CTNF 18/749,037 CTNF 98881 DETAILED ACTION This Non-Final Office Action is in response to application number 18/749,037 filed on June 20 th 2024. 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 Statements The information disclosure statements (IDS), submitted on September 16 th , 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Claim Rejections – 35 USC § 102 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 AIA Claim s 1-2, 4-7,9-11 and 20-23 are rejected under 35 U.S.C. 102( a ) as being anticipated by Wang et al. (US 20240356817 A1) . Regarding claims 1,21 and 23, Wang et al. disclose a communication method in a wireless network, comprising at a first station: transmitting, to a second station, a management frame including artificial intelligence and machine learning, AIML, profile information defining a profile of AIML capabilities at the first station, wherein the AIML profile information is based on pre-defined AIML profiles or sub-profiles (US 20240356817 A1 Paragraph 0197 discloses “The processing capability may be included in a non-AP STA Capabilities element. For example, an exemplary EBCS Parameters element format for FL is depicted in FIG.8B. FL/ML Parameters subfield 820 depicted in FIG. 8B may indicate the Machine Learning (ML) capabilities of a non-AP STA. The processing capability may be reported from the non-AP to the AP in any appropriate manner. For example, the processing capabilities may be included in the response to the Trigger frame sent from the AP. The processing capabilities may be included in the Capabilities element, which may be reported to the AP when the non-AP STA is associated with the AP.” Additionally Paragraph 0157 discloses “ The ML/FL announcement frame may include a FL model identifier (ID), the model layers, the number of weights per layer, downlink (DL) scheduling, uplink (UL) scheduling, or the like. The ML/FL announcement frame may include the ML/FL model and/or parameters.” ) . Regarding claims 2 and 22, Wang et al. disclose a communication method in a wireless network, comprising at a second station: receiving, from a first station, a management frame including artificial intelligence and machine learning, AIML, profile information defining a profile of AIML capabilities at the first station, wherein the AIML profile information is based on pre-defined AIML profiles or sub-profiles . (US 20240356817 A1 Paragraph 0197 discloses “The processing capability may be included in a non-AP STA Capabilities element. For example, an exemplary EBCS Parameters element format for FL is depicted in FIG.8B. FL/ML Parameters subfield 820 depicted in FIG. 8B may indicate the Machine Learning (ML) capabilities of a non-AP STA. The processing capability may be reported from the non-AP to the AP in any appropriate manner. For example, the processing capabilities may be included in the response to the Trigger frame sent from the AP. The processing capabilities may be included in the Capabilities element, which may be reported to the AP when the non-AP STA is associated with the AP.” Additionally Paragraph 0157 discloses “ The ML/FL announcement frame may include a FL model identifier (ID), the model layers, the number of weights per layer, downlink (DL) scheduling, uplink (UL) scheduling, or the like. The ML/FL announcement frame may include the ML/FL model and/or parameters.”) . Regarding claim 4, Wang et al. disclose the method of Claim 1, wherein the AIML profile information includes multiple AIML sub-profile fields corresponding to multiple parameters or sets of parameters composing an AIML profile, wherein each AIML sub-profile field of the multiple AIML sub-profile fields includes an index referencing a pre-defined AIML sub-profile for the corresponding parameter or set of parameters ( US 20240356817 A1 Paragraph 0193 discloses “The parameters of the Trigger Dependent Common Info subfield 800 may include the updated training model 810, which may request the STA(s) to feedback the updated training model or updated training model parameters. For example, if the value of the updated training model subfield is 1, it may indicate that the AP requests the STA to send the updated training model (or training model parameters). If the value of the Updated Training Model subfield is 0, it may indicate the AP does not request the STA to send any updated training model (or training model parameters).”). Regarding claim 5, Wang et al. disclose the method of Claim 1, wherein the AIML profile information further includes AIML profile version information defining a set of pre-defined AIML profiles or sub-profiles available (US 20240356817 A1 Paragraph 0136 discloses “ML/FL Model identification (ID) field 408 is a subfield that may indicate the ID of one or more ML/FL models.”) . Regarding claim 6, Wang et al. disclose the method of Claim 1, wherein the first station caches the AIML profile information in local memory and reuses the cached AIML profile information in a future management frame (US 20240356817 A1 Paragraph 0230 discloses “In an illustrative embodiment, any of the operations, processes, etc. described herein may be implemented as computer-readable instructions stored on a computer-readable medium. The computer-readable instructions may be executed by a processor of a mobile unit, a network element, and/or any other computing device.”) . Regarding claim 7, Wang et al. disclose the method of Claim 1, wherein the second station retrieves the AIML profile information from the received management frame and locally stores the retrieved AIML profile information as part of first stations information (US 20240356817 A1 Paragraph 0230 discloses “In an illustrative embodiment, any of the operations, processes, etc. described herein may be implemented as