DETAILED ACTION
The non-final office action is responsive to the preliminary amendment to U.S. Patent Application 19/216,231 on 04/30/2025. Claims 1-14 and 20-25 are pending; claims 1-14 and 20-25 are rejected.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/30/2025 was filed before the mailing date of the non-final office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 4, 10, 12, and 22 are objected to because of the following informalities: “-”. 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 and 20-25 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claims 1 and 14, the claim limitation recites “receiving the information from a first node and/or a second node, wherein the information is used for the third node to assist the AI/ ML related operation in the wireless communication system” (emphasis added). It is not clear the conjunction should be treated as “and” or “or”. As people with ordinary skill in the art would know that “and” is a type of coordinating conjunction and is commonly used to indicate a dependent relationship. Here, the two clauses are dependent on each other and both are true and take together. And “or” is another type of coordinating conjunction, but it indicates an independent relationship. Here, the two clauses are somewhat separate; while they are related, they are not co-dependent on each other. In terms of networking or software, “and” is used to indicate a function where both categories are met, whereas “or” indicates a function where either categories are met. So “and/or” is confusing and renders the claim indefinite. Examiner will treat “and/or” as “or” for examination purpose.
Claims 2-10 and 20-24 have the limitation from claims 1 and 14 and do not remedy the deficiency. Claims 2-10 and 20-24 are rejected under same rationale.
As to claims 2-10 and 20-23 have limitations with “and/or”. As set forth in the rejection of claims 1 and 14 above, the term “and/or” is confusing and renders the claims indefinite.
As to claim 11, claim limitation recites “the first node receiving a message from a third node, and/or a second node; and the first node changing the AI/ML model related operation or not” (emphasis added). It is not clear the conjunction should be treated as “and” or “or”. As people with ordinary skill in the art would know that “and” is a type of coordinating conjunction and is commonly used to indicate a dependent relationship. Here, the two clauses are dependent on each other and both are true and take together. And “or” is another type of coordinating conjunction, but it indicates an independent relationship. Here, the two clauses are somewhat separate; while they are related, they are not co-dependent on each other. In terms of networking or software, “and” is used to indicate a function where both categories are met, whereas “or” indicates a function where either categories are met. So “and/or” is confusing and renders the claim indefinite. Examiner will treat “and/or” as “or” for examination purpose.
Claims 2-10 and 24 have the limitation from claim 1 and do not remedy the deficiency. Claims 2-10 and 24 are rejected under same rationale.
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)(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 1-14 and 20-25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Application Publication 2024/0333604 A1 to ZHU et al. (hereinafter ZHU).
As to claim 1, ZHU teaches a method for assisting an artificial intelligence (AI)/machine learning (ML) related operation in a wireless communication system based on information (A method and apparatus for training an artificial intelligence AI model in a wireless network are provided, ZHU, Abstract), performed by a third node (The federated learning includes a central node (e.g. claimed “third node”) and an edge node (e.g. claimed “third node”). The central node (for example, a server or a base station) may invoke a distributed device located on an edge device (for example, a smartphone or a sensor) to participate in model training without compromising privacy, ZHU, [0074]-[0076], [0053]), comprising:
receiving the information from a first node and/or a second node, wherein the information is used for the third node to determining assist the AI/ ML model related operation in the wireless communication system (Each of n terminals (e.g. claimed “a first node and/or a second node”) reports terminal information to a base station (e.g. claimed “third node”), and the base station centrally collects the terminal information, and reports the collected terminal information to a central node (e.g. claimed “third node”), ZHU, [0094]-[0126], [0074]-[0093], [0127]-[0143], [0144]-[0162]).
As to claim 2, ZHU teaches the method for assisting the AI/ML related operation in the wireless communication system based on information according to claim 1, wherein the information is sent form the first node and/or the second node to the third node and comprises a message including AI/ML related operation, and/or AI/ML model related assistant information (Each of n terminals (e.g. claimed “a first node and/or a second node”) reports terminal information to a base station (e.g. claimed “third node”), and the base station centrally collects the terminal information, and reports the collected terminal information to a central node (e.g. claimed “third node”), ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]).
As to claim 3, ZHU teaches the method for assisting the AI/ML related operation in the wireless communication system based on information according to claim 2, wherein AI/ML model related operation is transmitted from the second node to the third node; and/or from the first node to the third node (Each of n terminals reports terminal information to a base station, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]).
As to claim 4, ZHU teaches the method for assisting the AI/ML related operation in the wireless communication system based on information according to claim 2, wherein the AI/ML related assistant information contains one of the followings: related operation ID: related operation group ID; parameters and/or structure of AI/ML related operation (The terminal reports the gradient (e.g. claimed “parameters” of AI/ML., see ZHU, [0003]) in the current round of model training, the base station calculates the average gradient in the current round of model training, and the base station updates a parameter of the AI model based on the average gradient in the current round of model training, and delivers the average gradient in the current round of model training to the n terminals participating in federated learning, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]).
As to claim 5, ZHU teaches the method for assisting the AI/ML related operation in the wireless communication system based on information according to claim 4, wherein if the third node receives parameters and/or structure of AI/ML related operation (The terminal reports the gradient in the current round of model training, the base station calculates the average gradient in the current round of model training, and the base station updates a parameter of the AI model based on the average gradient in the current round of model training, and delivers the average gradient in the current round of model training to the n terminals participating in federated learning, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]), and if the third node has related operation correlation (the central node may use, as the terminal participating in federated learning, a terminal whose communication capability is greater than or equal to a communication capability threshold, whose computing capability is greater than or equal to a computing capability threshold, and whose data set feature meets a data set feature requirement, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]), the third node determines assists one AI/ML related operation (The terminal reports the gradient in the current round of model training, the base station calculates the average gradient in the current round of model training, and the base station updates a parameter of the AI model based on the average gradient in the current round of model training, and delivers the average gradient in the current round of model training to the n terminals participating in federated learning, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]).
