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 .
This action is in response to the application and claims filed 02/06/2024. Claims 1-30 are pending and have been examined. Claims 1-30 are rejected.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present application claims priority to U.S. Provisional Application No. 63/486,814, filed on 02/24/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/19/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informality: in paragraph [0115] it appears that “determining that the one or more AIMs” contains a typographical error and should read “determining that the one or more AI/ML models”.
Appropriate correction is required.
Claim Objections
Claim 23 objected to because of the following informalities: the phrase “receive transmit” in line 2 appears to be a typographical error and renders the intended operation unclear. If supported by Applicant’s original specification, examiner suggests that two ways to address this objection would be to amend the claim to recite either “receive” or “transmit” (or, alternatively, “receive and transmit”). Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 7-8, 17 and 22-23 are rejected under 35 U.S.C 112(b), as being indefinite for failing to point out and distinctly claim the subject matter.
Line 5 of claim 7 recites “a model identifier is used in a functionality for life cycle management (LCM) operations”. The term “a functionality” in this limitation is unclear. Applicant previously introduced “functionalities” in claim 1. However, it is unclear whether “a functionality” subsequently recited in claim 7 refers to one of the “functionalities” previously introduced claim 1 (from which claim 7 depends on, via intervening claim 6), or whether “a functionality” refers to a new and separate functionality not otherwise associated with the previously introduced “functionalities”. Appropriate correction is required. Applicant may wish to amend the claims to either consistently use “functionalities” (or “the one or more functionalities”) throughout to maintain consistency.
Also, claim 8 which depends directly from claim 7 is rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claim 7.
Lines 3-4 of claim 17 recites “wherein receiving the one or more AI/ML models is based at least in part on the indication of the one or more available AI/ML model.” There is insufficient antecedent basis for this limitation in the claim. However, claim 14, from which claim 17 depends on, recites that the apparatus is configured to “transmit one or more AI/ML models” to the UE; claim 14 does not recite any act of the apparatus receiving the AI/ML models. It is unclear whether “receiving” in claim 17 is intended to refer to the UE’s receipt of the models transmitted by the apparatus of claim 14, or whether this is a typographical error and applicant intended to recite “transmitting”, consistent with the language in claims 18 and 19, which depend on claim 14. Appropriate correction required.
Also, claim 23 which depends directly from claim 22 is rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claim 22.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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)(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.
(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.
Claims 1, 3-5, 9, 14, 16-19, 24 and 29-30 are rejected under 35 U.S.C. 102 as being anticipated by Chen Larsson et al. (WIPO Publication No. WO2023209577A1, hereinafter Ericsson). Ericsson was filed as International application no. PCT/IB2023/053131, on 4/25/2023 and claims priority to U.S. provisional application 63/324,967 (hereinafter “provisional ‘967”) filed on 03/29/2022, which is prior to the effective filing date of the instant application, 02/24/2023. Therefore, Ericsson constitutes as prior art under 35 U.S.C. 102(a)(2).
Regarding independent claim 1, Ericsson discloses the invention as including an apparatus for wireless communication at a user equipment (UE), comprising: one or more memories; and one or more processors, coupled to the one or more memories (see paragraph [0130], “UE 2000 includes processing circuitry 2202…a memory 2210…certain UEs may contain…multiples processors, memories” [support found in paragraph [0117] of provisional ‘967]), configured to:
transmit an indication of functionalities associated with artificial intelligence or machine learning (AI/ML) models supported by the UE (see e.g., paragraph [0029], “The term "functionality" may be used in claims instead of "ML model", since "model" may not appear in 3GPP specifications”, see e.g., paragraph [0050], “To structure or partition the signaling of the model type, the diverse ML-Model(s) can be first divided into functional areas and secondly in configuration parts.” and see e.g., paragraph [0033], “UE provides ML model support info to a NW node, including at least one type or version info of at least one ML model associated with a certain functionality”[support found in paragraphs [0025], [0054] and [0020] of provisional ‘967]);
and receive one or more AI/ML models associated with the functionalities (see paragraph [0057], “Based on the UE report of its ML model support, the network can identify the supported functionality, and accordingly configure the specific ML-model…which subsequently also identifies a functional area…when the model is referred to using its model ID”, and see e.g., paragraph [0115] “method 800 further comprise[s] receiving the identified ML model from a network node.” [support found in paragraph [0059], [0044] and [0102] of provisional application ‘967]).
Regarding claim 3, as discussed above, Ericsson discloses the apparatus of claim 1.
Ericsson further discloses wherein the one or more processors are further configured to transmit an indication of supported AI/ML models associated with the functionalities (see e.g., paragraph [0033], “UE provides ML model support info to a NW node” [i.e., “NW node” being network node])
wherein the one or more AI/ML models are received based at least in part on the indication of the supported AI/ML models (see paragraph [0055], “the model capability or availability signaling for one or more supported models, also referred to as model support” [i.e., “model support” includes supported AI/ML models information]; see e.g., paragraph [0009], “obtaining a ML model identifier from the network node that identified one of the one more ML models available at the UE”, see paragraph [0068], “the term ‘available model’ may refer to a model…that may be downloaded on demand to the UE” [i.e., “available model” as defined by Ericsson, may also refer to AI/ML models that are to be received/ downloaded by the UE in addition to indicate AI/ML models that already reside at the UE], see paragraph [0182], “central node is a network node”, and see e.g., paragraph [0174], “UE receives a model version from a central node…UE receives one or more versions from the central node…UE may receive different version(s) from the central node” [i.e., “version nodes” being AI/ML models and UE receiving the models implies that the central node is transmitting the models]).
Regarding claim 4, as discussed above, Ericsson discloses the apparatus of claim 1.
Ericsson further discloses wherein the one or more processors are further configured to transmit an indication of one or more available AI/ML models that are already available at the UE (see paragraph [0009], “providing one or more indications of ML model support information to a network node, describing the ML model available at the UE”, and see paragraph [0068], “the term “available model” may refer to a model that is implemented, stored, or downloaded in the UE” [i.e., “ML model support info” includes information about models already available at the UE and “available model” includes AI/ML models that already reside at the UE through implementation, installation or storage),
wherein the one or more AI/ML models are received based at least in part on the indication of the one or more available AI/ML models (see paragraph [0090], “the UE receives a message assigning at least one ML-model ID to at least one ML model (which may also be received in the same message or may be previously stored at the UE)”.
