CTNF 18/598,604 CTNF 82841 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement (IDS) submitted on 07/30/2025 is considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-12-aia AIA (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. 07-15-03-aia AIA Claim (s) 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lutchoomun et al. (US 2026/0105363; hereinafter Lutchoomun) . Regarding claim 1, Lutchoomun shows a method (Figures 2-4 shows the disclosed method performed in part by a WTRU.) of wireless communication performed by a user equipment (UE), comprising: collecting a dataset during a data collection session associated with an artificial intelligence or machine learning (AI/ML) air interface use case (Figure 4; Par. 0072, 0076, 0138-0142, 0152, 0215-0216; The WTRU receives configuration information comprising information on one or more AIML lifecycle management (LCM) stages of an AIML model. The WTRU may be configured to determine a configuration of registered AIML models for inference.); receiving, from a network node, a dataset identifier assigned to the data collection session associated with the AI/ML air interface use case (Figures 3-4; Par. 0138-0142, 0152, 0215-0216; the WTRU receives configuration information comprising information on LCM stage reporting identification (ID) granularities for the one or more AIML LCM.); and associating the dataset identifier with the dataset and with one or more configurations associated with the UE during the data collection session (Figures 3-4; Par. 0138-0142, 0146-0147, 0152, 0215-0216; the WTRU receives configuration information comprising information on LCM stage reporting identification (ID) granularities for the one or more AIML LCM. The WTRU may receive an indication from the gNB/network on the list of acquirable models. For example, the gNB may have information from the WTRU vendor about a model that is not yet available at the WTRU. The WTRU may receive a list of global IDs associated with the acquirable models.). Regarding claim 2, Lutchoomun shows transmitting, to the network node, a request to assign the dataset identifier to the data collection session associated with the AI/ML air interface use case, wherein the dataset identifier is received from the network node in response to the request (Par. 0152-0153, 0159-0160; The WTRU may determine to activate and/or deactivate an AIML model and send an indication to the gNB (e.g., via MAC CE). For example, a WTRU may determine to activate if the WTRU determines (e.g., through an indication from the gNB) that gNB may have an AIML model that may be able to provide better performance compared to legacy RAN procedure, the WTRU subsequently requests for model transfer/download from the gNB. The MAC CE may be a bitmap field in which activation is indicated by 1 and deactivation is indicated by 0. The specific bitmap location may be pre-configured (assigned) to a specific model ID.). Regarding claim 3, Lutchoomun shows associating the dataset identifier with one or more radio statistics associated with a channel between the UE and the network node during the data collection session (Par. 0128, 0133; The identity associated with RAN function or use case may include, for example, different logical identifiers that may be assigned to CSI feedback, beam management, positioning, mobility management, and the like. A mapping table at the gNB may assign different functions a unique ID. For example, CSI prediction may correspond to ID #1, beam prediction may correspond to ID #2, etc. The gNB may share the mapping table with the WTRU (e.g., semi-statically in RRC configuration/reconfiguration).). Regarding claim 4, Lutchoomun shows training one or more AI/ML models using the dataset; and associating the dataset identifier with the one or more AI/ML models (Par. 0136; the model ID may be associated with a binary model that may be compiled and optimized to specific WTRU hardware. In one or more other cases, the model ID may be associated with a model structure and/or parameters. In another example, the logical model may refer to the dataset used to train a model.). Regarding claim 5, Lutchoomun shows transmitting, to the network node, information that indicates support for one or more UE-side features associated with the AI/ML air interface use case, wherein the support is valid for a set of attributes associated with the dataset identifier (Figures 3-4; Par. 0096, 0206; the WTRU transmits a capability indicator to the gNB. The capability indicator may include the AIML capabilities of the WTRU. The AIML