As to claim 6, ZHU teaches the method for assisting the AI/ML related operation in the wireless communication system based on information according to claim 4, wherein if the third node receives parameters and/or structure of AI/ML model related operation, the third node assists one AI/ML model related operation (The terminal reports the gradient in the current round of model training, the base station calculates the average gradient in the current round of model training, and the base station updates a parameter of the AI model based on the average gradient in the current round of model training, and delivers the average gradient in the current round of model training to the n terminals participating in federated learning, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]).
As to claim 7, ZHU teaches the method for assisting the AI/ML related operation in the wireless communication system based on information according to claim 4, wherein the third node assists that the AI/ML related operation by related operation selection, and/or related operation reconfiguration, and/or related operation update (The terminal reports the gradient in the current round of model training, the base station calculates the average gradient in the current round of model training, and the base station updates a parameter of the AI model based on the average gradient in the current round of model training, and delivers the average gradient in the current round of model training to the n terminals participating in federated learning, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]).
As to claim 8, ZHU teaches the method for assisting the AI/ML related operation in the wireless communication system based on information according to claim 5, wherein the related operation correlation indicates a range for some parameters and/or structure of AI/ML model related operations (the central node may use, as the terminal participating in federated learning, a terminal whose communication capability is greater than or equal to a communication capability threshold, whose computing capability is greater than or equal to a computing capability threshold, and whose data set feature meets a data set feature requirement, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]).
As to claim 9, ZHU teaches the method for assisting the AI/ML related operation in the wireless communication system based on information according to claim 5, wherein the related operation correlation is configured by a network, or fixed, or indicated by the second node or the first node ().
As to claim 10, ZHU teaches the method for assisting the AI/ML related operation in the wireless communication system based on information according to claim 7, wherein the related operation reconfiguration and/or update comprises options as followings: - based on the third node decides by itself; or - based on model related operation correlation (The terminal reports the gradient in the current round of model training, the base station calculates the average gradient in the current round of model training, and the base station updates a parameter of the AI model based on the average gradient in the current round of model training, and delivers the average gradient in the current round of model training to the n terminals participating in federated learning, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]).
As to claim 24, ZHU teaches the method for assisting the AI/ML related operation in the wireless communication system based on information according to claim 1, wherein the first node is a user equipment (UE) (The terminal may also be referred to as a terminal device, user equipment (UE), ZHU, [0063]), the second node is a gNB, and the third node is a target gNB (gNB, ZHU, [0053], [0074]-[0076]).
As to claim 11, ZHU teaches a method of processing about the AI/ML related operation by a first node, comprising: the first node receiving a message from a third node, and/or a second node; and the first node changing the AI/ML related operation or not (The terminal reports the gradient in the current round of model training, the base station calculates the average gradient in the current round of model training, and the base station updates a parameter of the AI model based on the average gradient in the current round of model training, and delivers the average gradient in the current round of model training to the n terminals participating in federated learning, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]).
As to claim 12, ZHU teaches the method of claim 11, wherein the message comprises at least one of followings: - a first indicator, which indicates whether the AI/ML related operation changed, and the UE a user equipment (UE) based on the first indicator is able to know if a target gNB uses a new AI/ML model related operation or not; - AI/ML related operation ID, which uniquely identifies the AI/ML related operation; - a second indicator, which indicates that the UE uses the old AI/ML model related operation or use the new AI/ML model related operation (The terminal reports the gradient in the current round of model training, the base station calculates the average gradient in the current round of model training, and the base station updates a parameter of the AI model based on the average gradient in the current round of model training, and delivers the average gradient in the current round of model training to the n terminals participating in federated learning, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]), wherein the first node is the UE, the second node is a gNB, and the third node is the target gNB (ZHU, [0053], [0063], [0074]-[0076]).
As to claim 13, ZHU teaches the method of claim 12, wherein the first node changes the AI/ML model related operation or not, comprising: if the target gNB sends the second indicator, the UE changes the new AI/ML model related operation change or not based on the second indicator requirement; or if the target gNB does not send the second indicator, but the first node only knows the AI/ML model related operation is changed in the target gNB based on the first indicator, the first node based on itself changes the new AI/ML model related operation or not (The terminal reports the gradient in the current round of model training, the base station calculates the average gradient in the current round of model training, and the base station updates a parameter of the AI model based on the average gradient in the current round of model training, and delivers the average gradient in the current round of model training to the n terminals participating in federated learning, ZHU, [0094]-[0126], [0074]-[0083], [0127]-[0143], [0144]-[0162]).
As to claims 14, 20-23, and 25, the same reasoning applies mutatis mutandis to the corresponding system claims 14, 20-23, and 25 (Note: memory, transceiver and processor are disclosed by ZHU in [0167], 0175]-[0176]). Accordingly, claims 14, 20-23, and 25 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over ZHU.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUOLEI ZONG whose telephone number is (571)270-7522. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM IFP.
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/RUOLEI ZONG/Primary Examiner, Art Unit 2449 8/28/2026
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