-9), comprising: one or more memories (par. 146); and one or more processors coupled to the one or more memories (par. 145, line 4 – par. 146, line 6), the one or more processors being configured to cause the apparatus (par. 145, line 4 – par. 146, line 6) to: send a first configuration (par. 56, lines 3-9; par. 95, lines 1-5); communicate with a user equipment (par. 56, lines 3-9), via at least one communication channel (par. 94, lines 18-25), obtain an indication of one or more performance metrics (par. 56 lines 3-14). It is an object of Vahdat’s invention to provide a method of training a machine learning model. It is an object of Esswie’s invention to provide a method of managing AI/ML models. Therefore, 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 teaching of Vahdat by including communicating with a user equipment, as taught by Esswie, for the purpose of efficiently providing services in a wireless network. Consider claim 17, as applied in claim 16 ; Esswie discloses send one or more reference signals (par. 92, lines 3-9); and to obtain the indication of the one or more performance metrics (par. 56 lines 3-14), the one or more processors (par. 145, line 4 – par. 146, line 6) are configured to cause the apparatus (e.g. network RAN) to obtain the indication of the one or more performance metrics (par. 56 lines 3-14) based at least in part on one or more measurements of the one or more reference signals (par. 95, lines 1-5). Consider claim 18, as applied in claim 17 ; Esswie discloses obtain the indication of the one or more performance metrics (par. 56 lines 3-14), the one or more processors (par. 145, line 4 – par. 146, line 6) are configured to cause the apparatus (e.g. network RAN) to obtain the indication of the one or more performance metrics (par. 56 lines 3-14) based at least in part on a comparison between the one or more measurements of the one or more reference signals and the prediction of the one or more channel properties (par. 119, lines 7-10). Consider claim 19, as applied in claim 16 ; Esswie discloses send a second configuration that indicates to report the one or more performance metrics associated with the first ML model (par. 56 lines 3-14; par. 95, lines 1-5). Consider claim 20, as applied in claim 16 ; Esswie discloses sending a second configuration that indicates one or more states that trigger deactivation of the first ML model [e.g. real-time performance metrics (par. 56, lines 3-9)]. Consider claim 21, as applied in claim 16 ; Esswie discloses send a second configuration that indicates one or more states that trigger communication of training data associated with the first ML model (par. 56 lines 3-14; par. 95, lines 1-5). Consider claim 22, as applied in claim 21 ; Esswie discloses send one or more reference signals (par. 92, lines 3-9); and obtain training data associated with the first ML model based on the second configuration (par. 56 lines 3-14; par. 95, lines 1-5), the training data being based at least in part on one or more measurements of the one or more reference signals (par. 95, lines 1-5). Consider claim 23, as applied in claim 22 ; Vahdat discloses predict the at least one channel property [e.g. channel gain (par. 50, lines 1-11)], based at least in part on the perception information [based on par. 35 in the specification this can be device location (par. 169, lines 1-13)], using the second ML model (par. 57, lines 22-28; par. 76, line 1 – par. 77, line 9). Vahdat discloses the claimed invention except: send a second ML model trained based on the training data. In an analogous art Esswie discloses send a second ML model trained based on the training data (par. 56 lines 1-9; par. 95, lines 1-5). It is an object of Vahdat’s invention to provide a method of training a machine learning model. It is an object of Esswie’s invention to provide a method of managing AI/ML models. Therefore, 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 teaching of Vahdat by including communicating with a user equipment, as taught by Esswie, for the purpose of efficiently providing services in a wireless network. Consider claim 24, as applied in claim 16 ; Esswie discloses sending a second configuration (e.g. deactivation) that indicates one or more states that trigger communication of an indication that the first ML model is incompatible with an environment in which the apparatus is positioned (e.g. no location information) (par. 56, lines 3-9; par. 95, lines 1-5; par. 102, lines 5-15). Consider claim 25, as applied in claim 24 ; Esswie discloses obtain the indication that the first ML model is incompatible with the environment based on the second configuration (par. 56, lines 3-9; par. 95, lines 1-5; par. 102, lines 5-15); and send an indication to deactivate the first ML model (par. 56, lines 3-9). Consider claim 