DETAILED ACTION
Notice of Pre-AIA or AIA Status
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 § 103
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.
Claim(s) 1-6 and 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over Muhammad et al. (US 2024/0370760 A1), hereinafter referred to as D1, in view of Wang et al. (US 2025/0192849 A1), hereinafter referred to as D2.
Regarding claims 1 and 9, D1 discloses a method and system for managing AI models, which comprises:
a transceiver configured to enable wireless communications with a base station; and a processor, communicatively coupled to the transceiver, and configured to (Referring to Figures 1 and 2, UE in wireless communication with a base station, by definition comprising processors and transceivers. See paragraph 0059.):
receive, using the transceiver, a configuration message from the base station, wherein the configuration message indicates a model identification (ID) and one or more signals (Note, the Examiner interprets the claim as directed to a two-sided AI model as consistent with the Applicant’s instant disclosure. The two-sided AI model refers to a collaborative framework where both the network and the UE operate on a shared AI-trained representation. The terms “two-sided model” refers to an AI/ML model which is utilized (e.g., trained, validated, tested, inferenced, etc.) at both sides. For example, a two-sided model may be an AI/ML model which is firstly utilized at the UE, and then utilized at the gNB, or vice-versa. See paragraph 0068. The network may provide the AI/ML model and/or inference tuning parameters to the UE based on the model ID, and the like (receive from base station a configuration message which indicates the ID). See paragraphs 0070, 0071. and 0075. Performing air interface optimization may include channel state information (CSI) feedback enhancement, beam management, positioning accuracy enhancement, and optimization of any other suitable air interface related operations and features (CSI feedback procedure, per the standard comprises transmitting CSI-RS from the base station to the UE and UE responds with CSI feedback, thereby, disclosing one or more signals). See paragraph 0076.);
train a first artificial intelligence (AI) model corresponding to the model ID using the data set (Referring to Figures 1 and 2, as part of the two-sided model and the CSI feedback enhancement, the UE trains the CSI feedback data at the UE. See paragraphs 0068 and 0075.);
generate a second data set corresponding to the model ID (Referring to Figures 1 and 2, as part of the two-sided model the UE trains the CSI feedback to generate a resulting second set of data for transmission to the base station as part of the CSI feedback optimization. See paragraphs 0068 and 0075.); and
transmit, using the transceiver, the second data set and the model ID to the base station, wherein the second data set enables a second Al model corresponding to the model ID to be trained (Referring to Figures 1 and 2, as part of the two-sided model and CSI feedback enhancement the data is transmitted to the base station in which the AI model is then used on the received data. See paragraphs 0068. D2 teaches the information exchange may be performed based on ID of the associated AI/ML model(s) (transmit the model ID to the base station). For instance, the network may provide the AI/ML model and/or inference tuning parameters to the UE based on the model ID, and the like. See paragraph 0075. In this manner, the two-sided model performs training at both sides, UE and base station, based upon the ID of the AI/ML model. In summary, D1 teaches the claimed limitation as recited above as followed: receiving configuration information (network provides Model ID and signal for CSI feedback optimization), training the AI model corresponding to the model ID (per the two-sided model, training the AI corresponding to the Model ID received from the base station), generating a second data set corresponding to the model ID (per the two-sided model and CSI feedback optimization, generating results form the trained AI at the UE for transmission to the base station), transmitting the second data set and model ID to the base station (per the two-sided model and CSI feedback optimization, transmitting the feedback to the base station per the Model ID for training at the base station).)
D1 does not explicitly disclose measure, using the transceiver, the one or more signals to generate a data set corresponding to the model ID.
