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 .
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.
This action is responsive to the Remark filed on 12/22/25.
Claims 1, 16, 25 & 27 are amended. Claims 12, 28 & 30 are canceled.
Claim(s) 1-11, 13-27, 29, 31-33 is/are presented for examination.
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 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 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.
Claim(s) 1-9, 11, 13-19, 21-27, 29, 31-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Soldati, U.S. Pub/Patent No. US 2023/0276264 A1 in view of Aftab, US 2019/0325353 A1.
As to claim 1, Soldati teaches an apparatus for wireless communication, including:
a memory; and
at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to:
receive, at the first wireless device, a transmission that indicates a configuration for data collection and reporting of the first wireless device that includes a data validation that is specific to one or more of a model identifier (ID), a machine learning function, or a machine learning use case (Soldati, figure 9c; page 8, paragraph 92; page 12, paragraph 178 & 181; i.e., [0092] the supplementary ML model information may comprise an identification 924, for a RAN operation, of an ML model that the wireless device. ML model information may additionally or alternatively comprise an identification 925 of information reported by the wireless device. [0178] Examples of wireless device reports that may include an identification of ML based information include reports associated with radio resource management. [0181] The model ID could also indicate the operator or network vendor that generated the model);
collect data at the first wireless device based on the configuration (Soldati, figure 9c; page 7, paragraph 82; page 9, paragraph 113; i.e., [0082] The acknowledgement may for example include an identification of information relating to execution of ML models by wireless devices that the second RAN node is operable to provide, and/or an identification of information requested in the first request and that the second RAN node is unable to provide. For example, if the second RAN node is unable to provide all of the information requested by the first RAN node, the acknowledgement of the first request may include an identification or specification of the information that the second RAN node is able to provide; [0113] The maximum supported computational cost/load can also be associated to a particular type of model. This may enable a RAN node to select the most appropriate model (type, dimension, etc.) for a specific UE based on the UE capabilities);
validate, at the first wireless device, the collected data to determine if the collected data meets the criteria indicated in the configuration for the data validation that is specific to the model ID, the machine learning function, or the machine learning use case (Soldati, figure 9c; page 7, paragraph 82; page 9, paragraph 113; i.e., [0082] The acknowledgement may for example include an identification of information relating to execution of ML models by wireless devices that the second RAN node is operable to provide, and/or an identification of information requested in the first request and that the second RAN node is unable to provide. the acknowledgement of the first request may include an identification or specification of the information that the second RAN node is able to provide; [0113] The maximum supported computational cost/load can also be associated to a particular type of model. This may enable a RAN node to select the most appropriate model (type, dimension, etc.) for a specific UE based on the UE capabilities); and
report, via wireless communications to the second wireless device, the collected data based on a determination that the collected data meets the criteria of the configuration for the data validation associated with the model ID, the machine learning function, or the machine learning use case (Soldati, figure 9c; page 7, paragraph 80; page 11, paragraph 138-143; i.e., [0080] A range of additional elements may be included in the first request, including for example information about start time, validity time, periodicity and initiating condition for provision of information, an identifier of one or more wireless devices that are the subject of the request; [0138] A starting time for reporting ML/AI assistance information; [0139] A validity time and/or a time window for reporting ML/ AI assistance information; [0140] A periodicity for reporting ML/AI assistance information; [0142] One or more performance criteria or conditions that the second RAN node can use to initiate the transmission of ML/AI assistance information reporting. For instance, the identification of a wireless device served by the second RAN node, which uses or can be configured to use ML/AI models or algorithms for executing one or more network operations. [0143] An identifier for one or more cells of the second RAN node to which the ML/AI).
But Soldati failed to teach the claim limitation wherein the configuration is to determine, as part of the data validation at the first wireless device, if collected data meets a criteria to be included as input for model training or inference at a second wireless device, and wherein the criteria for the data validation is based on the model ID, the machine learning function, or the machine learning use case.
However, Aftab teaches the limitation the configuration is to determine, as part of the data validation at the first wireless device, if collected data meets a criteria to be included as input for model training or inference at a second wireless device, and wherein the criteria for the data validation is based on the model ID, the machine learning function, or the machine learning use case (Aftab, page 1, paragraph 10; page 8, paragraph 50-55; i.e., [0010] the first machine learning model to a containerized environment, validate the first machine learning model according to at least one validation criteria associated with a repository; [0050] the processing system validates the first machine learning model according to at least one validation criterion associated with a repository. For instance, the at least one validation criterion associated with repository can be one or more validation criteria associated with the containerized environment and/or one or more validation criteria imposed by the repository provider; [0055] the at least a first artifact defines a compatibility of at least one of an input port or an output port of the first machine learning model with at least one of an input port or an output port of the second process).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Soldati to substitute analyze large volumes of data to deliver various insights, key performance indicators, and
other actionable information (Aftab, page 1, paragraph 2).