Regarding claim 5, as discussed above, Ericsson discloses the apparatus of claim 1. Ericsson further discloses wherein the one or more processors are further configured to: receive an indication of mapping of the functionalities to the one or more AI/ML models (see paragraph [0170], “the UE receives a first RRC message indicating the addition of an ML model which has Model-ID X, wherein the message also indicated the assignment of a Model ID-Y (mapping). That first RRC message is of a first type (e.g., RRC Reconfiguration)”).
Regarding claim 9, as discussed above, Ericsson discloses the apparatus of claim 1. Ericsson further discloses wherein the one or more processors, to receive the one or more AI/ML models, are further configured to receive one or more of: full AI/ML models, (see paragraph [0103], “the UE receives a model version from a central node”, see paragraph [0107], “the first entity is a central repository or a central node…the second entity is a UE, where the UE receives the model version A0 from the central repository and trains with a local data set to arrive at model version A1”)
partial AI/ML models (see paragraph [0117] “signaling the ML model identifier to the UE comprises sending a version configuration based at least in part on the one or more model version indicators”),
updates to available AI/ML models (see paragraph [0060] “when the UE has obtained an updated model for a certain functionality…a new model ID is provided to distinguish the new version from the previous version”),
or an indication of one or more parameters for AI/ML models (see paragraph [0047], “The model type refers to a ML model for a certain use case or functionality, with a given set of hyperparameters and model parameters”).
Regarding independent claim 14, Ericsson discloses the invention as claimed including an apparatus for wireless communication at a network node, comprising: one or more memories; and one or more processors, coupled to the one or more memories (see paragraph [0146], “the network node 3300 includes a processing circuitry 3302, a memory 3304” [i.e., “processing circuitry” being a processor] [support found in Figure M2 of provisional ‘967]), configured to:
receive an indication of functionalities associated with artificial intelligence or machine learning (AI/ML) models supported by a user equipment (UE) (see paragraph [0116] “Step 910 is obtaining one or more indications of ML support information from a UE” [i.e., “model support information from a UE” being functionalities of AI/ML models supported by the UE] [support found in paragraph [0074] in provisional ‘967]);
and transmit one or more AI/ML models associated with the functionalities (see paragraph [0010], “determining a ML model identifier based at least in part on the one or more indications of ML model support information…signaling the ML model identifier to the UE”, see paragraph [0110], “central node is a network node”, see e.g., paragraph [0103], “UE receives a model version from a central node…UE receives one or more versions from the central node…UE may receive different version(s) from the central node” [i.e., “version nodes” being AI/ML models and UE receiving the models implies that the central node is transmitting the models] [support found in paragraphs [0102] and [0116] in provisional ‘967]).
Regarding claim 16, as discussed above, Ericsson discloses the apparatus of claim 14.
Ericsson further discloses receive the indication of the mapping from the UE, or receive the indication of the mapping from a device associated with the UE (see paragraph [0156], “the coordination node receives from a gNB model version info reported by a UE served by the gNB. The coordination node determines whether this version info has been present before and is present in the database. If found, the coordination node retrieves the model ID assigned to the model and passes it to the gNB” [i.e., the indication of mapping by a “coordination node”]).
Regarding claim 17, as discussed above, Ericsson discloses the apparatus of claim 14. Ericsson further discloses receiving an indication of one or more available AI/ML models that are already available at the UE (see paragraph [0065], “providing a ML model support info…to a NW node, describing the ML model available at the UE”, see paragraph [0068], “the term “available model” may refer to a model that is implemented, stored, or downloaded in the UE” [i.e., “ML model support info” includes information about models already available at the UE and “available model” includes AI/ML models that already reside at the UE through implementation, installation or storage),
wherein receiving the one or more AI/ML models is based at least in part on the indication of the one or more available AI/ML models (see paragraph [0090], “At the network side, the configuration of an ML model (associated to the ML-model ID the UE is being assigned) may be based on reported UE support related to AI/ML” [i.e., the network node signals a model ID or “the configuration of an ML model” to the UE based on the “ML model support info”).
Regarding claim 18, as discussed above, Ericsson discloses the apparatus of claim 14. Ericsson further discloses wherein the one or more processors are further configured to receive an indication of supported AI/ML models associated with the functionalities (see paragraph [0036], “obtaining one or more indications of ML support information from a UE” [i.e., “model support information from a UE” being AI/ML models supported by the UE]),
wherein the one or more AI/ML models are transmitted based at least in part on the indication of the supported AI/ML models (see paragraph [0117], “one or more indications of ML model support information … further comprises transmitting the identified ML model to the UE”).
Regarding claim 19, as discussed above, Ericsson discloses the apparatus of claim 14. Ericsson further discloses wherein the one or more processors are further configured to receive an indication of one or more available AI/ML models that are already available at the UE (see paragraph [0036], “obtaining one or more indications of ML support information from a UE” [i.e., “model support information from a UE” being AI/ML models supported by the UE]),
wherein the one or more AI/ML models are transmitted based at least in part on the indication of the one or more available AI/ML models (see paragraph [0117], “one or more indications of ML model support information … further comprises transmitting the identified ML model to the UE”).
Regarding claim 24, as discussed above, Ericsson discloses the apparatus of claim 14. Ericsson further discloses wherein the one or more processors, to transmit the one or more AI/ML models, are configured to transmit one or more of: full AI/ML models (see paragraph [0103], “the UE receives a model version from a central node”, see paragraph [0107], “the first entity is a central repository or a central node…the second entity is a UE, where the UE receives the model version A0 from the central repository and trains with a local data set to arrive at model version A1”)
partial AI/ML models (see paragraph [0117] “signaling the ML model identifier to the UE comprises sending a version configuration based at least in part on the one or more model version indicators”),
updates to available AI/ML models (see paragraph [0060] “when the UE has obtained an updated model for a certain functionality…a new model ID is provided to distinguish the new version from the previous version”),
or an indication of one or more parameters for AI/ML models (see paragraph [0047], “The model type refers to a ML model for a certain use case or functionality, with a given set of hyperparameters and model parameters”).
