DETAILED ACTION
This is in response to the application filed on June 26th 2025, in which claims 1-20 are presented for examination.
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 statements (IDS) submitted on 7/9/26, 1/23/26, 8/4/25 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
A series of singular dependent claims is permissible in which a dependent claim refers to a preceding claim which, in turn, refers to another preceding claim.
A claim which depends from a dependent claim should not be separated by any claim which does not also depend from said dependent claim. It should be kept in mind that a dependent claim may refer to any preceding independent claim. In general, applicant's sequence will not be changed. See MPEP § 608.01(n).
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)(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.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Rajasekar US 2023/0113408 A1.
Regarding claim 1, Rajasekar discloses: a model usage method, performed by a first communication device (abstract, paragraph 21, Fig. 1), comprising:
determining a first association relationship, wherein the first association relationship is an association relationship between X pieces of scenario information, Y pieces of configuration information, and M first artificial intelligence AI models of the first communication device, X, Y, and M are positive integers, and M is less than or equal to X*Y (note: the BRI of the definitions for X, Y and M is simply that the claim requires at least one scenario information, at least one configuration information, and at least one model, despite reciting “models”; Rajasekar discloses an association/relationship between a scenario, configuration and model – see abstract, Fig. 2, paragraphs 22-27, also see Fig. 9; Rajasekar also teaches “models” – paragraph 19); and
obtaining first information from a second communication device (devices exchange data – Fig. 1, paragraph 21) to train at least one first Al model in the M first AI models, and/or determining one first AI model for inference from the M first AI models based on the first association relationship (train model – abstract, paragraphs 1-6, 19-20, Figs. 2 and 5; determine model using inferencing – paragraphs 19, 34).
Regarding claim 2, Rajasekar discloses a combination manner of scenario information and configuration information that correspond to one first AI model in the M first AI models is related to the first communication device (at least the scenario and/or configuration information is related to the first device – see Figs. 1-3, paragraphs 20 and 27).
Regarding claim 3, Rajasekar discloses sending second information to the second communication device, wherein the second information indicates the first association relationship (devices communicate information relating to the association – Figs. 1-3, paragraph 33).
Regarding claim 4, Rajasekar discloses wherein determining the first association relationship comprises: determining the first association relationship based on a second association relationship, wherein the second association relationship is an association relationship between the X pieces of scenario information, the Y pieces of configuration information, and N second AI models of the second communication device, N is a positive integer, and N is less than or equal to X*Y (“relationships” – paragraph 21, Fig. 1; thus there is a first and second association relationship; plurality of models – paragraphs 3-4, 19; association relationship between scenario/configuration/model as described above – see Fig. 2, paragraphs 22-27).
Regarding claim 5, Rajasekar discloses wherein the first information indicates data collected by the second communication device in a scenario indicated by first scenario information and a configuration indicated by first configuration information, the first scenario information is at least one of the X pieces of scenario information, and the first configuration information is at least one of the Y pieces of configuration information (composite scenario includes (collect training dataset for training model scenario and transmit the data to training system with configurations – abstract, paragraph 27, Fig. 2).
Regarding claim 6, Rajasekar discloses sending first indication information to the second communication device (devices communicate – Figs. 1-2), wherein the first indication information indicates the first scenario information and the first configuration information (transmit scenario and configuration information – Figs. 1-3, paragraphs 25-27).
Regarding claim 7, Rajasekar discloses the first indication information indicates one or more of the following: first association relationship, first model information, first scenario information, or first configuration information (the previous claim already recited the first indication information indicates the first scenario and the first configuration – see rejection above; also see Fig. 6, paragraphs 40-41 which teaches selecting a specific model).
Regarding claim 8, Rajasekar discloses wherein the method further comprises: performing inference by using a third AI model, wherein the third AI model is determined based on second scenario information, or the second scenario information and the second information, and the third AI model is one of the M first AI models (composite scenario is a plurality of scenarios/models that provides inferencing – abstract, paragraphs 22, 40),
wherein the second scenario information is one of the X pieces of scenario information, the second information comprises one or more of the following: model performance indicators of the M first AI models or second configuration information, and the second configuration information is at least one piece of configuration information determined by the first communication device from the Y pieces of configuration information (composite scenario is X pieces of scenario information and includes configuration information model performance indicators such as metrics – see paragraphs 27 and 39 and Figs. 1-5).
Regarding claim 9, Rajasekar discloses wherein the method further comprises: receiving second indication information sent by the second communication device, wherein the second indication information indicates the first communication device to perform inference by using the third AI model (models provide inferences – paragraph 2, Fig. 6; plurality of devices and plurality of models – Figs. 1-2, abstract, paragraphs 3-4, 19).
Regarding claim 10, Rajasekar discloses wherein the second indication information indicates that one or more of the following are comprised: second model indication information used to determine the third AI model; third configuration information; or third scenario information, wherein the third configuration information/the third scenario information is associated with the third AI model (at least multiple models, configuration information, and multiple scenarios – see abstract, paragraph 27 and Figs. 1-5).
Regarding claim 11, Rajasekar discloses wherein the method further comprises: indicating the second scenario information to the second communication device (plurality of scenarios – abstract, paragraphs 22-23, Fig. 1; communicate scenario information for model training – Figs. 2-5, paragraph 35)
Regarding claim 12, Rajasekar discloses wherein the method further comprises: indicating the second configuration information to the second communication device (send configuration information – Figs. 2-5, paragraphs 27 and 35).
Regarding claim 13, it is a device that corresponds to the method of claim 1. Therefore, the corresponding limitations are rejected for the same reasons. Rajasekar also discloses the devices features of a processor, a memory and program instructions (computer device with processor and memory – Fig. 10, paragraphs 58-61).
Regarding claims 14-20, they corresponds to previously presented dependent claims 2- 8 respectively. Therefore, they are rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Narayanan Thangaraj et al. US 2024/0187127 A1 discloses adapting AI Models depending on an association relationship with a particular context (abstract, Fig. 2A, paragraph 73).
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/JASON D RECEK/ Primary Examiner, Art Unit 2458
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