As to claim 2, Soldati-Aftab teaches the apparatus as recited in claim 1, wherein a transceiver coupled to the at least one processor:
As to claim 3, Soldati-Aftab teaches the apparatus as recited in claim 2, wherein the apparatus is for the wireless communication at a user equipment (UE), and the at least one processor is configured to receive the configuration from a network node and report the data collected a the UE to the network node (Soldati, figure 9c; page 4, paragraph 43; page 11, paragraph 138-143; i.e., [0043] the UE could be configured with an ML model; [0138] A starting time for reporting ML/AI assistance information; [0139] A validity time and/or a time window for reporting ML/ AI assistance information; [0140] A periodicity for reporting ML/AI assistance information; [0142] One or more performance criteria or conditions that the second RAN node can use to initiate the transmission of ML/AI assistance information reporting. For instance, the identification of a wireless device served by the second RAN node, which uses or can be configured to use ML/AI models or algorithms for executing one or more network operations. [0143] An identifier for one or more cells of the second RAN node to which the ML/AI).
As to claim 4, Soldati-Aftab teaches the apparatus as recited in claim 2, wherein the apparatus is for the wireless communication at a network node, and the at least one processor is configured to receive the configuration from a user equipment (UE) and report the data to the UE (Soldati, figure 9c; page 11, paragraph 138-143; i.e., [0138] A starting time for reporting ML/AI assistance information; [0139] A validity time and/or a time window for reporting ML/ AI assistance information; [0140] A periodicity for reporting ML/AI assistance information; [0142] One or more performance criteria or conditions that the second RAN node can use to initiate the transmission of ML/AI assistance information reporting. For instance, the identification of a wireless device served by the second RAN node, which uses or can be configured to use ML/AI models or algorithms for executing one or more network operations. [0143] An identifier for one or more cells of the second RAN node to which the ML/AI).
As to claim 5, Soldati-Aftab teaches the apparatus as recited in claim 2, wherein the apparatus is for the wireless communication at a first network node, and the at least one processor is configured to receive the configuration from a second network node and report the data to the second network node (Soldati, figure 9c; page 11, paragraph 130; i.e., [0130] the first RAN node may transmit, to a second RAN node, an ML/AI assistance information REQUEST. The first RAN node then receives from the second RAN node, an ML/AI assistance information report).
As to claim 6, Soldati-Aftab teaches the apparatus as recited in claim 2, wherein the apparatus is for the wireless communication at a first user equipment (UE), and the at least one processor is configured to receive the configuration from a second UE and report the data collected a the first UE to the second UE (Soldati, figure 9c; page 11, paragraph 130; i.e., [0130] the first RAN node may transmit, to a second RAN node, an ML/AI assistance information REQUEST. The first RAN node then receives from the second RAN node, an ML/AI assistance information report).
As to claim 7, Soldati-Aftab teaches the apparatus as recited in claim 1, wherein the configuration for the data validation associated with the model ID, the machine learning function, or the machine learning use case indicates one or more of: a data reporting method, at least one input parameter for the machine learning, unprocessed data to obtain model input parameters, at least one measurement to obtain the model input parameters, at least one data processing module (Soldati, figure 9c; page 11, paragraph 138-143; i.e., [0138] A starting time for reporting ML/AI assistance information; [0139] A validity time and/or a time window for reporting ML/ AI assistance information; [0140] A periodicity for reporting ML/AI assistance information; [0142] One or more performance criteria or conditions that the second RAN node can use to initiate the transmission of ML/AI assistance information reporting. For instance, the identification of a wireless device served by the second RAN node, which uses or can be configured to use ML/AI models or algorithms for executing one or more network operations. [0143] An identifier for one or more cells of the second RAN node to which the ML/AI).
As to claim 8, Soldati-Aftab teaches the apparatus as recited in claim 1, wherein report different data based on multiple configurations, each configuration associated with a different the model ID, a different machine learning function, or a different machine learning use case (Soldati, figure 9c; page 9, paragraph 114; i.e., [0114] the capabilities may include an indication about the number of different ML models with which the wireless device can be configured simultaneously).