Regarding independent claim 29, Ericsson discloses the invention as claimed including a method of wireless communication performed by a user equipment (UE) (see paragraph [0009], “method performed by a UE”), comprising:
transmitting an indication of functionalities associated with artificial intelligence or machine learning (AI/ML) models supported by the UE (see paragraph [0114], “Step 810 is providing one or more indications of ML model support information to a network node that describes one or more ML models available at the UE... Step 820 is obtaining a ML model identifier from the network node that identifies one of the one or more ML models available at the UE.” and see paragraph [0115], “the one or more model indicators refer to an ML model for a given functionality” ”[support found in paragraphs [0025], [0054] and [0020] of provisional ‘967]);
and receiving one or more AI/ML models associated with the functionalities (see paragraph [0057], “Based on the UE report of its ML model support, the network can identify the supported functionality, and accordingly configure the specific ML-model…which subsequently also identifies a functional area…when the model is referred to using its model ID”, and see e.g., paragraph [0115] “method 800 further comprise[s] receiving the identified ML model from a network node.” [support found in paragraph [0059], [0044] and [0102] of provisional application ‘967])
Regarding independent claim 30, Ericsson discloses the invention as claimed including a method of wireless communication performed by a network node (UE) (see paragraph [0010], “method performed by a network node” [support found in paragraph [0116] in provisional ‘967]), comprising:
receiving an indication of functionalities associated with artificial intelligence or machine learning (AI/ML) models supported by a user equipment (UE) (see paragraph [0116] “Step 910 is obtaining one or more indications of ML support information from a UE” and see paragraph [0115], “the one or more model indicators refer to an ML model for a given functionality” [i.e., “model support information from a UE” being functionalities of AI/ML models supported by the UE] [support found in paragraph [0074] in provisional ‘967]),
and transmitting one or more AI/ML models associated with the functionalities (see paragraph [0057], “Based on the UE report of its ML model support, the network can identify the supported functionality, and accordingly configure the specific ML-model…which subsequently also identifies a functional area…when the model is referred to using its model ID”, and see e.g., paragraph [0115] “method 800 further comprise[s] receiving the identified ML model from a network node.” [support found in paragraphs [0102] and [0116] in provisional ‘967]).
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen Larsson et al. (WIPO Publication No. WO2023209577A1, hereinafter Ericsson) in view of Jeon et al (US Patent Publication No. 20220294666, hereinafter Jeon). Ericsson was filed as International application no. PCT/IB2023/053131, on 4/25/2023 and claims priority to U.S. provisional application 63/324,967 filed on 03/29/2022, which is prior to the effective filing date of the instant application, 02/24/2023. Jeon is published on 09/15/2022 and constitutes as prior art under 35 U.S.C 102(a)(1).
Regarding claim 2, as discussed above, Ericsson discloses the apparatus of claim 1. However, Ericsson does not explicitly disclose wherein the one or more processors are further configured to transmit an indication of mapping of the functionalities to the one or more AI/ML models, wherein the one or more AI/ML models are received based at least in part on the mapping of the functionalities to the one or more AI/ML models.
Jeon further discloses wherein the one or more processors are further configured to transmit an indication of mapping of the functionalities to the one or more AI/ML models (see paragraph [0006], “Assistance information generated based on the configuration information is transmitted from the UE to the base station”; see paragraph [0015], “the configuration information may include N indices each corresponding to a different one of the one or more operations … M indices each corresponding to a different one of M predefined ML algorithms and indicating an ML algorithm to be employed for the corresponding operation(s)”; see pg. 7-8, TABLE 1; [i.e., “ML algorithm” being AI/ML model. An index denotes the linkage between the specific operation (functionality) and a specific ML algorithm/model]),
wherein the one or more AI/ML models are received based at least in part on the mapping of the functionalities to the one or more AI/ML models (see paragraph [0112], “part of or all the configuration information can be broadcasted as a part of cell-specific information”, see paragraph [0006], “ML/AI configuration information transmitted from a base station to a UE includes…one or more ML models”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified Ericsson’s ML model support reporting where “a UE provides ML model support info to a NW node, including at least on ML model associated with a certain functionality” (see, Ericsson, paragraph [0033]) with Jeon’s “use case and ML/AI approach [that] can be jointly configured…[where] [t]he configuration information can include one or multiple mode indexes to enable the operations/ uses cases and ML algorithms” (see, Jeon, paragraph [0097]). One of ordinary skill in the art would have been motivated to make this modification as it is “desirable that, when repetitively referencing a model, [that] the associated signaling load [is] low” (see, Ericsson, paragraph [0030]) and for the benefit of “enabl[ing] unambiguous referencing” (see, Ericsson, paragraph [0043]), as suggested by Ericsson.
Regarding claim 15, as discussed above, Ericsson discloses the apparatus of claim 14. However, Ericsson does not explicitly disclose wherein the one or more processors are further configured to receive an indication of mapping of the functionalities to the one or more AI/ML models,
wherein the one or more AI/ML models are transmitted based at least in part on the mapping of the functionalities to the one or more AI/ML models.
In the same field, analogous art Jeon teaches wherein the one or more processors are further configured to receive an indication of mapping of the functionalities to the one or more AI/ML models (see paragraph [0006], “Assistance information generated based on the configuration information is transmitted from the UE to the base station”; see paragraph [0082], “At operation 403, the BS receives assistance information from one or multiple UEs”; see paragraph [0015], “the configuration information may include N indices each corresponding to a different one of the one or more operations … M indices each corresponding to a different one of M predefined ML algorithms and indicating an ML algorithm to be employed for the corresponding operation(s)”; see pg. 7-8, TABLE 1; [i.e., “ML algorithm” being AI/ML model. An index denotes the linkage between the specific operation (functionality) and a specific ML algorithm/model]),
wherein the one or more AI/ML models are transmitted based at least in part on the mapping of the functionalities to the one or more AI/ML models (see paragraph [0006], “ML/AI configuration information transmitted from a base station to a UE”; see paragraph [0114], “part of or all the configuration information can be sent by UE-specific signaling”, see TABLE 3 [i.e., base station sending the algorithm selection/mapping back to UE; “base station” being the network node]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified Ericsson’s ML model support reporting where “a UE provides ML model support info to a NW node, including at least on ML model associated with a certain functionality” (see, Ericsson, paragraph [0033]) with Jeon’s “use case and ML/AI approach [that] can be jointly configured…[where] [t]he configuration information can include one or multiple mode indexes to enable the operations/ uses cases and ML algorithms” (see, Jeon, paragraph [0097]). One of ordinary skill in the art would have been motivated to make this modification as it is “desirable that, when repetitively referencing a model, [that] the associated signaling load [is] low” (see, Ericsson, paragraph [0030]) and for the benefit of “enabl[ing] unambiguous referencing” (see, Ericsson, paragraph [0043]), as suggested by Ericsson.
