DETAILED ACTION
This application has been examined. Claims 1-15,18,30,54-56 are pending. Claims 16-17,19-29,31-53,57-58 are cancelled.
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 .
Making Final
Applicant's arguments filed 6/25/2026 have been fully considered but they are not persuasive.
The Examiner is maintaining the rejection(s) using the same grounds for rejection and thus making this action FINAL.
Response to Arguments
Applicant's arguments filed 6/25/2026 have been fully considered but they are not persuasive.
The Applicant presents the following argument(s) [in italics]:
…Ryden neither discloses nor suggests that a wireless communication device reports a capability indicating whether it is capable of executing multiple ML models, nor that a source device determines a subset of multiple ML models from a model pool based on such capability information…
The Examiner respectfully disagrees with the Applicant.
Ryden-Lutchoomun disclosed wherein a wireless communication device reports a capability indicating whether it is capable of executing multiple ML models ((Ryden-Paragraph 43, obtaining (for example requesting and receiving) information about a capacity of the wireless device to execute an ML model in step 202a, Paragraph 105, the capabilities may include an indication about the number of different ML models with which the UE can be configured simultaneously, also Lutchoomun-Paragraph 148, WTRU may send an indication to a gNB or to another WTRU when it determines that a model is not suitable)
Ryden-Lutchoomun disclosed wherein a source device determines a subset of multiple ML models from a model pool based on such capability information (Ryden-Paragraph 45, RAN node determines, on the basis of the information about an operating environment of the wireless device, configuration information for an ML model to be executed by the wireless device,Paragraph 98, enable the network node to select the most appropriate model (type, dimension, etc.) for a specific UE based on the UE capabilities.)
The Applicant presents the following argument(s) [in italics]:
… Lutchoomun does not disclose selecting a subset from a pool of multiple ML models according to device capability. Furthermore, transferring model parameters is fundamentally different from downloading one or more ML models from a capability-matched subset and activating the downloaded models for execution….
The Examiner respectfully disagrees with the Applicant.
Lutchoomun is not relied upon to disclose ‘selecting a subset from a pool of multiple ML models according to device capability.’
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Ryden-Lutchoomun disclosed ‘selecting a subset from a pool of multiple ML models according to device capability.’ (Ryden-Paragraph 45, RAN node determines, on the basis of the information about an operating environment of the wireless device, configuration information for an ML model to be executed by the wireless device,Paragraph 98, enable the network node to select the most appropriate model (type, dimension, etc.) for a specific UE based on the UE capabilities.)
Ryden-Lutchoomun disclosed ‘downloading one or more ML models from a capability-matched subset and activating the downloaded models for execution (Lutchoomun-Paragraph 137,a WTRU may be provided with one or more AIML models. The WTRU may receive or download an AIML model from a gNB or another WTRU, Paragraph 90, model selection/activation/deactivation/switching/ fallback)
The Applicant presents the following argument(s) [in italics]:
…The Office Action appears to rely on impermissible hindsight reconstruction by selectively extracting isolated features from the references and using Applicants' disclosure as a roadmap to arrive at the claimed invention. Absent knowledge of the present application, a person of ordinary skill in the art would not have been motivated to modify the model configuration framework of Ryden with the transfer-learning mechanisms of Lutchoomun so as to arrive at the claimed capability-based subset selection, downloading, and activation of multiple ML models….
The Examiner respectfully disagrees with the Applicant.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
The Applicant presents the following argument(s) [in italics]:
… Ryden is concerned with model configuration and execution, whereas Lutchoomun is concerned with model training and parameter transfer. Neither reference recognizes the problem of capability-based activation and management of multiple pre- deployed ML models…
The Examiner respectfully disagrees with the Applicant.
Ryden-Lutchoomun disclosed ‘activation and management of multiple pre- deployed ML models’ (Lutchoomun-Paragraph 137,a WTRU may be provided with one or more AIML models. The WTRU may receive or download an AIML model from a gNB or another WTRU, Paragraph 90, model selection/ activation/ deactivation/ switching/ fallback)
Priority
This application claims benefits of priority from PCT Application PCT/CN2022/100544 filed June 22, 2022.
The effective date of the claims described in this application is June 22, 2022.
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) 1-3,30,54-56 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryden (USPGPUB 2024/0049003) further in view of Lutchoomun (USPGPUB 2025/0350383)
Regarding Claim 1
Ryden Figure 2a Paragraph 42 disclosed wherein the RAN node may obtain information about an operational environment of the wireless device to be managed.