D1 teaches a two-sided AI model for CSI feedback optimization, which is generally understood by a person of ordinary skill in the art to comprise transmitting a CSI-RS from the base station to the UE and UE responds with CSI feedback in which the UE provides compressed, high resolution CSI. The key benefit is in the form of channel matrices that facilitate network-side CSI processing by better preserving phase information. D1 does not explicitly recite the CSI feedback optimization process itself, though a person of ordinary skill in the art would recognize the term “CSI feedback optimization” as described above. D2 teaches utilizing AI models for CSI estimation and reporting, see paragraph 0073, and it is well-known in the art for measurement and feedback of channel state information (CSI), CSI is measured at a terminal equipment side (measuring the one or more signals), an AI/ML model is used to generate CSI feedback information, and after transmitting the same to a network side via an air interface, the network side receives the CSI feedback information, and restores original CSI via a corresponding AI/ML model. In such example, by using the AI/ML model, CSI feedback overhead may be reduced, or quality of feedbacks may be improved, thereby improving the communication quality. See paragraphs 0005 and 0079. The AI/ML model is a two-sided model and has a model identifier and a version identifier; the CSI generation portion (generating a data set corresponding to the model ID) and the CSI reconstruction portion of the two-sided model have the same model identifier and version identifier. See paragraph 0211.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement the well-known CSI measurement procedure of D2 in the CSI feedback optimization of D1. One of ordinary skill in the art before the effective filing date of the invention would have been motivated to do so to comply with well-known standards for CSI measurement procedures for AI based CSI optimization feedback.
Regarding claims 2 and 10 the primary reference further teaches wherein the first AI model and the second Al model are parts of a two-sided AT model corresponding to the model ID (Referring to Figures 1 and 2, channel state information (CSI) feedback enhancement utilizing two-sided AI model, first and second AI model, corresponding to the model ID. See paragraphs 0067-0071 and 0074-0076.)
Regarding claims 3 and 11, the primary reference further teaches wherein the one or more signals include a channel state information reference signal (CSI-RS) (Referring to Figures 1 and 2, D1 teaches a two-sided AI model for CSI feedback optimization, which is generally understood to comprise transmitting a CSI-RS from the base station to the UE and UE responds with CSI feedback in which the UE provides compressed, high resolution CSI. See paragraphs 0067-0071. See also D2, paragraphs 0204 and 0215.)
Regarding claims 4 and 12, the primary reference further teaches transmit, using the transceiver, a capability report to the base station, wherein the capability report indicates a two sided model including the first Al model and the second Al mode (Referring to Figures 1 and 2, At operation S320, one or more AI/ML capability reports are received. Specifically, the first node may be configured to receive, from the UE, one or more AI/ML capability reports in response to the AI/ML capability request(s), wherein the one or more AI/ML capability reports may indicate the AI/ML capabilities of the UE. According to embodiments, the one or more AI/ML capability reports may include information defining the classification of the AI/ML capabilities of the UE. See paragraphs 0098-0099. Corresponding to two-sided models. See paragraph 0070.)
Regarding claims 5 and 13, the primary reference further teaches receive, using the transceiver, an activation message that indicates the model ID; and activate, based on the activation message, the first AI model to perform a task (Referring to Figures 1 and 2, the network may provide the AI/ML model and/or inference tuning parameters to the UE based on the model ID, and the like. Further, according to embodiments, at the second level #1, a full scale AI/ML model training may occur at the UE and/or at the network (activate based on the activation message the first AI model to perform a task). See paragraphs 0075-0077.).
Regarding claims 6 and 14, the primary reference further teaches wherein the task include: channel state information (CSI) compression (Referring to Figures 1 and 2, D1 teaches a two-sided AI model for CSI feedback optimization, which is generally understood to a person of ordinary skill in the art to comprise transmitting a CSI-RS from the base station to the UE and UE responds with CSI feedback in which the UE provides compressed, high resolution CSI. The key benefit is in the form of channel matrices that facilitate network-side CSI processing by better preserving phase information. See paragraph 0076. See also, D2 at paragraph 0079.)
Claim(s) 7, 8, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Muhammad et al. (US 2024/0370760 A1), hereinafter referred to as D1, in view of Wang et al. (US 2025/0192849 A1), hereinafter referred to as D2, in further view of Kumar at al. (US 2022/0182263 A1), hereinafter referred to as D3.
Regarding claims 7 and 15, D1 does not disclose wherein the processor is further configured to: monitor a signal quality of signals received from the base station; determine that the signal quality is below a predetermine threshold; transmit an action request to the base station; and receive an action confirmation from the base station.