Regarding claim 20, as discussed above (Claim Rejections - 35 USC § 102), Ericsson discloses the apparatus of claim 14.
Ericsson does not explicitly disclose wherein the one or more processors are further configured to: transmit an indication of mapping of the functionalities to the one or more AI/ML models.
However, in the same field, analogous art Jeon discloses wherein the one or more processors are further configured to: transmit an indication of mapping of the functionalities to the one or more AI/ML models (see e.g., paragraph [0100], “the configuration information can include which AI/ML model or algorithm is to be used for certain operation/use case(s)”, see Fig. 4, 402, shows “The BS sends configuration information to a UE” [i.e., “BS” being base station or network node], and see e.g., paragraph [0101], “the use case and ML/AI approach can be jointly configured. For example, there can be K predefined operation modes, where each mode corresponding to certain operation/use case with certain ML algorithm. One or more modes can be configured. TABLE 1 provides an example of this embodiment, where the configuration information can include one or multiple mode indexes to enable the operations/use cases and ML algorithms” [i.e., “mode” as defined by Jeon refers to a predefined configuration that pairs a telecommunication use case/ functionality with a designated AI/ML algorithm/model and its corresponding key model parameters. “Configuration information” being the indication that is transmitted containing the mapping]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified Ericsson’s ML model support reporting where “a UE provides ML model support info to a NW node, including at least on ML model associated with a certain functionality” (see, Ericsson, paragraph [0033]) with Jeon’s “use case and ML/AI approach [that] can be jointly configured…[where] [t]he configuration information can include one or multiple mode indexes to enable the operations/ uses cases and ML algorithms” (see, Jeon, paragraph [0097]). One of ordinary skill in the art would have been motivated to make this modification to allow the configuration to explicitly “enable the operations/use cases and ML algorithm” via “one or multiple mode indexes” as suggested by Jeon (see e.g., Jeon, paragraph [0097]).
Claims 6-8, 10-13, 21-23 and 25-28 are rejected under 35 U.S.C. 103 as being unpatentable over Chen Larsson et al. (WIPO Publication No. WO2023209577A1, hereinafter Ericsson) in view of Larsson et al. (U.S. Patent Publication No. 20250225435, hereinafter Larsson). Ericsson was filed as International application no. PCT/IB2023/053131, on 4/25/2023 and claims priority to U.S. provisional application 63/324,967 filed on 03/29/2022, which is prior to the effective filing date of the instant application, 02/24/2023. Therefore, Ericsson constitutes as prior art under 35 U.S.C. 102(a)(2). Larsson is a national stage entry (under 35 U.S.C. 371) of International Application PCT/IB2023/053131 on 3/29/2023 and claims priority to U.S. Provisional Application No. 63/324,967, filed on 3/29/2022, which is prior to the earliest effective filing date of the instant application, 02/24/2023. Therefore, Larsson constitutes prior art under 35 U.S.C. 102(a)(2).
Regarding claim 6, as discussed above, Ericsson discloses the apparatus of claim 1. However, Ericsson does not explicitly disclose wherein the one or more processors are further configured to transmit a request for the one or more AI/ML models,
wherein the one or more AI/ML models are received based at least in part on the request for the one or more AI/ML models.
However, in the same field, analogous art Larsson discloses wherein the one or more processors are further configured to transmit a request for the one or more AI/ML models (see paragraph [0005], “a first message that indicates a request to update or reconfigure a functionality in the first node” and see “the first message is a request message or an assistance information message” [i.e., “the first node” being the UE),
wherein the one or more AI/ML models are received based at least in part on the request for the one or more AI/ML models (see paragraph [0116], “Based on receiving the ML-model adjusted message, the network may reply to the UE with a confirmation message indicating that it has received the ML-model adjusted message and/or configure a new updated ML-model for the UE (step 308)”).
Therefore, it would have been obvious to one of ordinary skill in art, before the effective filling date of the claimed invention, to have modified Ericsson’s on-demand model delivery framework, wherein “term "available model" may refer to a model that is implemented, stored, or downloaded in the UE, or that may be downloaded on demand to the UE.” (see, e.g., Ericsson, paragraph [0068]) and wherein the UE method “further comprise[s] receiving the identified ML model from a network node” (see, e.g., Ericsson, [0115]) with Larsson’s request-and-response signaling, wherein the UE sends a “first message that indicates a request to update or reconfigure a functionality…related to an ML-model…[and] receiving, from the second mode, a second message responsive to the first message” (see, e.g., Larsson [0064]). One of ordinary skill in the art would have been motivated to make this combination to achieve “a clear picture for the… network about when and what ML-model the network and the UE update, add, or depreciate…to ensure constant connectivity and a predictable behavior” as suggested by Larsson (see, e.g., Larsson [0013]), and benefit that directly serves the need for “an efficient ML model identification framework to ensure robust and unambiguous model control”, as suggested by Ericsson (see, e.g., Ericsson [0030]).
Regarding claim 7, as discussed above, Ericsson discloses the apparatus of claim 1. Ericsson further discloses wherein a model identifier is used in a functionality for life cycle management (LCM) operations (see paragraph [0043], “The ML model ID framework enables unambiguous referencing of a multitude of ML models implemented or available in a UE for model handling and LCM (life cycle management) purposes.”)