model registration procedure may include the WTRU reporting its AIML capability, including support for different RAN functions for which AIML models may be supported by the WTRU, the number of AIML models supported, AIML processing capability, and the like. In another example, the AIML model registration procedure may include AIML model identity assignment or AIML model ID space configuration.); receiving, from the network node, signaling to apply one or more lifecycle management (LCM) actions for the one or more UE-side features associated with the AI/ML air interface use case (Figures 3-4; Par. 0096, 0143; The WTRU may be configured to determine the identity of the AIML model based on explicit signaling. The WTRU may be configured to apply the identity of the AIML model based on explicit signaling. The WTRU may be configured to apply the identity of the ML model based on explicit signaling by the gNB. The WTRU may be configured to apply the identity of the ML model based on explicit signaling during a training procedure. The WTRU may be configured to apply the identity of the AIML model based on explicit signaling during one or more steps.); and applying the one or more LCM actions for the one or more UE-side features associated with the AI/ML air interface use case in accordance with the set of attributes associated with the dataset identifier (Figures 3-4; Par. 0096, 0143; The WTRU may be configured to determine the identity of the AIML model based on explicit signaling. The WTRU may be configured to apply the identity of the AIML model based on explicit signaling. The WTRU may be configured to apply the identity of the ML model based on explicit signaling by the gNB. The WTRU may be configured to apply the identity of the ML model based on explicit signaling during a training procedure. The WTRU may be configured to apply the identity of the AIML model based on explicit signaling during one or more steps.). Regarding claim 6, Lutchoomun shows wherein the support for the one or more UE-side features is indicated in a UE capability message (Figure 3; Par. 0206; the WTRU transmits a capability indicator to the gNB.) or uplink control information (UCI). Regarding claim 7, Lutchoomun shows receiving, from the network node, information that indicates support for one or more network-side features associated with the AI/ML air interface use case, wherein the support is valid for a set of attributes associated with the dataset identifier (Par. 0115-0118; The WTRU and gNB may jointly perform the model verification. For example, the WTRU and gNB may perform model verification jointly via a signaling exchange to verify that the models at the WTRU and gNB are ready. The model verification may involve one or more of integrity, compatibility, and/or interoperability.); and applying one or more lifecycle management (LCM) actions for one or more UE-side features associated with the AI/ML air interface use case in accordance with the set of attributes associated with the dataset identifier (Par. 0098, 0115-0118; the AIML model registration procedure may include AIML model identity assignment or AIML model ID space configuration, in which the WTRU and/or the NW may address an AIML model without ambiguity during activation/deactivation, performance monitoring, training, etc. For example, the AIML model registration procedure may include AIML model verification, in which the WTRU may verify the integrity, compatibility, and/or applicability of AIML models.). Regarding claim 8, Lutchoomun shows wherein the support is indicated in a medium access control (MAC) control element (MAC-CE) (Par. 0097; The different parts of the registration procedure may be executed based on signaling at different protocol layers (e.g., a WTRU capability transmission via RRC messages and model activation/deactivation using MAC CE).), a downlink control information (DCI) message, or a broadcast message. Regarding claim 9, Lutchoomun shows receiving, from the network node, a message to indicate that the dataset identifier is no longer valid for the AI/ML air interface use case (Par. 0190-0191; The WTRU may be configured to update a AIML model after a joint WTRU-gNB training. The WTRU may update its AIML model based on joint training with a peer AIML model at the gNB. If the joint training is successful, the WTRU may receive an updated model identity from the gNB. The WTRU may be configured to reuse the same model ID used before the joint training. The WTRU may be configured to delete the old AIML model if the model ID remains the same before