As to claim 9, Soldati-Aftab teaches the apparatus as recited in claim 1, wherein receive a data reporting activation of the configuration received for the model ID, the machine learning function, or the machine learning use case and to report the data in response to the data reporting activation of the configuration (Soldati, figure 9c; page 6, paragraph 79; page 11, paragraph 138-143; i.e., [0079] Referring first to FIG. 9a, in a first step 902, the first RAN node detects occurrence of a trigger condition. The trigger condition may comprise at least one of a request from the wireless device to connect to the network; [0138] A starting time for reporting ML/AI assistance information; [0139] A validity time and/or a time window for reporting ML/ AI assistance information; [0140] A periodicity for reporting ML/AI assistance information; [0142] One or more performance criteria or conditions that the second RAN node can use to initiate the transmission of ML/AI assistance information reporting. For instance, the identification of a wireless device served by the second RAN node, which uses or can be configured to use ML/AI models or algorithms for executing one or more network operations. [0143] An identifier for one or more cells of the second RAN node to which the ML/AI).
As to claim 11, Soldati-Aftab teaches the apparatus as recited in claim 1, wherein the at least one processor is configured to report the data in at least one of a radio resource control (RRC) message, a medium access control-control element (MAC-CE), uplink control information (UCI) or downlink control information (DCI) (Soldati, figure 9c; page 3, paragraph 34; i.e., [0034] Radio Resource Control (RRC) connection setup,).
As to claim 13, Soldati-Aftab teaches the apparatus as recited in claim 1, wherein the configuration for the data validation includes one or more of: at least one rule for data validation associated with the model ID, the machine learning function, or the machine learning use case, at least one data statistic associated with the model ID, the machine learning function, or the machine learning use case, or at least one data property associated with the model ID, the machine learning function, or the machine learning use case (Soldati, figure 9c; page 11, paragraph 138-143; i.e., [0138] A starting time for reporting ML/AI assistance information; [0139] A validity time and/or a time window for reporting ML/ AI assistance information; [0140] A periodicity for reporting ML/AI assistance information; [0142] One or more performance criteria or conditions that the second RAN node can use to initiate the transmission of ML/AI assistance information reporting. For instance, the identification of a wireless device served by the second RAN node, which uses or can be configured to use ML/AI models or algorithms for executing one or more network operations. [0143] An identifier for one or more cells of the second RAN node to which the ML/AI).
As to claim 14, Soldati-Aftab teaches the apparatus as recited in claim 1, wherein the at least one processor is further configured to:
identify at least one of an inference or training output based on the machine learning that does not meet the criteria of the configuration for the data validation associated with the model ID, the machine learning function, or the machine learning use case (Soldati, figure 9c; page 6, paragraph 79; page 11, paragraph 132; i.e., [0079] UE is reporting inaccurate reference signal measurements generated by an outdated or invalid ML model. In step 904, the first RAN node sends a first request to a second RAN node in the
communication network; [0132] performance degradation includes UE beamforming gain that fails to match the beamforming gain of other UEs served by the first node. This could arise if for example the UE is reporting inaccurate reference signal measurements generated by an outdated or invalid ML model); and
indicate a data validation failure according to the configuration for the model ID, the machine learning function, or the machine learning use case (Soldati, figure 9c; page 6, paragraph 79; page 11, paragraph 132; i.e., [0079] UE is reporting inaccurate reference signal measurements generated by an outdated or invalid ML model. In step 904, the first RAN node sends a first request to a second RAN node in the communication network; [0132] performance degradation includes UE beamforming gain that fails to match the beamforming gain of other UEs served by the first node. This could arise if for example the UE is reporting inaccurate reference signal measurements generated by an outdated or invalid ML model).
As to claim 15, Soldati-Aftab teaches the apparatus as recited in claim 14, wherein an indication of the data validation failure further indicates a transition to a procedure without the machine learning (Soldati, figure 9c; page 15, paragraph 212; i.e., [0212] a RAN node may be able to verify whether the ML model used by a wireless device is valid for radio network operations when a wireless device enters the coverage area of a radio cell controlled by the RAN node, without requesting and receiving such information directly from the wireless device over the air interface. A model may become invalid owing to network changes that are unknown to the device (new base stations, new antenna tilt settings, new bandwidth, new frequencies, etc.)).