Ericsson does not explicitly disclose wherein the one or more processors, to transmit the request for the one or more AI/ML models, are further configured to transmit one or more of: an indication of model identifiers of the one or more AI/ML models,
or an indication of one or more functionalities associated with the one or more AI/ML models
However, in the same field, analogous art Larsson teaches wherein the one or more processors, to transmit the request for the one or more AI/ML models, are further configured to transmit one or more of: an indication of model identifiers of the one or more AI/ML models (see paragraph [0138-0139], “The information related to which model that is adjusted can be for a certain function on a higher level…These can further be indicated as an ID rather the explicit naming of the functions” can be done by "explicitly indicating the corresponding RRC configuration that the gNB has configured the UE with that is applicable, ID of the model, etc.…If it is the ID of the model, this can be the ID of the model indicated within the UE capabilities, it can be an ID of the configuration assigned by the gNB to identify the particular configuration of ML-model or set of ML-models”),
or an indication of one or more functionalities associated with the one or more AI/ML models (see [0006], “the first message includes: (a) a request for a functionality update. (b) a functionality ID, (c) a functionality area ID characterizing a purpose of the functionality ID including a channel estimation, or a decoding”).
Therefore, it would have been obvious to one of ordinary skill in art, before the effective filling date of the claimed invention, to have modified model-request procedure of Ericsson wherein the ML model ID framework “enables unambiguous referencing of a multitude of ML models implemented or available in a UE for model handling and LCM (life cycle management) purposes” (see e.g., Ericsson [0043]) with request message related to an ML-model functionality of Larsson which includes “(b) functionality ID, (c) a functionality area ID characterizing the purpose of the functionality ID” (see, e.g., Larsson [0067]-[0068]). One of ordinary skill in the art would have been motivated to make this modification for the benefit of ensuring the UE’s request explicitly specifies the exact model identifier of functionality area requiring the update (see e.g., Larsson, paragraph [0006], and see e.g., Ericsson, paragraph [0034]).
Regarding claim 8, as discussed above, Ericsson discloses the apparatus of claim 1 and claim 7. Ericsson does not explicitly disclose explicitly disclose the one or more processors are further configured to receive one or more of: all AI/ML models associated with the one or more functionalities,
AI/ML models associated with the one or more functionalities and supported by the UE,
or AI/ML models associated with the one or more functionalities and unavailable at the UE.
However, in the same field, analogous art Larsson teaches the one or more processors are further configured to receive one or more of: all AI/ML models associated with the one or more functionalities (see paragraph [0140], “The network may respond to the message indicating which ML-model(s) the UE can adjust or just confirming directly that the indicated ML-model(s) can be adjusted (step 502, step 602).”),
AI/ML models associated with the one or more functionalities and supported by the UE (see paragraph [0106], “that order may contain details on how and when the UE adjust the ML-model(s). The specific ML-model(s) may be identified with an ID. Note that it can be a subset of ML-model(s) that the UE requested to adjust.”),
or AI/ML models associated with the one or more functionalities and unavailable at the UE (see paragraph [0102], “the network may request a new ML-model or UE capability message, which may include a complete or partial complete list of the UE capabilities ML-model support from the UE, or it may request information about particular features only.”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the filling date of the claimed invention, to incorporate Ericsson’s model support framework (see e.g., Ericsson, paragraph [0034]) into Larsson’s UE-initiated request mechanism (specifying Model IDs or functionality IDs) (see, e.g., Larsson, paragraph [0005]). One of ordinary skill in the art would have been motivated to make this incorporation to allow UE to request specific models or updates on-demand when local operational conditions change, thereby avoiding unnecessary downlink signaling overhead and satisfies the “need for an improved model info provision solutions from the UE to the NW”, as suggested by Ericsson (see, e.g., Ericsson, paragraph [0030]).
Regarding claim 10, as discussed above, Ericsson discloses the apparatus of claim 1. Ericsson further discloses wherein the one or more processors are further configured to: receive an indication to activate or deactivate a functionality and (see paragraph [0058], “the UE may start to operate the ML-model in question until it is de-configured, or deactivated, or expired. The configuration could also be a two-step mechanism wherein the UE is configured with a specific ML-model, including the Model ID, by a first message, and the ML-model is later activated by a second message”, see paragraph [0100], “the UE receives a first RRC message…that first RRC message is of a first type (e.g., RRC Reconfiguration). Then the UE receives a second message (of the first type or of a different type, e.g., RRC Resume) indicating the release of the mapping, upon which the UE releases the association between the ML-model of Model-ID X and the Model ID-Y”);
activate an associated AI/ML model, deactivating the associated AI/ML model, switching the associated AI/ML model (see paragraph [0099], “UE receives a first RRC message…and a second message…indicating the medication of the mapping or re-configuration of the ML model of Model-ID X)
Ericsson does not explicitly disclose applying a fallback associated with the associated AI/ML model.
However, in the same field, analogous art Larsson teaches applying a fallback associated with the associated AI/ML model (see paragraph [0112], “the UE may fallback to another algorithm instead, either ML or non-ML based”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combined the activation, deactivation and updating elements of Ericsson (see e.g., Ericsson, paragraph [0030]) with the fallback element of Larsson (see, e.g., Larsson, paragraph [0112], lines 7-9). One of ordinary skill in the art would have been motivated to make this modification to avoid a service gap during model adjustment and “the network would schedule with such a [fallback] during the period the model is being adjusted” (see e.g., Larsson, paragraph [0112]).
Regarding claim 11, as discussed above, Ericsson discloses the apparatus of claim 1. Ericsson further discloses wherein the one or more processors are further configured to: receive an indication to deactivate an AI/ML model of the one or more AI/ML models (see paragraph [0100], “the UE receives a second message (of the first type or of a different type, e.g., RRC Resume) indicating the release of the mapping, upon which the UE releases the association between the ML-model of Model-ID X and the Model ID-Y” [i.e., deactivation]);
Ericsson does not explicitly disclose and transmit model identifier information associated with the AI/ML model.
However, in the same analogous art, Larsson teaches and transmit model identifier information associated with the AI/ML model (see paragraphs [0146-0148], “MLi: Is the ML-model ID that is requested to be adjusted. Each MLi is a single bit. A specific value is used to indicate that the ML-model should be adjusted. If ML-model is not adjusted the other bit value is used… Update length: This field give the amount of time needed to adjust the ML-model in some unit, for example SFNs, slots, symbols and soon. It may further give a recommended occasion in time. Update state: This bit field indicates which state the adjustment could occur within.” and see paragraphs [0154-0155], “Within the MAC CE for confirmation ML-model(s) to be adjusted in FIG. 9 the different fields represent the following: MLi: Is the ML-model ID that is confirmed can be adjusted. Each MLi is a single bit. A specific value is used to indicate that the ML-model should adjusted. If ML-model is not adjusted the other bit value is used.” [i.e., ML models are identified by single-bit positions (ML0-ML15) within the CE. “Update state” indicates whether a given identified model should be adjusted]).