Ryden Paragraph 97 disclosed wherein the configuration information for an ML model to be executed by a wireless device may be determined, at least in part, on the basis of a capability of the wireless device to execute an ML model. An indication of such capability may be received from the wireless device, for example as part of a legacy UE Capability Information message. For example, in 3GPP LTE or Next Generation-RAN systems, a user device transmits to the network node a UE Capability Information message via Radio Resource Control (RRC) signalling, for example during initial registration process. The legacy UE Capability Information message could be enhanced to include information associated to the UE capabilities to execute models. This could avoid configuring models that would result be too complex to be executed by the user device.
Ryden Paragraph 105 disclosed wherein the capabilities may include an indication about the number of different ML models with which the UE can be configured simultaneously.
Ryden disclosed (re. Claim 1) a device capability discovery method for machine learning (ML), executable in a wireless communication device, comprising:
transmitting a capability message of the wireless communication device to a source device having machine learning (ML) models, wherein the capability message shows whether the wireless communication device is capable of executing multiple ML models; (Ryden-Paragraph 43, obtaining (for example requesting and receiving) information about a capacity of the wireless device to execute an ML model in step 202a, Paragraph 105, the capabilities may include an indication about the number of different ML models with which the UE can be configured simultaneously)
and identifying one or more ML models from a subset of ML models, (Ryden-Paragraph 82, a network node of a RAN network transmits, to at least one wireless user device, a control message comprising information necessary to configure the user device with one or more models for executing radio networking operations,Paragraph 105, the capabilities may include an indication about the number of different ML models with which the UE can be configured simultaneously.) wherein the subset of ML models matches the capability message of the wireless communication device.(Ryden-Paragraph 45, RAN node determines, on the basis of the information about an operating environment of the wireless device, configuration information for an ML model to be executed by the wireless device,Paragraph 98, enable the network node to select the most appropriate model (type, dimension, etc.) for a specific UE based on the UE capabilities.)
While Ryden substantially disclosed the claimed invention Ryden does not disclose (re. Claim 1) downloading and activating one or more ML models from a subset of ML models.
Lutchoomun Paragraph 137 disclosed wherein a WTRU may be provided with one or more AIML models. The WTRU may receive or download an AIML model from a gNB or another WTRU.
Lutchoomun disclosed (re. Claim 1) downloading and activating one or more ML models from a subset of ML models.( Lutchoomun-Paragraph 137,a WTRU may be provided with one or more AIML models. The WTRU may receive or download an AIML model from a gNB or another WTRU, Paragraph 90, model selection/activation/deactivation/switching/fallback)
Ryden and Lutchoomun are analogous art because they present concepts and practices regarding configuration of devices for executing AI/ML models. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Lutchoomun into Ryden. The motivation for the said combination would have been to enable determining that the one or more AI/ML models are not suitable for use by the WTRU, and to send first information indicating that the one or more AI/ML models are not suitable for the WTRU and/or that the WTRU will be training a local AI/ML model.(Lutchoomun-Paragraph 4)
Regarding Claim 30
Claim 30 (re. method) recites substantially similar limitations as Claim 1. Claim 30 is rejected on the same basis as Claim 1.
Regarding Claim 54
Claim 54 (re. device) recites substantially similar limitations as Claim 1. Claim 54 is rejected on the same basis as Claim 1.
Regarding Claim 55
Claim 55 (re. chip) recites substantially similar limitations as Claim 1. Claim 55 is rejected on the same basis as Claim 1.
Regarding Claim 56
Claim 56 (re. non-transitory computer-readable storage medium) recites substantially similar limitations as Claim 1. Claim 56 is rejected on the same basis as Claim 1.
Regarding Claim 2
Ryden-Lutchoomun disclosed (re. Claim 2) wherein the capability message comprises a model type.(Ryden- Paragraph 98, Paragraph 103, the capabilities signalled to the network node could include Type of models supported, for example decision tree, decision forest, linear regression, feedforward neural network, recurrent neural network, convolutional neural network, etc….enable the network node to select the most appropriate model (type, dimension, etc.) for a specific UE based on the UE capabilities.)