As to claim 25, Soldati teaches an apparatus for registering a machine learning model, including:
a memory; and
at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to:
register the machine learning model for collection and reporting of data based on wireless communication (Soldati, figure 9c; page 4, paragraph 127; page 11, paragraph 130-131; i.e., [0127] The method may be applied to a scenario of selecting a desired AI model in a 3GPP network for data analytics. The method may be performed by a data analytics apparatus provided in the present application; [0130] the first RAN node may transmit, to a second RAN node, an ML/AI assistance information REQUEST. The first RAN node then receives from the second RAN node, an ML/AI assistance information report. The request message thus starts an ML/AI assistance information procedure between the first and second RAN nodes Triggering an ML/AI Assistance Information REQUEST; [0131] In one example, a request for ML/AI information may be triggered whenever a UE connects to a communication network. At connection setup, the network can receive a list of radio access nodes (RAN nodes, such as an eNB, gNb etc.) that have served the UE. The list of previous serving nodes can be reported from the UE); and
provide, in association with registration of the machine learning model a data validation scheme for the machine learning model, wherein the data validation scheme is specific to one or more of a model identifier (ID), a machine learning function, or a machine learning use case (Soldati, figure 9c; page 11, paragraph 138-143; i.e., [0138] A starting time for reporting ML/AI assistance information; [0139] A validity time and/or a time window for reporting ML/ AI assistance information; [0140] A periodicity for reporting ML/AI assistance information; [0142] One or more performance criteria or conditions that the second RAN node can use to initiate the transmission of ML/AI assistance information reporting. For instance, the identification of a wireless device served by the second RAN node, which uses or can be configured to use ML/AI models or algorithms for executing one or more network operations. [0143] An identifier for one or more cells of the second RAN node to which the ML/AI).
But Soldati failed to teach the claim limitation wherein provides a criteria for a data validation determination at a first wireless device, about whether to include collected data as input for training or inference at a second wireless device and wherein the criteria for the data validation based on the model ID, the machine learning function, or the machine learning use case.
However, Aftab teaches the limitation the configuration is to determine, as part of the data validation at the first wireless device, if collected data meets a criteria to be included as input for model training or inference at a second wireless device, and wherein the criteria for the data validation is based on the model ID, the machine learning function, or the machine learning use case (Aftab, page 1, paragraph 10; page 8, paragraph 50-55; i.e., [0010] the first machine learning model to a containerized environment, validate the first machine learning model according to at least one validation criteria associated with a repository, and publish the first machine learning model to the repository; [0050] the processing system validates the first machine learning model according to at least one validation criterion associated with a repository. For instance, the at least one validation criterion associated with repository can be one or more validation criteria associated with the containerized environment and/or one or more validation criteria imposed by the repository provider; [0055] the at least a first artifact defines a compatibility of at least one of an input port or an output port of the first machine learning model with at least one of an input port or an output port of the second process).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Soldati to substitute analyze large volumes of data to deliver various insights, key performance indicators, and
other actionable information (Aftab, page 1, paragraph 2).
As to claim 32, Soldati-Aftab teaches the apparatus as recited in claim 1, wherein the configuration further indicates one or more of a condition, a timing, or a periodicity for the data collection associated with the model ID, the machine learning function, or the machine learning use case (Soldati, figure 9c; page 8, paragraph 92; page 12, paragraph 178 & 181; i.e., [0092] the supplementary ML model information may comprise an identification 924, for a RAN operation, of an ML model that the wireless device. ML model information may additionally or alternatively comprise an identification 925 of information reported by the wireless device. [0178] Examples of wireless device reports that may include an identification of ML based information include reports. [0181] The model ID could also indicate the operator or network vendor that generated the model).
As to claim 33, Soldati-Aftab teaches the apparatus as recited in claim 25, wherein provide, in association with the registration of the machine learning model, at least one of an input feature for the machine learning model or a data processing module for obtaining the input feature for the machine learning model (Soldati, figure 9c; page 8, paragraph 92; page 12, paragraph 178 & 181; i.e., [0092] the supplementary ML model information may comprise an identification 924, for a RAN operation, of an ML model that the wireless device. ML model information may additionally or alternatively comprise an identification 925 of information reported by the wireless device. [0178] Examples of wireless device reports that may include an identification of ML. [0181] The model ID could also indicate the operator or network vendor that generated the model).
Claim(s) 16 & 27 is/are directed to a system & method claims and they do not teach or further define over the limitations recited in claim(s) 1. Therefore, claim(s) 16 & 27 is/are also rejected for similar reasons set forth in claim(s) 1.