Therefore, it would have been obvious to one of ordinary skill in art, before the effective filling date of the claimed invention, to have incorporated the “model ID…refer[ring] to the ML model in subsequent model handling-related signaling” of Ericsson (see e.g., Ericsson, paragraph [0034]) with “UE indicat[ing] to the network (e.g., to a network node) the need of performing a ML-model…deprecation” of Larsson (see e.g., Larsson, paragraph [0062], [i.e., model deactivation]). One of ordinary skill in the art would have been motivated to make this modification for the benefit of “ensur[ing] constant connectivity and predictable behavior...or when the traffic is of a nature that connectivity cannot be lost”, as suggested by Larsson (see, e.g., Larsson, paragraph [0013]).
Regarding claim 12, as discussed above, Ericsson discloses the apparatus of claim 1. Ericsson does not explicitly disclose wherein the one or more processors are further configured to: receive an indication that the AI/ML model has a performance metric that fails to satisfy a threshold;
transmit an updated UE capability based at least in part on the performance metric that fails to satisfy the threshold;
or modify a mapping between functionality and associated models based on updated UE capability signaling.
However, in the same field, analogous art, Larsson discloses wherein the one or more processors are further configured to: receive an indication that the AI/ML model has a performance metric that fails to satisfy a threshold (see paragraph [0038], “This first node can receive a message from a second node indicating that the functionality is not performing correctly, e.g. prediction error is higher than a pre-defined value, error interval is not in acceptable levels, or prediction accuracy is lower than a pre-defined value.” [i.e., performance monitoring]);
transmit an updated UE capability based at least in part on the performance metric that fails to satisfy the threshold (see paragraph [0102] “the network may request a new ML-model or UE capability message, which may include a complete or partial complete list of the UE capabilities ML-model support from the UE, or it may request information about particular features only. The UE would then respond with UE capability information according to the request.”);
or modify a mapping between functionality and associated models based on updated UE capability signaling (see paragraph [0118], “the UE can request to be configured with an ML-model adjustment gap.”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate “the long model ID [enabling] aggregating model performance and other information…for improves data drift and other performance tracking” of Ericsson (see e.g., Ericsson, paragraph [0043]) with the error threshold indication in cases like “predication error is high than pre-defined value, error interval is not in acceptable levels” of Larsson (see e.g., Larsson, paragraph [0038]). One of the ordinary skill in the art would have been motivated to make this incorporation to ensure that the network and UE maintain accurate operational state awareness and see the “UE perform measurements…what triggers measurement reports”, as suggested by Larsson (see e.g., Larsson, paragraph [0095]).
Regarding claim 13, as discussed above, Ericsson discloses the apparatus of claim 1. Ericsson does not explicitly disclose wherein the one or more processors are further configured to: detect that an AI/ML model has a performance metric that fails to satisfy a threshold;
modify a mapping between functionality and associated models;
and transmit an updated UE capability based at least in part on the performance metric that fails to satisfy the threshold.
However, Larsson discloses wherein the one or more processors are further configured to: detect that an AI/ML model has a performance metric that fails to satisfy a threshold (see paragraphs [0058-0059], “ Data and Model Monitoring: Data & model monitoring refers to validating that the inference data are from a distribution that aligns well with the training data, as well as monitoring model outputs for detecting any performance, or operational, drifts. A drift detection stage that informs about any drifts in the model operations”);
modify a mapping between functionality and associated models (see paragraph [0118], “the UE can request to be configured with an ML-model adjustment gap.”);
and transmit an updated UE capability based at least in part on the performance metric that fails to satisfy the threshold (see paragraph [0121], “the UE sends an RRC message (e.g., a request, or indication) to the network indicating that it wants or needs to adjust or update the ML-model… The information in which state the UE is able to adjust the model … may be associated to a UE capability which is reported as part of the UE capability signaling from the UE to the network”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate “the long model ID [enabling] aggregating model performance and other information…for improves data drift and other performance tracking” of Ericsson (see e.g., Ericsson, paragraph [0043]) with the error threshold indication in cases like “predication error is high than pre-defined value, error interval is not in acceptable levels” of Larsson (see e.g., Larsson, paragraph [0038]). One of the ordinary skill in the art would have been motivated to make this incorporation to ensure that the network and UE maintain accurate operational state awareness, notify the network, and see the “UE perform measurements…what triggers measurement reports”, as suggested by Larsson (see e.g., Larsson, paragraph [0095]).
Regarding claim 21, as discussed above, Ericsson discloses the apparatus of claim 14. However, Ericsson does not explicitly disclose wherein the one or more processors are further configured to receive a request for the one or more AI/ML models,
wherein the one or more AI/ML models are transmitted based at least in part on the request for the one or more AI/ML models.
However, in the same field, analogous art, Larsson discloses wherein the one or more processors are further configured to receive a request for the one or more AI/ML models (see paragraph [0062] “In one embodiment, a UE indicates to the network (e.g., to a network node) the need of performing an ML-model update and/or deprecation. The network sends another message to the UE, based on which the UE updates and/or depreciates its ML-model.”; see paragraph [0121] “the UE sends an RRC message (e.g., a request, or indication) to the network indicating that it wants or needs to adjust or update the ML-model (step 500, step 600).”; see paragraph [0007] “the first message is a request message or an assistance information message”),
wherein the one or more AI/ML models are transmitted based at least in part on the request for the one or more AI/ML models (see paragraph [0116], “Based on receiving the ML-model adjusted message, the network may reply to the UE with a confirmation message indicating that it has received the ML-model adjusted message and/or configure a new updated ML-model for the UE (step 308)”).