D3 teaches model discovery and selection for cooperative machine learning in cellular networks, which comprises, referring to Figure 7, for example, if the performance of the UE 702 degrades below a threshold, the UE 702 may transmit, at 720, a ML/NN model and feature request to the base station 704 to switch to the ML/NN procedure of the UE 702 and increase the performance of the UE 702, such as for ML/NN procedures associated with encoding and decoding operations. The ML/NN model and feature request may be further transmitted, at 722, from the base station 704 to the OAM core network 706, which may transmit, at 724, a ML/NN model and feature response to the base station 704 based on the received ML/NN model and feature request. The base station 704 may likewise relay, at 726, the ML/NN model and feature response for the ML/NN procedure to the UE 702 based on the ML/NN model and feature request received, at 720, and the ML/NN model and feature response received, at 724. See paragraphs 0080-0082.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement the model selection of D3 in the system of D1 and D2. One of ordinary skill in the art before the effective filing date of the invention would have been motivated to do so to improve system performance when UE performance degrades.
Regarding claim 8 and 16, D1 does not disclose wherein the action request indicates: a deactivation the first AI model and the second AI model; a switch to other AI models; or a falling back to a default.
D3 teaches model discovery and selection for cooperative machine learning in cellular networks, which comprises, referring to Figure 7, for example, if the performance of the UE 702 degrades below a threshold, the UE 702 may transmit, at 720, a ML/NN model and feature request to the base station 704 to switch to the ML/NN procedure of the UE 702 and increase the performance of the UE 702, such as for ML/NN procedures associated with encoding and decoding operations. The ML/NN model and feature request may be further transmitted, at 722, from the base station 704 to the OAM core network 706, which may transmit, at 724, a ML/NN model and feature response to the base station 704 based on the received ML/NN model and feature request. The base station 704 may likewise relay, at 726, the ML/NN model and feature response for the ML/NN procedure to the UE 702 based on the ML/NN model and feature request received, at 720, and the ML/NN model and feature response received, at 724. See paragraphs 0080-0082.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement the model selection of D3 in the system of D1 and D2. One of ordinary skill in the art before the effective filing date of the invention would have been motivated to do so to improve system performance when UE performance degrades.
Claim Rejections - 35 USC § 102
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 –
(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.
Claim(s) 17 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by D1.
Regarding claim 17, D1 discloses a method and system for managing AI models, which comprises:
a transceiver configured to enable wireless communications with a base station and a processor, communicatively coupled to the transceiver (Referring to Figures 1 and 2, UE in wireless communication with a base station, by definition comprising processors and transceivers. See paragraph 0059.), configured to:
transmit, using the transceiver, a capability report to the base station, wherein the capability report indicates one or more model functions (Referring to Figures 1 and 2, At operation S320, one or more AI/ML capability reports are received. Specifically, the first node may be configured to receive, from the UE, one or more AI/ML capability reports in response to the AI/ML capability request(s), wherein the one or more AI/ML capability reports may indicate the AI/ML capabilities of the UE. According to embodiments, the one or more AI/ML capability reports may include information defining the classification of the AI/ML capabilities of the UE. See paragraphs 0098-0099.);
receive, using the transceiver, an activation message from the base station, wherein the activation message indicates a model function of the one or more model functions; select an artificial intelligence (AI) model from one or more AI models corresponding to the model function; and perform a task using the AI model (Referring to Figures 1 and 2, At operation S330, the collaboration level is determined. Specifically, the first node may be configured to determine the collaboration level, from among the plurality of predetermined collaboration levels, based on the received AI/ML capability report(s). According to embodiments, the first node may be configured to determine the collaboration level by determining the classification of the AI/ML capabilities of the UE. See paragraph 0099. Based upon the capability report, Class 0-Class 3) (model function of the one or more model functions), the node performs the two sided AI model (select an AI model from the one or more AI models) and CSI feedback optimization in coordination with the UE (perform a task using the AI model). See paragraphs 0097 and 0076.).