computer-readable instructions stored on a computer-readable medium. The computer-readable instructions may be executed by a processor of a mobile unit, a network element, and/or any other computing device.”) . Regarding claim 9, Wang et al. disclose the method of Claim 1, wherein the management frame is an Action frame including a Category field, an AIML Action field and the AIML profile information (US 20240356817 A1 Paragraph 0145 discloses “The ML/FL Announcement frame may be implemented as public action frame which may have a HLP container that contains the HLP information needed to transmit the ML/FL related information. ). Regarding claim 10, Wang et al. disclose he method of Claim 1, wherein the first station is a first access-point, AP, station, the second station is a non-AP station, and the non-AP station: receives, from a second AP station, a second management frame including second AIML profile information defining a profile of AIML capabilities at the second AP station, selects one of the first and second AP stations based on the received AIML profile information and second AIML profile information, and requests association to the selected AP station (US 20240356817 A1 Paragraph 0145 discloses “A non-AP STA may choose compatible networks for performing authentication and association. Once compatible networks are discovered, the STA may attempt authentication. For example, a STA may send an authentication frame to an AP. The AP may receive the authentication frame and respond to the STA. After the STA is authenticated, the STA may perform an association procedure for enabling data transfers through the AP. The STA may send an association request to the AP. If the AP has the capabilities to support the STA, the AP may create an Association ID for the STA and respond with an association response with a success message granting network access to the STA. The STA is then successfully associated to the AP and data transfer may begin.”) . Regarding claim 11, Wang et al. disclose he method of Claim 1, wherein the first station is a non-access-point, AP, station, the second station is an AP station, and the AP station: decides, based on the received AIML profile information, whether to accept or refuse an association of the non-AP station to a Basic Service Set, BSS, the AP station manages, and transmits, to the non-AP station, an Association Response frame including an indication of the decided association acceptation or refusal (US 20240356817 A1 Paragraph 0145 discloses “A non-AP STA may choose compatible networks for performing authentication and association. Once compatible networks are discovered, the STA may attempt authentication. For example, a STA may send an authentication frame to an AP. The AP may receive the authentication frame and respond to the STA. After the STA is authenticated, the STA may perform an association procedure for enabling data transfers through the AP. The STA may send an association request to the AP. If the AP has the capabilities to support the STA, the AP may create an Association ID for the STA and respond with an association response with a success message granting network access to the STA. The STA is then successfully associated to the AP and data transfer may begin.”) . . Regarding claim 20, Wang et al. disclose the method of Claim 1, wherein the profile of AIML capabilities defines one or more use cases that correspond to one or more communication mechanisms in which an AIML model complying with the AIML capabilities is to be used (US 20240356817 A1 Paragraph 0132 discloses “FIG. 4A depicts an example design of a ML/FL Model sharing announcement frame 402 ln. ML/FL Model sharing announcement frame 402 may be referred to herein as a FL Announcement frame, an announcement frame, a sharing announcement frame, a ML announcement frame, FL sharing announcement frame, a ML sharing announcement frame, or a ML/FL sharing announcement frame. In one example application, a machine learning and federated learning model-sharing broadcasting procedure may be implemented in a circumstance in which an EBCS STA, such as an EBCS AP or an EBCS non-AP STA, is connected to a machine learning (ML) or federated learning server (LS) and the LS is configured to initiate or execute machine learning or federated learning algorithms in cooperation with one or more of STAs within the area or the network.”) . Claim Rejections - 35 USC § 103 07-20-fti The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action: A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made. 07-21 AIA Claim 8 is rejected under 35 U.S.C. 103(a) as being unpatentable over Wang et al. (US 20240356817 A1) in view of Wang2 et al. (WO 2024186743 A1) Regarding claim 8, Wang et al. discloses the method of Claim 1, Wang et al. fails to explicitly disclose wherein the AIML profile information is included in a Reduced Neighbor Report, RNR, element or Neighbor Report, NR, element or Multi-Link element or Vendor Specific element as defined in the IEEE 802.11 family. However in an analogous art Wang2 et al. teaches wherein the AIML profile information is included in a Reduced Neighbor Report, RNR, element or Neighbor Report, NR, element or Multi-Link element or Vendor Specific element as defined in the IEEE 802.11 family (WO 2024186743 Paragraph 0126 discloses “An AP affiliated with an AP MLD may include an AIML MAC element in a reported STA profile for another AP affiliated with the same AP MLD, for example, in a multi-link element. For example the basic multi- link element or AIML multi-link element. An AP may include indication of a neighbor AP is capable of supporting AIML-based MAC operations in the Reduced Neighbor report.”) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang et al. to incorporate the teachings of Wang2 et al, wherein the AIML profile information is included in a Reduced Neighbor Report, RNR, element or Neighbor Report, NR, element or Multi-Link element or Vendor Specific element as defined in the IEEE 802.11 family, in order to ensure proper handovers or proper identification of neighbor cells whereby the neighbor cell is able to support AIML profile related capabilities . 07-21 AIA Claim s 3 and 12-19 are rejected under 35 U.S.C. 103(a) as being unpatentable over Wang et al. (US 20240356817 A1) in view of Narayan et al. (WO 2023081187 A1) Regarding claim 3, Wang et al. disclose the method of Claim 1. Wang et al. fail to explicitly disclose wherein the AIML profile information includes a bitmap, bits of which respectively mapping to pre-defined AIML profiles. However in an analogous art Narayan et al. teaches wherein the AIML profile information includes a bitmap, bits of which respectively mapping to pre-defined AIML profiles. (WO 2023081187 A1 Paragraph 0190 discloses “In certain representative embodiments, the WTRU may be configured with a plurality of AIML models wherein each AIML model may be associated with a different compression ratio. For example, upon receiving aperiodic CSI request, the WTRU may identify the subset AIML model(s) based on the number of UCI payload bits allocated and number of CSI reports triggered.”). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang et al. to incorporate the teachings of Narayanan et al, to utilize the allocated bits to determine , in order to identify the optimal Machine Learning Model to be used to achieve the desired profile objective. Regarding claim 12, Wang et al. disclose the method of Claim 1. Wang et al fail to explicitly disclose wherein the profile of AIML capabilities is associated with one or more predefined model groups, each defining AIML models. However in an analogous art Narayanan et al. teaches wherein the profile of AIML capabilities is associated with one or more predefined model groups, each defining AIML models ( Paragraph 0104 discloses “According to embodiments, the WTRU may be configured with a selection criterion to select a subset of AIML models from a set of preconfigured AIML models for CSI processing. The WTRU may then use one or more of the selected AIML models for CSI processing. Herein different AIML models may be associated with different characteristics, for example any of the following: • Different AIML models may be optimized for better performance under specific scenarios (e.g., SNR, doppler etc.). • Different AIML models may be configured to meet different requirements for the base station (e.g., gNB) scheduler (e.g., low latency CSI, high resolution CSI, low overhead CSI etc.). • Different AIML models may be configured to enable tradeoff between memory requirements, processing requirements, complexity, performance etc. 5.1. Methods for AIML model configuration Model Pairing for CSI compression ~ e.g., when plurality of encoder decoder pairs is predefined”) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang et al. to incorporate the teachings of Narayanan et al, wherein the profile of AIML capabilities is associated with one or more predefined model groups, each defining AIML models, in order to facilitate accelerated capability matching and reduce overhead signaling. Regarding claim 13, Wang et al. disclose .the method of Claim 12, Wang et al. fail to explicitly disclose wherein the AIML models of the same model group have the same ML algorithm, the same input parameters, the same output parameters and the same optimization policy to train the AIML models. However in analogous art Narayanan et al. teaches wherein the AIML models of the same model group have the same ML algorithm, the same input parameters, the same output parameters and the same optimization policy to train the AIML models ( Paragraph 0104 discloses “According to embodiments, the WTRU may be configured with a selection criterion to select a subset of AIML models from a set of preconfigured AIML models for CSI processing. The WTRU may then use one or more of the selected AIML models for CSI processing. Herein different AIML models may be associated with different characteristics, for example any of the following: • Different AIML models may be optimized for better performance under specific scenarios (e.g., SNR, doppler etc.). • Different AIML models may be configured to meet different requirements for the base station (e.g., gNB) scheduler (e.g., low latency CSI, high resolution CSI, low overhead CSI etc.). • Different AIML models may be configured to enable tradeoff between memory requirements, processing requirements, complexity, performance etc. 5.1. Methods for AIML model configuration Model Pairing for CSI compression ~ e.g., when plurality of encoder decoder pairs is predefined”) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang et al. to incorporate the teachings of Narayanan et al, wherein the AIML models of the same model group have the same ML algorithm, the same input parameters, the same output parameters and the same optimization policy to train the AIML models, in order to facilitate accelerated capability matching and reduce overhead signaling. Regarding claim 14, Wang et al. disclose the method of Claim 12, wherein the first station is a first access-point, AP, station, the second station is a non-AP station, and the non-AP station: determines a model group associated with the AIML profile information included in the management frame, obtains an AIML model of the determined model group, and requests AIML data to the AP station to train the obtained AIML model (US 20240356817 A1 Paragraph 0197 discloses “The processing capability may be included in a non-AP STA