Therefore, it would have been obvious to one of ordinary skill in art, before the effective filling date of the claimed invention, to have modified Ericsson’s on-demand model delivery framework, wherein “term "available model" may refer to a model that is implemented, stored, or downloaded in the UE, or that may be downloaded on demand to the UE.” (see, e.g., Ericsson, paragraph [0068]) and wherein the UE method “further comprise[s] receiving the identified ML model from a network node” (see, e.g., Ericsson, paragraph [0115]) with Larsson’s request-and-response signaling, wherein the UE sends a “first message that indicates a request to update or reconfigure a functionality…related to an ML-model…[and] receiving, from the second mode, a second message responsive to the first message” (see, e.g., Larsson [0064]). One of ordinary skill in the art would have been motivated to make this combination to achieve “a clear picture for the… network about when and what ML-model the network and the UE update, add, or depreciate…to ensure constant connectivity and a predictable behavior”, as suggested by Larsson (see, e.g., Larsson paragraph [0013]), and benefit that directly serves the need for “an efficient ML model identification framework to ensure robust and unambiguous model control”, as suggested by Ericsson (see, e.g., Ericsson paragraph [0030]).
Regarding claim 22, as discussed above, Ericsson discloses the apparatus of claim 14. Ericsson further discloses wherein a model identifier is used in a functionality for life cycle management (LCM) operations (see paragraph [0043], “The ML model ID framework enables unambiguous referencing of a multitude of ML models implemented or available in a UE for model handling and LCM (life cycle management) purposes.”),
Ericsson does not explicitly disclose wherein the one or more processors, to receive the request for the one or more AI/ML models, are configured to receive one or more of: an indication of model identifiers of the one or more AI/ML models,
or an indication of one or more functionalities associated with the one or more AI/ML models.
However, in the same field, analogous art Larsson teaches wherein the one or more processors, to receive the request for the one or more AI/ML models, are configured to receive one or more of: an indication of model identifiers of the one or more AI/ML models (see paragraphs [0138-0139], “The information related to which model that is adjusted can be for a certain function on a higher level…These can further be indicated as an ID rather the explicit naming of the functions” can be done by "explicitly indicating the corresponding RRC configuration that the gNB has configured the UE with that is applicable, ID of the model, etc.…If it is the ID of the model, this can be the ID of the model indicated within the UE capabilities, it can be an ID of the configuration assigned by the gNB to identify the particular configuration of ML-model or set of ML-models”),
or an indication of one or more functionalities associated with the one or more AI/ML models (see paragraph [0006], “the first message includes: (a) a request for a functionality update. (b) a functionality ID, (c) a functionality area ID characterizing a purpose of the functionality ID including a channel estimation, or a decoding”).
Therefore, it would have been obvious to one of ordinary skill in art, before the effective filling date of the claimed invention, to have modified model-request procedure of Ericsson wherein the ML model ID framework “enables unambiguous referencing of a multitude of ML models implemented or available in a UE for model handling and LCM (life cycle management) purposes” (see e.g., Ericsson [0043]) with request message related to an ML-model functionality of Larsson which includes “(b) functionality ID, (c) a functionality area ID characterizing the purpose of the functionality ID” (see, e.g., Larsson [0067]-[0068]). One of ordinary skill in the art would have been motivated to make this modification for the benefit of ensuring the UE’s request explicitly specifies the exact model identifier of functionality area requiring the update (see e.g., Larsson, paragraph [0006], and see e.g., Ericsson, paragraph [0034]).
Regarding claim 23, the claim recites the phrase “receive transmit”, which appears to be a typographical error1. For the purposes of compact prosecution, this phrase is interpreted as “transmit” and has been examined accordingly. As discussed above, Ericsson discloses the apparatus of claim 14. Ericsson further discloses all AI/ML models associated with the one or more functionalities (see e.g., paragraph [0103], “UE receives one or more [model] versions from a central node”),
AI/ML models associated with the one or more functionalities and supported by the UE (see e.g., paragraph [0009], “obtaining a ML model identifier from the network node that identified one of the one more ML models available at the UE”; see paragraph [0068], “the term ‘available model’ may refer to a model…that may be downloaded on demand to the UE” [i.e., “available model” as defined by Ericsson, may also refer to AI/ML models that are to be received/ downloaded by the UE in addition to indicate AI/ML models that already reside at the UE] and see paragraph [0055], “the model capability or availability signaling for one or more supported models, also referred to as model support,”).,
Ericsson does not explicitly disclose wherein the one or more processors are further configured to receive transmit one or more of:
or AI/ML models associated with the one or more functionalities and unavailable at the UE.
However, in the same field, analogous art Larsson teaches wherein the one or more processors are further configured to receive transmit one or more of (see e.g., paragraph [0244], “from the second node, a second message responsive to the first message” [i.e., “second node” being the network node]):
or AI/ML models associated with the one or more functionalities and unavailable at the UE (see paragraph [0136], “The UE determines that based on the fact that it has downloaded an update of the ML-model” and see e.g., paragraph [0053], “deployment stage to make the trained (or re-trained AI model part of the inference pipeline.” [i.e., providing models or updates that the UE needs but are currently unavailable/outdated]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the filling date of the claimed invention, to incorporate Ericsson’s model support framework (see e.g., Ericsson, paragraph [0034]) into Larsson’s UE-initiated request mechanism (specifying Model IDs or functionality IDs) (see, e.g., Larsson, paragraph [0005]). One of ordinary skill in the art would have been motivated to make this incorporation to allow UE to request specific models or updates on-demand when local operational conditions change, thereby avoiding unnecessary downlink signaling overhead and satisfies the “need for an improved model info provision solutions from the UE to the NW”, as suggested by Ericsson (see, e.g., Ericsson, paragraph [0030]).
Regarding claim 25, as discussed above, Ericsson discloses the apparatus of claim 14. Ericsson further discloses wherein the one or more processors are further configured to: transmit an indication to deactivate an AI/ML model of the one or more AI/ML models (see paragraph [0100], “the UE receives a second message (of the first type or of a different type, e.g., RRC Resume) indicating the release of the mapping, upon which the UE releases the association between the ML-model of Model-ID X and the Model ID-Y” [i.e., “release of the mapping” being deactivation and network node transmits the second message to the UE]);
Ericsson does not explicitly disclose and receive model identifier information associated with the AI/ML model (see paragraph [0100], “the UE receives a second message (of the first type or of a different type, e.g., RRC Resume) indicating the release of the mapping, upon which the UE releases the association between the ML-model of Model-ID X and the Model ID-Y” [i.e., deactivation]).