and after the joint training.). Regarding claim 10, Lutchoomun shows receiving, from the network node, a message to modify a set of attributes associated with the dataset identifier (Par. 0190-0191; The WTRU may be configured to update a AIML model after a joint WTRU-gNB training. The WTRU may update its AIML model based on joint training with a peer AIML model at the gNB. If the joint training is successful, the WTRU may receive an updated model identity from the gNB. The WTRU may be configured to reuse the same model ID used before the joint training. The WTRU may be configured to delete the old AIML model if the model ID remains the same before and after the joint training.). Regarding claim 11, Lutchoomun shows transmitting, to the network node, a message to indicate that the dataset identifier is no longer valid for the AI/ML air interface use case (Par. 0153; The WTRU may determine to activate and/or deactivate an AIML model and send an indication to the gNB (e.g., via MAC CE).). Regarding claim 12, Lutchoomun shows transmitting, to the network node, a message to modify a set of attributes associated with the dataset identifier (Par. 0153; The WTRU may determine to activate and/or deactivate an AIML model and send an indication to the gNB (e.g., via MAC CE).). Regarding claim 13, Lutchoomun shows a user equipment (UE) (Figure 2 shows a WTRU performing the disclosed method of Figures 2-4.) for wireless communication, comprising: one or more memories (Figure 2 shows one or more memories.); and one or more processors, coupled to the one or more memories (Figure 2 shows a processor coupled to the one or more memories.), configured to cause the UE to: collect a dataset during a data collection session associated with an artificial intelligence or machine learning (AI/ML) air interface use case (Figure 4; Par. 0072, 0076, 0138-0142, 0152, 0215-0216; The WTRU receives configuration information comprising information on one or more AIML lifecycle management (LCM) stages of an AIML model. The WTRU may be configured to determine a configuration of registered AIML models for inference.); receive, from a network node, a dataset identifier assigned to the data collection session associated with the AI/ML air interface use case (Figures 3-4; Par. 0138-0142, 0152, 0215-0216; the WTRU receives configuration information comprising information on LCM stage reporting identification (ID) granularities for the one or more AIML LCM.); and associate the dataset identifier with the dataset and with one or more configurations associated with the UE during the data collection session (Figures 3-4; Par. 0138-0142, 0146-0147, 0152, 0215-0216; the WTRU receives configuration information comprising information on LCM stage reporting identification (ID) granularities for the one or more AIML LCM. The WTRU may receive an indication from the gNB/network on the list of acquirable models. For example, the gNB may have information from the WTRU vendor about a model that is not yet available at the WTRU. The WTRU may receive a list of global IDs associated with the acquirable models.). Regarding claims 14, 15, 16 and 17, these claims are rejected based on the same reasoning as presented in the rejection of claims 2, 3, 4 and 7, respectively. Regarding claim 18, Lutchoomun shows wherein the one or more processors are further configured to cause the UE to: receive, from the network node, one or more of a message to indicate that the dataset identifier is no longer valid for the AI/ML air interface use case (Par. 0190-0191; The WTRU may be configured to update a AIML model after a joint WTRU-gNB training. The WTRU may update its AIML model based on joint training with a peer AIML model at the gNB. If the joint training is successful, the WTRU may receive an updated model identity from the gNB. The WTRU may be configured to reuse the same model ID used before the joint training. The WTRU may be configured to delete the old AIML model if the model ID remains the same before and after the joint training.) or a message to modify a set of attributes associated with the dataset identifier (Par. 0190-0191; The WTRU may be configured to update a AIML model after a joint WTRU-gNB training. The WTRU may update its AIML model based on joint training with a peer AIML model at the gNB. If the joint training is successful, the WTRU may receive an updated model identity from the gNB. The WTRU may be configured to reuse the same model ID used before the joint training. The WTRU may be configured to delete the old AIML model if the model ID remains