Regarding Claim 3
Ryden-Lutchoomun disclosed (re. Claim 3) wherein the model type comprises one or more of: a type of ML model trained to provide CSI feedback; (Ryden-Paragraph 82,Paragraph 84-90, enable a network node to configure a wireless user device to perform one or more of the user device's radio networking operations based on a model … operations performed by the user device that could be executed in accordance with the configured model include Beam Management, Compression of channel state information (CSI) ) a type of ML model trained to provide beam prediction in a time domain; a type of ML model trained to provide beam prediction in a spatial domain; and a type of ML model trained to provide positioning.
Claim(s) 4-7,18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryden (USPGPUB 2024/0049003) further in view of Lutchoomun (USPGPUB 2025/0350383) further in view of Li (USPGPUB 2025/0293942)
Regarding Claim 4
While Ryden-Lutchoomun substantially disclosed the claimed invention Ryden-Lutchoomun does not disclose (re. Claim 4) wherein the model type comprises one or more of: a type of generalized ML model; and a type of scenario-specific ML model.
Li Paragraph 97 disclosed wherein UE is capable of supporting at least two ML models, where the first ML model is a generalized model that can be used in a wide variety of deployments (e.g., indoor and outdoor, dense urban and rural, high mobility and low mobility), and the second ML model is a specialized model that is trained for best performance for a particular deployment.
Li disclosed (re. Claim 4) wherein the model type comprises one or more of: a type of generalized ML model; and a type of scenario-specific ML model. (Li-Paragraph 97, UE is capable of supporting at least two ML models, where the first ML model is a generalized model that can be used in a wide variety of deployments (e.g., indoor and outdoor, dense urban and rural, high mobility and low mobility), and the second ML model is a specialized model that is trained for best performance for a particular deployment.)
Ryden,Lutchoomun and Li are analogous art because they present concepts and practices regarding configuration of devices for executing AI/ML models. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Li into Ryden-Lutchoomun. The motivation for the said combination would have been to ensure that robustness and resilience performance of the functionality is not impacted if the ML model is not performing well. (Li-Paragraph 26)
Regarding Claim 5
Ryden-Lutchoomun-Li disclosed (re. Claim 5) wherein a first range of ML model identifiers is allocated to the type of generalized ML model, (Li-Paragraph 98, the two ML-models are identified by a model ID or model version ,Paragraph 111, capability message also indicates that the UE may support different combinations of at least one ML-based feature and at least one fallback feature) and a second range of ML model identifiers is allocated to the type of scenario-specific ML model. (Li-Paragraph 98, the two ML-models are identified by a model ID or model version , Paragraph 111, capability message also indicates that the UE may support different combinations of at least one ML-based feature and at least one fallback feature)
Regarding Claim 6
Ryden-Lutchoomun-Li disclosed (re. Claim 6) wherein according to a mapping between a first generalized ML model and a first scenario-specific ML model, (Li-Paragraph 98, the two ML-models are identified by a model ID or model version , Paragraph 111, capability message also indicates that the UE may support different combinations of at least one ML-based feature and at least one fallback feature) the first scenario-specific ML model serves as a backup ML model for the first generalized ML model, the first scenario-specific ML model is activated in response to deactivation of the first generalized ML model.(Li-Paragraph 29, When a performance problem is detected for at least one ML-based feature, the UE may either be instructed by the network to switch to a fallback feature for the functionality or autonomously switch to a fallback feature and indicate the feature switching to the network )
Regarding Claim 7
Ryden-Lutchoomun-Li disclosed (re. Claim 7) wherein the mapping between the first generalized ML model and the first scenario-specific ML model is determined based on association between a model identifier of the first generalized ML model and a model identifier of the first scenario-specific ML model. (Li-Paragraph 98, the two ML-models are identified by a model ID or model version , Paragraph 111, capability message also indicates that the UE may support different combinations of at least one ML-based feature and at least one fallback feature)
Regarding Claim 18
Ryden-Lutchoomun-Li disclosed (re. Claim 18) wherein a first set of UE capabilities of the wireless communication device is disabled in response to enabling of a second set of UE capabilities of the wireless communication device; (Li-Paragraph 39, configures the UE to deactivate/stop/switch-off at least one ML-based feature and active/switch-on the associated fallback feature(s) for the associated functionality ) or the first set of UE capabilities of the wireless communication device is enabled in response to enabling of the second set of UE capabilities of the wireless communication device. (Li-Paragraph 29, When a performance problem is detected for at least one ML-based feature, the UE may either be instructed by the network to switch to a fallback feature for the functionality or autonomously switch to a fallback feature and indicate the feature switching to the network, Paragraph 39, configures the UE to deactivate/stop/switch-off at least one ML-based feature and active/switch-on the associated fallback feature(s) for the associated functionality )
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryden (USPGPUB 2024/0049003) further in view of Lutchoomun (USPGPUB 2025/0350383) further in view of Li (USPGPUB 2025/0293942) further in view of Ren1 (USPGPUB 20240259954)
Regarding Claim 8
While Ryden-Lutchoomun-Li substantially disclosed the claimed invention Ryden-Lutchoomun-Li does not disclose (re. Claim 8) wherein the mapping between the first generalized ML model and the first scenario-specific ML model is determined based on association between a parameter range of the first generalized ML model and a parameter range of the first scenario-specific ML model.
