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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 25 October 2024 and 13 August 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
Claims 1, 19 and 20 are rejected under 35 U.S.C. 102(a) as being anticipated by Anonymous: “O-RAN Working Group 2 AI/ML workflow description and requirements v1.01”, 1 April 2020 (2020-04-01), XP055775462 (O-RAN).
Regarding claim 1, O-RAN discloses: “an information indication apparatus (Pg. 17, Table 2: “AI/ML deployment scenarios”: Table 2 shows the various deployment scenario, and interfaces), comprising: processor circuitry configured to determine whether to stop or update or switch an AI/ML model or an AI/ML model group (Pg. 18, Step 3. “ML model Deployment and Inference”; “The AT/ML model that is selected for the use case can be deployed via containerized image to MF where ML model shall be executing. This also includes configuration of ML inference host with Al/ML model description file”; Pg. 19, lines 13-14: “Once a new model has been trained, it will be deployed as described in Step 3, and the updated model will be used for ML inference”) for a signal processing function (Pg. 21, lines 1-3: “A: RF signal strength predictor - Predicts RF signal KPIs a UE would experience with a neighbor cell at the current time ‘x’, as well as predict the signal strength that same UE would experience with both its current serving cell and its neighbor cells at time ‘x+D’”;-Pg. 21, lines 10: “Such a modular approach could be desirable in that other uses for RF signal strength prediction”); and a transmitter configured to transmit indication information (Pg. 17, Table 2: “Interface for ML model deployment/update”; Pg. 18, Step 3 “ML model Deployment and Inference”; “The AI/ML model that is selected for the use case can be deployed via containerized image to MF where ML model shall be executing”) to a second network device (Pg. 17, Table 2; “Deployment Scenario”: Scenario 1.3; “ML Inference Host”: O-CU/O-DU) or a terminal equipment or a core network device (Pg. 17, Table 2; “Deployment Scenario”: Scenario 1.1; “Interface for ML model deployment“: SMO interna”) in a case where it is determined to stop or update or switch the AI/ML model or the AI/ML model group” (Pg. 19, Step 5. “ML model redeploy/update”; lines 13-14: “Once a new model has been trained, it will be deployed as described in Step 3, and the updated model will be used for ML inference”).
With respect to claim 19, O-RAN discloses: “an information processing apparatus (Pg. 17, Table 2: “AI/ML deployment scenarios”: Table 2 shows the various deployment scenario, and interfaces), comprising: a receiver configured to receive indication information transmitted by a first network device; and processor circuitry configured to stop or update or switch an AI/ML model or an AI/ML model group (Pg. 18, Step 3. “ML model Deployment and Inference”; “The AI/ML model that is selected for the use case can be deployed via containerized image to MF where ML model shall be executing. This also includes configuration of ML inference host with Al/ML model description file”; Pg. 19, lines 13-14: “Once a new model has been trained, it will be deployed as described in Step 3, and the updated model will be used for ML inference”) for a signal processing function according to the indication information (Pg. 21, lines 1-3: “A: RF signal strength predictor - Predicts RF signal KPIs a UE would experience with a neighbor cell at the current time ‘x’, as well as predict the signal strength that same UE would experience with both its current serving cell and its neighbor cells at time ‘x+D’”;-Pg. 21, lines 10: “Such a modular approach could be desirable in that other uses for RF signal strength prediction”).
With respect to claim 20, O-RAN discloses: “a communication system, comprising: a network device configured to determine whether to stop or update or switch an AI/ML model or an AI/ML model group (Pg. 18, Step 3. “ML model Deployment and Inference”; “The AI/ML model that is selected for the use case can be deployed via containerized image to MF where ML model shall be executing. This also includes configuration of ML inference host with Al/ML model description file”; Pg. 19, lines 13-14: “Once a new model has been trained, it will be deployed as described in Step 3, and the updated model will be used for ML inference”) for a signal processing function (Pg. 21, lines 1-3: “A: RF signal strength predictor - Predicts RF signal KPIs a UE would experience with a neighbor cell at the current time ‘x’, as well as predict the signal strength that same UE would experience with both its current serving cell and its neighbor cells at time ‘x+D’”;-Pg. 21, lines 10: “Such a modular approach could be desirable in that other uses for RF signal strength prediction”), and transmit indication information to a second network device or a terminal equipment or a core network device in a case where it is determined to stop or update or switch the AI/ML model or the AI/ML model group” (Pg. 18, Step 3. “ML model Deployment and Inference”; “The AI/ML model that is selected for the use case can be deployed via containerized image to MF where ML model shall be executing. This also includes configuration of ML inference host with Al/ML model description file”; Pg. 19, lines 13-14: “Once a new model has been trained, it will be deployed as described in Step 3, and the updated model will be used for ML inference”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2, 7 and 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over O-RAN in view of WIPO Publication No. WO 2021/142637 (Shen).
Claims 2, 7 and 12-16 are ultimately dependent upon Claim 1. As discussed above, claim 1 is disclosed by O-RAN. Thus, those limitations of claims 2, 7 and 12 -15 that are recited in claim 1 are also disclosed by O-RAN.