Regarding claim 20, D1 discloses wherein the one or more model functions include: channel state information (CSI) prediction module function, spatial domain beam prediction model function, or channel line-of-sight (LoS) classification module function (Referring to Figures 1 and 2, o this end, system 100 provides a framework for managing intelligence and data of one or more AI/ML models in RAN. Accordingly, system 100 (and the associated architecture) may be utilized for various purposes in a telecommunications systems. For instance, one or more example embodiments may apply the system 100 for AI/ML optimization of air interfaces in the telecommunications system. In one or more embodiments, the air interface is a 5G New Radio (NR) air interface, and the air interface optimization may include channel state information (CSI) feedback enhancement (interpreted as CSI prediction module function), beam management (interpreted as spatial domain beam prediction model function), positioning accuracy enhancement, and optimization of any other suitable air interface related operations and features. Furthermore, it is also contemplated that system 100 may be utilized in any other possible use cases (e.g., network energy saving, load balancing, mobility optimization, etc.). See paragraphs 0057-0058.)
Claim Rejections - 35 USC § 103
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.
Claim(s) 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Muhammad et al. (US 2024/0370760 A1), hereinafter referred to as D1, in view Kumar at al. (US 2022/0182263 A1), hereinafter referred to as D3.
Regarding claim 18, D1 does not disclose monitor a signal quality of signals received from the base station; determine that the signal quality is below a predetermine threshold; transmit an action request to the base station; and receive an action confirmation from the base station.
D3 teaches model discovery and selection for cooperative machine learning in cellular networks, which comprises, referring to Figure 7, for example, if the performance of the UE 702 degrades below a threshold, the UE 702 may transmit, at 720, a ML/NN model and feature request to the base station 704 to switch to the ML/NN procedure of the UE 702 and increase the performance of the UE 702, such as for ML/NN procedures associated with encoding and decoding operations. The ML/NN model and feature request may be further transmitted, at 722, from the base station 704 to the OAM core network 706, which may transmit, at 724, a ML/NN model and feature response to the base station 704 based on the received ML/NN model and feature request. The base station 704 may likewise relay, at 726, the ML/NN model and feature response for the ML/NN procedure to the UE 702 based on the ML/NN model and feature request received, at 720, and the ML/NN model and feature response received, at 724. See paragraphs 0080-0082.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement the model selection of D3 in the system of D1. One of ordinary skill in the art before the effective filing date of the invention would have been motivated to do so to improve system performance when UE performance degrades.
Regarding claim 19, D1 does not disclose deactivate the model function; switch to another model function; or fall back to a default.
D3 teaches model discovery and selection for cooperative machine learning in cellular networks, which comprises, referring to Figure 7, for example, if the performance of the UE 702 degrades below a threshold, the UE 702 may transmit, at 720, a ML/NN model and feature request to the base station 704 to switch to the ML/NN procedure of the UE 702 and increase the performance of the UE 702, such as for ML/NN procedures associated with encoding and decoding operations. The ML/NN model and feature request may be further transmitted, at 722, from the base station 704 to the OAM core network 706, which may transmit, at 724, a ML/NN model and feature response to the base station 704 based on the received ML/NN model and feature request. The base station 704 may likewise relay, at 726, the ML/NN model and feature response for the ML/NN procedure to the UE 702 based on the ML/NN model and feature request received, at 720, and the ML/NN model and feature response received, at 724. See paragraphs 0080-0082.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement the model selection of D3 in the system of D1. One of ordinary skill in the art before the effective filing date of the invention would have been motivated to do so to improve system performance when UE performance degrades.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Echigo et al. (US 2025/0227498 A1) - a control section that judges the NW-trained model, based on the NW-trained model information and the reference model information. According to an aspect of the present disclosure, preferable overhead reduction/channel estimation/resource use can be achieved.
Zhu et al. (US 2022/0400373 A1) - transmitting, to a base station (BS), UE capability information indicating at least one radio capability of the UE and at least one machine learning (ML) capability of the UE and receiving, from the BS based on the UE capability information, ML configuration information indicating at least one neural network function (NNF) and at least one ML model corresponding to the at least one NNF.
Sheng (US 2025/0379798 A1) - The wireless communication device transmits a capability message of the wireless communication device to a source device having a pool of machine learning (ML) models. The capability message shows whether the wireless communication device is capable of executing multiple ML models.
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DONALD L. MILLS
Primary Examiner
Art Unit 2462
/Donald L Mills/Primary Examiner, Art Unit 2462