Ren1 Paragraph 6,Paragraph 38 disclosed receiving a physical downlink control channel (PDCCH) message comprising an indication of a machine learning model group. The method further includes switching to the machine learning model group in response to receiving the PDCCH.
Ren1 Paragraph 104 disclosed wherein association rules between the PDCCH parameters and machine learning groups are standardized. Once a specific PDCCH parameter is configured, the corresponding machine learning group is triggered.
Ren1 disclosed (re. Claim 8) wherein the mapping between the first generalized ML model and the first scenario-specific ML model is determined based on association between a parameter range of the first generalized ML model (Ren1- Paragraph 104, association rules between the PDCCH parameters and machine learning groups are standardized. Once a specific PDCCH parameter is configured, the corresponding machine learning group is triggered…. if the UE-specific search space set is configured for physical downlink shared channel (PDSCH), the advanced downlink (DL) model group is triggered to pursue better performance ) and a parameter range of the first scenario-specific ML model.( Ren1- Paragraph 104, association rules between the PDCCH parameters and machine learning groups are standardized. Once a specific PDCCH parameter is configured, the corresponding machine learning group is triggered…. only the basic function model group is triggered, or the low-complexity model groups would be triggered)
Ryden,Lutchoomun and Ren1 are analogous art because they present concepts and practices regarding configuration of devices for executing AI/ML models. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Ren1 into Ryden-Lutchoomun. The motivation for the said combination would have been to enable a UE to quickly switch the machine learning groups to adapt to different conditions and specifications.(Ren1-Paragraph 88)
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryden (USPGPUB 2024/0049003) further in view of Lutchoomun (USPGPUB 2025/0350383) further in view of Li (USPGPUB 2025/0293942) further in view of Ren1 (USPGPUB 2024/0259954) further in view of Balevi (USPGPUB 2023/0232377)
Regarding Claim 9
While Ryden-Lutchoomun-Li substantially disclosed the claimed invention Ryden-Lutchoomun-Li does not disclose (re. Claim 9) wherein the first scenario-specific ML model works for a first parameter range, a second scenario-specific ML model works for a second parameter range, the first generalized ML model works for the first parameter range and the second parameter range, and the first scenario-specific ML model and the second scenario-specific ML model serve as backup ML models for the first generalized ML model
Balevi Figure 4,Figure 6,Paragraph 61 disclosed wherein a first local machine learning model and a second local machine learning model update the global model and the parameter server 410 may aggregate 414 all the updates 422 from the edge devices 402, and may update 416 the global machine learning model 412 based on the aggregated updates 422.
Balevi disclosed (re. Claim 9) wherein the first scenario-specific ML model works for a first parameter range, a second scenario-specific ML model works for a second parameter range, the first generalized ML model works for the first parameter range and the second parameter range, and the first scenario-specific ML model and the second scenario-specific ML model serve as backup ML models for the first generalized ML model.( Balevi-Figure 4,Paragraph 61, a first local machine learning model and a second local machine learning model update the global model and the parameter server 410 may aggregate 414 all the updates 422 from the edge devices 402, and may update 416 the global machine learning model 412 based on the aggregated updates 422. )
Ryden,Lutchoomun and Balevi are analogous art because they present concepts and practices regarding configuration of devices for executing AI/ML models. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Balevi into Ryden-Lutchoomun. The motivation for the said combination would have been to enable federated learning to be performed with OTA aggregation, which may be associated with smaller communication overhead than digital transmission because OTA aggregation may allow the edge devices to transmit the updates over the same time-frequency resources on a multiple access channel.(Balevi-Paragraph 62)
Claim(s) 10-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryden (USPGPUB 2024/0049003) further in view of Lutchoomun (USPGPUB 2025/0350383) further in view of Ren2 (USPGPUB 2025/0007789)
Regarding Claim 10
While Ryden-Lutchoomun substantially disclosed the claimed invention Ryden-Lutchoomun does not disclose (re. Claim 10) wherein the capability message comprises a maximum number of ML models supported by the wireless communication device.