However, in the same analogous art, Larsson teaches and receive model identifier information associated with the AI/ML model (see paragraphs [0146-0148], “MLi: Is the ML-model ID that is requested to be adjusted. Each MLi is a single bit. A specific value is used to indicate that the ML-model should be adjusted. If ML-model is not adjusted the other bit value is used… Update length: This field give the amount of time needed to adjust the ML-model in some unit, for example SFNs, slots, symbols and soon. It may further give a recommended occasion in time. Update state: This bit field indicates which state the adjustment could occur within.” See paragraphs [0154-0155], “Within the MAC CE for confirmation ML-model(s) to be adjusted in FIG. 9 the different fields represent the following: MLi: Is the ML-model ID that is confirmed can be adjusted. Each MLi is a single bit. A specific value is used to indicate that the ML-model should adjusted. If ML-model is not adjusted the other bit value is used.” [i.e., ML models are identified by single-bit positions (ML0-ML15) within the CE. “Update state” indicates whether a given identified model should be adjusted]).
Therefore, it would have been obvious to one of ordinary skill in art, before the effective filling date of the claimed invention, to have incorporated the “model ID…refer[ring] to the ML model in subsequent model handling-related signaling” of Ericsson (see e.g., Ericsson, paragraph [0034]) with “UE indicat[ing] to the network (e.g., to a network node) the need of performing a ML-model…deprecation” of Larsson (see e.g., Larsson, paragraph [0062], [i.e., model deactivation]). One of ordinary skill in the art would have been motivated to make this modification for the benefit of “ensur[ing] constant connectivity and predictable behavior...or when the traffic is of a nature that connectivity cannot be lost”, as suggested by Larsson (see, e.g., Larsson, paragraph [0013]).
Regarding claim 26, as discussed above, Ericsson discloses the apparatus of claim 14. Ericsson does not explicitly disclose wherein the one or more processors are further configured to: transmit an indication that the AI/ML model has a performance metric that fails to satisfy a threshold;
receive an updated UE capability based at least in part on the performance metric that fails to satisfy the threshold;
or modify a mapping between functionality and associated models based on updated UE capability signaling.
However, in the same field, analogous art, Larsson teaches wherein the one or more processors are further configured to: transmit an indication that the AI/ML model has a performance metric that fails to satisfy a threshold; (see paragraph [0038], “This first node can receive a message from a second node indicating that the functionality is not performing correctly, e.g. prediction error is higher than a pre-defined value, error interval is not in acceptable levels, or prediction accuracy is lower than a pre-defined value.” [i.e., performance monitoring]);
receive an updated UE capability based at least in part on the performance metric that fails to satisfy the threshold (see paragraph [0102] “the network may request a new ML-model or UE capability message, which may include a complete or partial complete list of the UE capabilities ML-model support from the UE, or it may request information about particular features only. The UE would then respond with UE capability information according to the request.”);
or modify a mapping between functionality and associated models based on updated UE capability signaling (see paragraph [0118], “the UE can request to be configured with an ML-model adjustment gap.”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate “the long model ID [enabling] aggregating model performance and other information…for improves data drift and other performance tracking” of Ericsson (see e.g., Ericsson, paragraph [0043]) with the error threshold indication in cases like “predication error is high than pre-defined value, error interval is not in acceptable levels” of Larsson (see e.g., Larsson, paragraph [0038]). One of the ordinary skill in the art would have been motivated to make this incorporation to ensure that the network and UE maintain accurate operational state awareness and see the “UE perform measurements…what triggers measurement reports”, as suggested by Larsson (see e.g., Larsson, paragraph [0095]).
Regarding claim 27, as discussed above, Ericsson discloses the apparatus of claim 14. Ericsson does not explicitly disclose wherein the one or more processors are further configured to: receive an updated UE capability based at least in part on a performance metric that fails to satisfy a threshold transmit an updated UE capability based at least in part on the performance metric that fails to satisfy the threshold.
However, in the same field, analogous art, Larsson teaches wherein the one or more processors are further configured to: receive an updated UE capability based at least in part on a performance metric that fails to satisfy a threshold transmit an updated UE capability based at least in part on the performance metric that fails to satisfy the threshold (see paragraph [0102] “the network may request a new ML-model or UE capability message, which may include a complete or partial complete list of the UE capabilities ML-model support from the UE, or it may request information about particular features only. The UE would then respond with UE capability information according to the request.”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate “the long model ID [enabling] aggregating model performance and other information…for improves data drift and other performance tracking” of Ericsson (see e.g., Ericsson, paragraph [0043]) with the error threshold indication in cases like “predication error is high than pre-defined value, error interval is not in acceptable levels” of Larsson (see e.g., Larsson, paragraph [0038]). One of the ordinary skill in the art would have been motivated to make this incorporation to ensure that the network and UE maintain accurate operational state awareness, notify the network, and see the “UE perform measurements…what triggers measurement reports”, as suggested by Larsson (see e.g., Larsson, paragraph [0095]).
Regarding claim 28, as discussed above, Ericsson discloses the apparatus of claim 14. Ericsson does not explicitly disclose wherein the one or more processors are further configured to: modify a mapping between functionality and associated models
However, in the same field, analogous art, Larsson teaches wherein the one or more processors are further configured to: modify a mapping between functionality and associated models (see paragraph [0118], “the UE can request to be configured with an ML-model adjustment gap.”).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate “the long model ID [enabling] aggregating model performance and other information…for improves data drift and other performance tracking” of Ericsson (see e.g., Ericsson, paragraph [0043]) with the error threshold indication in cases like “predication error is high than pre-defined value, error interval is not in acceptable levels” of Larsson (see e.g., Larsson, paragraph [0038]). One of the ordinary skill in the art would have been motivated to make this incorporation to ensure that the network and UE maintain accurate operational state awareness, notify the network, and see the “UE perform measurements…what triggers measurement reports”, as suggested by Larsson (see e.g., Larsson, paragraph [0095]).
Conclusion
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/RB/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
1 See Claim Objections section.
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