Claim(s) 17-21 & 22-24 is/are directed to a system & method claims and they do not teach or further define over the limitations recited in claim(s) 7-11 & 13-15. Therefore, claim(s) 17-21 & 22-24 is/are also rejected for similar reasons set forth in claim(s) 7-11 & 13-15.
Claim(s) 26, 29 & 30 is/are directed to a system & method claims and they do not teach or further define over the limitations recited in claim(s) 7, 8 & 12. Therefore, claim(s) 26, 29 & 30 is/are also rejected for similar reasons set forth in claim(s) 7, 8 & 12.
Claim(s) 10 & 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Soldati, U.S. Pub/Patent No. US 2023/0276264 A1 in view of Aftab, US 2019/0325353 A1, and further in view of Zeng, U.S. Patent/Pub. No. US 2023/0209390 A1.
As to claim 10, Soldati-Aftab teaches the apparatus as recited in claim 1. But Soldati-Aftab failed to teach the claim limitation wherein the at least one processor is further configured to: receive a data reporting deactivation of the configuration for the model ID, the machine learning function, or the machine learning use case; and stop the reporting of the data for the model ID, the machine learning function, or the machine learning use case in response to the data reporting deactivation of the configuration.
However, Zeng teaches the limitation wherein receive a data reporting deactivation of the configuration for the model ID, the machine learning function, or the machine learning use case (Zeng, page 2, paragraph 21; i.e., [0021] an activated state or a deactivated state, or the task status includes an activated state, a deactivated state, or a released state); and stop the reporting of the data for the model ID, the machine learning function, or the machine learning use case in response to the data reporting deactivation of the configuration (Zeng, page 30, paragraph 408; i.e., [0408] In the method shown in FIG. 8B, the RIC module may further delete or terminate, by using a task deletion procedure, a task that has been published to the base station. The task release message is used to release or terminate one or more tasks, and a name of the message is not limited, for example, may be a task release request message or a yth message, where y is a positive integer. The task release acknowledgement message is used to acknowledge releasing or termination of one or more tasks, and a name of the message is not limited, for example, may be a yth message, where y is a positive integer).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Soldati-Aftab to substitute configuration parameter from Zeng for performance parameter from Soldati-Aftab to ultra-high rate, an ultra-low latency, and/or massive connection (Zeng, page 1, paragraph 3).
Claim(s) 20 is/are directed to a system claim and they do not teach or further define over the limitations recited in claim(s) 10. Therefore, claim(s) 20 is/are also rejected for similar reasons set forth in claim(s) 10.
Claim(s) 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Soldati, U.S. Pub/Patent No. US 2023/0276264 A1 in view of Aftab, US 2019/0325353 A1, and further in view of Hong, US 2022/0329493 A1.
As to claim 31, Soldati-Aftab teaches the apparatus as recited in claim 3. But Soldati-Aftab failed to teach the claim limitation wherein the at least one processor is further configured to: transmit, to the network node, a request for the configuration associated with the data collection for one or more of model training or model inference for a particular model ID, wherein the configuration for the particular model ID is received in response to the request.
However, Hong teaches the limitation wherein transmit, to the network node, a request for the configuration associated with the data collection for one or more of model training or model inference for a particular model ID, wherein the configuration for the particular model ID is received in response to the request (Hong, page 6, paragraph 157-160; i.e., [0157] training data: data provided from the NWDAF to the AI platform for AI model training, including the collected data and/or an analytics result thereof; [0158] an AI model identifier: an identifier of the desired AI model; [0159] an AI model type identifier: an identifier of type of the desired AI model; [0160] model screening condition: various conditions ( e.g., running environment requirements, performance, accuracy, etc.) to be satisfied by the AI model).
It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify Soldati-Aftab to substitute application function from Zeng for management function from Soldati to provides an analytics result to the AF, NF, or OAM (Hong, page 1, paragraph 3).
Response to Arguments
Applicant's arguments with respect to claim(s) 1-11, 13-27, 29, 31-33 have been considered but are moot in view of the new ground(s) of rejection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Listing of Relevant Arts
Chong, U.S. Patent/Pub. No. US 20230127558 A1 discloses AI training platform, model ID and trigger condition.
Cirkic, U.S. Patent/Pub. No. US 20210076267 A1 discloses triggering criteria, report, ML model and ID.
Contact Information
The present application is being examined under the pre-AIA first to invent provisions.
THUONG NGUYEN whose telephone number is (571)272-3864. The examiner can normally be reached on Monday-Friday 9:00-6:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Noel Beharry can be reached on 571-270-5630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THUONG NGUYEN/Primary Examiner, Art Unit 2416