the same before and after the joint training.). Regarding claim 19, Lutchoomun shows wherein the one or more processors are further configured to cause the UE to: transmit, to the network node, one or more of a message to indicate that the dataset identifier is no longer valid for the AI/ML air interface use case (Par. 0153; The WTRU may determine to activate and/or deactivate an AIML model and send an indication to the gNB (e.g., via MAC CE).) or a message to modify a set of attributes associated with the dataset identifier (Par. 0153; The WTRU may determine to activate and/or deactivate an AIML model and send an indication to the gNB (e.g., via MAC CE).). Regarding claim 20, Lutchoomun shows an apparatus (Figure 2 shows a WTRU performing the disclosed method of Figures 2-4.) for wireless communication, comprising: means for collecting a dataset during a data collection session associated with an artificial intelligence or machine learning (AI/ML) air interface use case (Figure 4; Par. 0072, 0076, 0138-0142, 0152, 0215-0216; The WTRU receives configuration information comprising information on one or more AIML lifecycle management (LCM) stages of an AIML model. The WTRU may be configured to determine a configuration of registered AIML models for inference.); means for receiving, from a network node, a dataset identifier assigned to the data collection session associated with the AI/ML air interface use case (Figures 3-4; Par. 0138-0142, 0152, 0215-0216; the WTRU receives configuration information comprising information on LCM stage reporting identification (ID) granularities for the one or more AIML LCM.); and means for associating the dataset identifier with the dataset and with one or more configurations associated with the UE during the data collection session (Figures 3-4; Par. 0138-0142, 0146-0147, 0152, 0215-0216; the WTRU receives configuration information comprising information on LCM stage reporting identification (ID) granularities for the one or more AIML LCM. The WTRU may receive an indication from the gNB/network on the list of acquirable models. For example, the gNB may have information from the WTRU vendor about a model that is not yet available at the WTRU. The WTRU may receive a list of global IDs associated with the acquirable models.) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20260128958 A1 - AI/ML Models in Wireless Communication Networks US 20260046218 A1 - METHOD OF ARTIFICIAL INTELLIGENCE-ASSISTED CONFIGURATION IN WIRELESS COMMUNICATION SYSTEM US 20260040096 A1 - METHOD AND APPARATUS FOR AI MODEL DEFINITION AND AI MODEL TRANSFER US 20250330392 A1 - MODEL INFORMATION TRANSMISSION METHOD AND APPARATUS, AND DEVICE US 20250056330 A1 - METHOD AND APPARATUS FOR PROACTIVE COMMUNICATION OF RESOURCE MAPPINGS TO NETWORK ELEMENT IMPLEMENTATIONS FOR PERFORMING A TASK US 20240370760 A1 - METHOD AND SYSTEM OF MANAGING ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AI/ML) MODEL US 20240295625 A1 - METHODS AND APPARATUS FOR TRAINING BASED POSITIONING IN WIRELESS COMMUNICATION SYSTEMS US 20240284199 A1 - METHODS AND APPARATUS OF GENERAL FRAMEWORK FOR MODEL/FUNCTIONALITY IDENTIFICATION US 20240275519 A1 – TRAINING NETWORK-BASED DECODERS OF USER EQUIPMENT CHANNEL STATE INFORMATION FEEDBACK US 20240265306 A1 - NETWORK-USER EQUIPMENT (UE) COLLABORATION LEVELS FOR ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AI/ML) OPERATION US 20220104055 A1 - RAN INITIATED DATA COLLECTION Any inquiry concerning this communication or earlier communications from the examiner should be directed to REDENTOR M PASIA whose telephone number is (571)272-9745. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /REDENTOR PASIA/Primary Examiner, Art Unit 2413 Application/Control Number: 18/598,604 Page 2 Art Unit: 2413 Application/Control Number: 18/598,604 Page 3 Art Unit: 2413 Application/Control Number: 18/598,604 Page 4 Art Unit: 2413 Application/Control Number: 18/598,604 Page 5 Art Unit: 2413 Application/Control Number: 18/598,604 Page 6 Art Unit: 2413 Application/Control Number: 18/598,604 Page 7 Art Unit: 2413 Application/Control Number: 18/598,604 Page 8 Art Unit: 2413 Application/Control Number: 18/598,604 Page 9 Art Unit: 2413 Application/Control Number: 18/598,604 Page 10 Art Unit: 2413 Application/Control Number: 18/598,604 Page 11 Art Unit: 2413 Application/Control Number: 18/598,604 Page 12 Art Unit: 2413 Application/Control Number: 18/598,604 Page 13 Art Unit: 2413 Application/Control Number: 18/598,604 Page 14 Art Unit: 2413
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