Ren2 Paragraph 39 disclosed wherein a compute resource limit may be defined in terms of a number of models that can execute simultaneously or substantially simultaneously.
Ren2 disclosed (re. Claim 10) wherein the capability message comprises a maximum number of ML models supported by the wireless communication device (Ren2-Paragraph 35, UEs can be configured with sets of machine learning models that are appropriate for the amount of compute resources available at the UE while balancing other factors, such as the accuracy of inferences performed by the UE, the number of models that can be executed concurrently, Paragraph 39,compute resource limit may be defined in terms of a number of models that can execute simultaneously or substantially simultaneously.)
Ryden,Lutchoomun and Ren2 are analogous art because they present concepts and practices regarding configuration of devices for executing AI/ML models. Before the time of the effective filing date of the claimed invention it would have been obvious to combine Ren2 into Ryden-Lutchoomun. The motivation for the said combination would have been to enable a UE to be configured with a set of machine learning models that are suitable for use by that UE and do not exceed the computational capabilities of the UE. Further, the set of machine learning models may include models of varying complexity, which may allow for inferences to be performed using a variety of machine learning models with varying levels of accuracy, based on an amount of accuracy needed for a given task. (Ren2-Paragraph 18)
Regarding Claim 11
Ryden-Lutchoomun-Ren2 disclosed (re. Claim 11) wherein the maximum number of ML models supported by the wireless communication device is associated with the model type (Ren2-Paragraph 57, compute resource limits may be signaled by a UE to a network entity (or other machine learning model manager) and may specify the maximum computational complexity)
Regarding Claim 12
Ryden-Lutchoomun-Ren2 disclosed (re. Claim 12) wherein the capability message comprises a set of capabilities of the wireless communication device for determining a complexity level of the wireless communication device; or the capability message comprises the complexity level of the wireless communication device. (Ren2-Paragraph 57, compute resource limits may be signaled by a UE to a network entity (or other machine learning model manager) and may specify the maximum computational complexity)
Regarding Claim 13
Ryden-Lutchoomun-Ren2 disclosed (re. Claim 13) wherein the complexity level of the wireless communication device is associated with the model type. (Ren2-Paragraph 57, compute resource limits may be signaled by a UE to a network entity (or other machine learning model manager) and may specify the maximum computational complexity)
Regarding Claim 14
Ryden-Lutchoomun-Ren2 disclosed (re. Claim 14) reporting successful deployment of a first download ML model (Lutchoomun-Paragraph 97, WTRU may assess the validity/applicability of each model in the first set of models that it is configured ) in the downloaded one or more ML models when a complexity level of the first download ML model matches the complexity level of the wireless communication device; (Ren2-Paragraph 57, compute resource limits may specify the maximum computational complexity ) and reporting unsuccessful deployment of the first download ML model in the downloaded one or more ML models (Lutchoomun-Paragraph 148, WTRU may send an indication to a gNB or to another WTRU when it determines that a model is not suitable) when the complexity level of the first download ML model does not match matches the complexity level of the wireless communication device. (Ren2-Paragraph 57, compute resource limits may specify the maximum computational complexity)
Regarding Claim 15
Ryden-Lutchoomun-Ren2 disclosed (re. Claim 15) wherein the set of capabilities of the wireless communication device comprises one or more of: a central processing unit (CPU), a memory size, a storage, floating-point operations per second (FLOPs), power, a buffer size, and a bus bandwidth of the wireless communication device.(Ren2-Paragraph 37, a compute resource limit may be defined for model computational complexity. Model computational complexity may be defined in terms of a number of instructions performed over a given time period (e.g., in terms of millions of operations per second, or MIPS) or a number of specific types of instructions performed over the given time period (e.g., in terms of floating point operations per second, or FLOPS).)
Conclusion
Examiner’s Note: In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREG C BENGZON whose telephone number is (571)272-3944. The examiner can normally be reached on Monday - Friday 8 AM - 4:30 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, John Follansbee can be reached on (571) 272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GREG C BENGZON/ Primary Examiner, Art Unit 2444
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