26, as applied in claim 16 ; Vahdat discloses send the indication of the one or more performance metrics (par. 77, lines 1-10), the one or more processors (par. 37, lines 1-10) are configured to cause the apparatus (e.g. amplifier) to obtain the indication of the one or more performance metrics (par. 77, lines 1-10; par. 78, lines 3-4) based at least in part on the prediction of the one or more channel properties (e.g. channel gain) satisfying a threshold (par. 50, lines 1-11; par. 77, lines 1-10). Consider claim 27, as applied in claim 16 ; Vahdat discloses obtain training data associated with the first ML model (par. 50, lines 1-11; par. 78, lines 3-4), wherein the training data comprises one or more of: the perception information, an indication of a channel property of a communication channel (e.g. channel loading) (par. 50, lines 1-11), or translation information for the perception information. Vahdat discloses the claimed invention except: send the first ML model trained based on the training data. In an analogous art Esswie discloses send the first ML model trained based on the training data (par. 56, lines 3-9). It is an object of Vahdat’s invention to provide a method of training a machine learning model. It is an object of Esswie’s invention to provide a method of managing AI/ML models. Therefore, 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 teaching of Vahdat by including communicating with a user equipment, as taught by Esswie, for the purpose of efficiently providing services in a wireless network. Consider claim 29 ; Vahdat discloses a method for wireless communications by an apparatus, comprising: predict at least one channel property (e.g. channel gain) (par. 50, lines 1-11), based at least in part on perception information [based on par. 35 in the specification this can be device location (par. 169, lines 1-13)], using a first machine learning (ML) model (par. 50, lines 1-11); based at least in part on a prediction of one or more channel properties associated with the at least one communication channel (par. 50, lines 1-11), wherein the prediction of the one or more channel properties is obtained via the first ML model (par. 50, lines 1-11); and predicting the one or more channel properties via the first ML model (par. 50, lines 1-11). Vahdat discloses the claimed invention except: send a first configuration; communicate with a user equipment, via at least one communication channel, obtain an indication of one or more performance metrics. In an analogous art Esswie discloses send a first configuration (par. 56, lines 3-9; par. 95, lines 1-5); communicate with a user equipment (par. 56, lines 3-9), via at least one communication channel (par. 94, lines 18-25), obtain an indication of one or more performance metrics (par. 56 lines 3-14). It is an object of Vahdat’s invention to provide a method of training a machine learning model. It is an object of Esswie’s invention to provide a method of managing AI/ML models. Therefore, 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 teaching of Vahdat by including communicating with a user equipment, as taught by Esswie, for the purpose of efficiently providing services in a wireless network. Conclusion Any response to this Office Action should be faxed to (571) 273-8300 or mailed to : Commissioner for Patents P.O. Box 1450 Alexandria, VA 22313-1450 Hand-delivered responses should be brought to Customer Service Window Randolph Building 401 Dulany Street Alexandria, VA 22314 Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Joel Ajayi whose telephone number is (571) 270-1091. The Examiner can normally be reached on Monday-Friday from 7:30am to 5:00pm. If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Matthew Anderson can be reached on (571) 272-4177. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free) or 703-305-3028. Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to the receptionist/customer service whose telephone number is (571) 272-2600. /JOEL AJAYI/ Primary Examiner, Art Unit 2646 Application/Control Number: 18/636,682 Page 2 Art Unit: 2646 Application/Control Number: 18/636,682 Page 3 Art Unit: 2646 Application/Control Number: 18/636,682 Page 4 Art Unit: 2646 Application/Control Number: 18/636,682 Page 5 Art Unit: 2646 Application/Control Number: 18/636,682 Page 6 Art Unit: 2646 Application/Control Number: 18/636,682 Page 7 Art Unit: 2646 Application/Control Number: 18/636,682 Page 8 Art Unit: 2646 Application/Control Number: 18/636,682 Page 9 Art Unit: 2646 Application/Control Number: 18/636,682 Page 10 Art Unit: 2646 Application/Control Number: 18/636,682 Page 11 Art Unit: 2646 Application/Control Number: 18/636,682 Page 12 Art Unit: 2646 Application/Control Number: 18/636,682 Page 13 Art Unit: 2646
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