However, O-RAN does not clearly disclose the remaining limitations of the claims. To that end with respect to claim 12, Shen discloses: “the first indication information for stopping the AI/ML model or the AI/ML model group ([0104]: “The AI/ML operation division method for mobile terminal participation may include: The terminal receives indication information from a wireless communication system network device (where the indication information may be scheduling information)”), and/or the second indication information for updating the AI/ML model or the AI/ML model group ([0105]: “Method 1: Preset n (n = 1, 2. N) an AI/ML model, where the indication information indicates a number of one model”), is/are transmitted to one or more terminal equipments in a cell or an area via system information and/or a paging message ([0103]: “based on a situation of the terminal (for example, factors such as an available computing power and a wireless transmission rate), an AI/ML operation task between the network device and the terminal, including: dynamically indicating the AI/ML model that the terminal should use”). It is respectfully submitted that it would have been obvious to one of ordinary skill in the art at the time of the invention to combine O-RAN with the invention of Shen in order to provide indication information regarding the AI/ML model (e.g., see Shen @ [0103], [0105]).
Regarding claim 13, Shen further discloses: “first indication information comprises at least one of the following: identification information of the signal processing function, identification information of the AI/ML model ([0103]: “an AI/ML operation task between the network device and the terminal, including: dynamically indicating the AI/ML model that the terminal should use”), version information of the AI/ML model ([0105]: “Method 1: Preset n (n = 1, 2. N) an AI/ML model, where the indication information indicates a number of one model”), identification information of the AI/ML model group, intra-group identification information of the AI/ML model group, type information of the AI/ML model, storage size information of the AI/ML model or the AI/ML model group, a model exclusion list and/or a model permission list, or model identification information and model performance information and/or model operation information corresponding to the model identification information.
With respect to claim14, Shen further discloses: “the first indication information is used by the terminal equipment to determine whether to stop the AI/ML model or the AI/ML model group in the terminal equipment ([0104]: “The AI/ML operation division method for mobile terminal participation may include: The terminal receives indication information from a wireless communication system network device (where the indication information may be scheduling information)”), wherein in a case where at least one piece of identification information of the signal processing function to which the AI/ML model in the terminal equipment corresponds, identification information of the AI/ML model ([0105]: “Method 1: Preset n (n = 1, 2. N) an AI/ML model, where the indication information indicates a number of one model”), identification information of the AI/ML model group, intra-group identification information of the AI/ML model group or version information of the AI/ML model is in consistence with the first indication information, the terminal equipment determines to stop the AI/ML model in the terminal equipment”.
Regarding claim15, Shen further discloses: “the second indication information comprises at least one of the following: identification information of the signal processing function, identification information of a new AI/ML model ([0105]: “Method 1: Preset n (n = 1, 2. N) an AI/ML model, where the indication information indicates a number of one model”), identification information of a new AI/ML model group, intra-group identification information of the new AI/ML model group, version information of the new AI/ML model, identification information of an original AI/ML model, identification information of an original AI/ML model group, intra-group identification information of the original AI/ML model group, version information of the original AI/ML model, a model exclusion list and/or a model permission list, or model identification information and model performance information and/or model operation information corresponding to the model identification information”.
With respect to claim16, Shen further discloses: “the second indication information is used by the terminal equipment to determine whether to update the AI/ML model or the AI/ML model group in the terminal equipment ([0104]: “The AI/ML operation division method for mobile terminal participation may include: The terminal receives indication information from a wireless communication system network device (where the indication information may be scheduling information)”); wherein in a case where identification information of the signal processing function to which the AI/ML model in the terminal equipment corresponds is in consistence with the second indication information and identification information of the AI/ML model is not in consistence with the second indication information and/or version information of the AI/ML model is lower than version information of the AI/ML model in the second indication information, the terminal equipment determines to update the AI/ML model in the terminal equipment.
In addition, O-RAN does not clearly disclose the remaining limitations of claim 2. To that end with respect to claim 2, Shen discloses: “wherein first indication information for stopping the AI/ML model or the AI/ML model group ([0104]: “The AI/ML operation division method for mobile terminal participation may include: The terminal receives indication information from a wireless communication system network device (where the indication information may be scheduling information)”), and/or second indication information for updating the AI/ML model or the AI/ML model group ([0105]: “Method 1: Preset n (n = 1, 2. N) an AI/ML model, where the indication information indicates a number of one model”), is/are transmitted to the second network device via an Xn interface” ([0147]: “In the several embodiments provided in this application, it should be understood that the disclosed technical content may be implemented in other manners”; “In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces”).
Further, O-RAN does not clearly disclose the remaining limitations of claim 7. To that end with respect to claim 7, Shen discloses: “wherein first indication information for stopping the AI/ML model or the AI/ML model group ([0104]: “The AI/ML operation division method for mobile terminal participation may include: The terminal receives indication information from a wireless communication system network device (where the indication information may be scheduling information)”), and/or second indication information for updating the AI/ML model or the AI/ML model group ([0105]: “Method 1: Preset n (n = 1, 2. N) an AI/ML model, where the indication information indicates a number of one model”), is/are transmitted to the core network device via an NG interface” ([0147]: “In the several embodiments provided in this application, it should be understood that the disclosed technical content may be implemented in other manners”; “In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces”).
Allowable Subject Matter
Claims 3-6, 8-11, 17 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/Myron Wyche/ 18 September 2026
Primary Examiner AU2644
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