Capabilities element. For example, an exemplary EBCS Parameters element format for FL is depicted in FIG.8B. FL/ML Parameters subfield 820 depicted in FIG. 8B may indicate the Machine Learning (ML) capabilities of a non-AP STA. The processing capability may be reported from the non-AP to the AP in any appropriate manner. For example, the processing capabilities may be included in the response to the Trigger frame sent from the AP. The processing capabilities may be included in the Capabilities element, which may be reported to the AP when the non-AP STA is associated with the AP.” Additionally Paragraph 0200 discloses “In one embodiment, one or multiple STAs may request a training model exchange, or training model parameter exchange, via a TWT procedure.”) . Regarding claim 15, Wang et al. disclose he method of Claim 14, wherein the requested AIML data include buffer statuses of other stations in the network, to train an AIML model that drives channel access of the non-AP station (US 20240356817 A1 Paragraph 0200 discloses “In one embodiment, one or multiple STAs may request a training model exchange, or training model parameter exchange, via a TWT procedure.”) . Regarding claim 16, Wang et al. disclose the method of Claim 14, wherein obtaining the AIML model includes requesting an AIML model for the determined model group, to the AP station (US 20240356817 A1 Paragraph 0200 discloses “In one embodiment, one or multiple STAs may request a training model exchange, or training model parameter exchange, via a TWT procedure.”) . Regarding claim 17, Wang et al. disclose the method of Claim 16, wherein the requested AIML model is a pre-trained AIML model (US 20240356817 A1 Paragraph 0170 discloses “For each STA that functions as an edge or local device to conduct updating the local model via local training using local data and believes there is a need to update the global model may provide the update of the ML model or model gradients via the RAML protocol. ” Examiner Note – The global model is a pre-trained model ) . Regarding claim 18, Wang et al. disclose the method of Claim 14, wherein the non-AP station shares an updated AIML model resulting from a training of the obtained AIML model, with the AP station (US 20240356817 A1 Paragraph 0118 discloses “Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a number of clients. One or more clients may have an unreliable or relatively slow network connection. On each round, each client may independently compute an update to the current model based on the client's local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global update. The typical clients in this setting may include mobile phones. As such, it may be advantageous to achieve efficient communications. Federated Learning may enable mobile phones to collaboratively learn a shared prediction model while keeping the training data on the mobile phone, decoupling the ability to do machine learning from the need to store the data in the cloud. The training data may be kept locally on users' mobile devices, and the devices may be used as nodes performing computation on their local data in order to update a global model.”) . Regarding claim 19, Wang et al. disclose the method of Claim 18, wherein the updated AIML model forms part of a global AIML model managed by the AP station, and the non-AP station receives, in response to the sharing, an updated global AIML model (US 20240356817 A1 Paragraph 0118 discloses “Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a number of clients. One or more clients may have an unreliable or relatively slow network connection. On each round, each client may independently compute an update to the current model based on the client's local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global update. The typical clients in this setting may include mobile phones. As such, it may be advantageous to achieve efficient communications. Federated Learning may enable mobile phones to collaboratively learn a shared prediction model while keeping the training data on the mobile phone, decoupling the ability to do machine learning from the need to store the data in the cloud. The training data may be kept locally on users' mobile devices, and the devices may be used as nodes performing computation on their local data in order to update a global model.”) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Samuel Dilan Rutnam whose telephone number is 703-756-1374. The examiner can normally be reached between 8:30am-5:00pm Mon-Fri. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sujoy Kundu can be reached on 571-272-8586. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /Samuel Dilan Rutnam/ Patent Examiner, Art Unit 2471 /MOHAMMAD S ADHAMI/Primary Examiner, Art Unit 2471 Application/Control Number: 18/749,037 Page 2 Art Unit: 2471 Application/Control Number: 18/749,037 Page 3 Art Unit: 2471 Application/Control Number: 18/749,037 Page 4 Art Unit: 2471 Application/Control Number: 18/749,037 Page 5 Art Unit: 2471 Application/Control Number: 18/749,037 Page 6 Art Unit: 2471 Application/Control Number: 18/749,037 Page 7 Art Unit: 2471 Application/Control Number: 18/749,037 Page 8 Art Unit: 2471 Application/Control Number: 18/749,037 Page 9 Art Unit: 2471 Application/Control Number: 18/749,037 Page 10 Art Unit: 2471 Application/Control Number: 18/749,037 Page 11 Art Unit: 2471 Application/Control Number: 18/749,037 Page 12 Art Unit: 2471 Application/Control Number: 18/749,037 Page 13 Art Unit: 2471 Application/Control Number: 18/749,037 Page 14 Art Unit: 2471 Application/Control Number: 18/749,037 Page 15 Art Unit: 2471 Application/Control Number: 18/749,037 Page 16 Art Unit: 2471 Application/Control Number: 18/749,037 Page 17 Art Unit: 2471
Prosecution Insights