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 .
This communication is responsive to the application filed on June 20, 2025. Claims 1-17 are pending at the time of examination.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in the instant Application No. filed on July 30, 2025.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7, 14-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims 1, 16 and 17 recite “obtaining a query statement of a user, determining at least one model identifier of at least one candidate service model based on the query statement; generating at least one first prompt word based on the query statement and the at least one model identifier, inputting the at least one first prompt word into a pre-trained target large model, and outputting, by the target large model, at least one screening parameter of the at least one candidate service model based on the at least one first prompt word; determining a target service model from the at least one candidate service model based on the at least one screening parameter; and inputting the query statement into the target service model, and obtaining feedback information corresponding to the query statement”.
The limitation of claims 1, 16 and 17, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “obtaining…, generating…, determining…, inputting…,” in the context of this claim encompasses the user manually perform the process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or manually performed, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a “pre-trained target model” to perform “input and output”. The pre-trained model in performing the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the steps) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claims 2-7, 14-15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
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 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 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Bharadwaj et al. (US 2024/0273345 A1) (“hereinafter “Bharadwaj “) in view of Upadhyay et al. (US Patent No. 12,314,825) (hereinafter “Upadhyay”).
As per claim 1, Bharadwaj discloses an information processing method, comprising:
obtaining a query statement of a user, determining at least one model identifier of at least one candidate service model based on the query statement;
generating at least one first prompt word based on the query statement and the at least one model identifier, inputting the at least one first prompt word into a pre-trained target large model,
inputting the query statement into the target service model, and obtaining feedback information corresponding to the query statement.
Bharadwaj does not explicitly teach, but Upadhyay teaches outputting, by the target large model, at least one screening parameter of the at least one candidate service model based on the at least one first prompt word;
determining a target service model from the at least one candidate service model based on the at least one screening parameter; and
as per claim 2, Upadhyay further teaches wherein determining the target service model from the at least one candidate service model based on the at least one screening parameter comprises: sorting the at least one candidate service model in descending order according to the at least one screening parameter, and selecting a first candidate service model ranked first as the target service model (col. 16, lines 1-24).
As per claim 3, Upadhyay further teaches determining the target service model from the at least one candidate service model based on the at least one screening parameter comprises: obtaining at least one current task amount of the at least one candidate service model, and determining the target service model from the at least one candidate service model based on the at least one current task amount and the at least one screening parameter (col. 2, lines 45-62).
As per claim 4, Upadhyay further teaches wherein determining the target service model from the at least one candidate service model based on the at least one current task amount and the at least one screening parameter comprises: sorting the at least one candidate service model in descending order according to the at least one screening parameter, and selecting a first candidate service model ranked first; and determining a second candidate service model ranked second as the target service model in response to the current task amount of the first candidate service model being greater than a predetermined threshold (col. 3, lines 13-20).
As per claim 5, Upadhyay further teaches wherein determining the target service model from the at least one candidate service model based on the at least one current task amount and the at least one screening parameter comprises: sorting the at least one candidate service model in descending order according to the at least one screening parameter to obtain a first sorting result; obtaining at least one current task amount of the at least one candidate service model, and adjusting the first sorting result based on the at least one current task amount to obtain a second sorting result; and selecting a third candidate service model sorted first in the second sorting result as the target service model (col. 4, lines 13-30).
As per claim 6, Upadhyay further teaches wherein, after obtain the feedback information corresponding to the query statement, the method comprises: monitoring the amount of resources used by the target service model during a process of processing the query statement (col. 10, lines 1-7, 35-47).
As per claim 7, Upadhyay further teaches generating billing information corresponding to the query statement based on the amount of resources used by the target service model (col. 10, lines 49-60).
As per claim 8, Upadhyay further teaches wherein a process of training the target large model comprises: obtaining model usage history data of a user associated with the query statement; determining a training sample set and a sample service model of a large model according to the model usage history data, wherein the training sample set comprises sample query statements of the large model; determining reference labels of the sample query statements based on the sample service model; and training the large model based on the sample query statements, a model identifier of the sample service model, and reference labels of the sample query statements, and obtaining the target large model (col. 4, lines 35-55).
As per claim 9, Upadhyay further teaches wherein a process of determining the training sample set comprises: obtaining candidate query statements according to the model usage history data, and obtaining target categories of the candidate query statements; grouping the candidate query statements according to the target categories to obtain a query statement set corresponding to each of the target categories; selecting part of candidate query statements from the query statement set corresponding to each category as sample query statements; obtaining the training sample set based on the sample query statements selected from each category (col. 4, lines 56-67).
As per claim 10, Upadhyay further teaches wherein a process of determining the training sample set comprises: matching the candidate query statements with category description information of subcategories of preset second prompt words, and obtaining target subcategories matched with the candidate query statements; mapping the target subcategories to a plurality of predetermined candidate categories, and taking candidate categories to which the target subcategories are mapped as the target categories of the candidate query statements (col. 6, lines 19-44).
As per claim 11, Upadhyay further teaches wherein a process of determining the sample service model comprises: obtaining candidate sample service models according to the model usage history data; and determining usage frequencies of the candidate sample service models, and selecting a candidate sample service model with a usage frequency greater than a predetermined value as the sample service model (col. 6, lines 6-17).
As per claim 12, Upadhyay further teaches wherein determining the reference labels of the sample query statements based on the sample service model comprises: obtaining answer information of the sample query statements based on the sample service model; obtaining standard answer information for the sample query statements; determining the reference labels of the sample query statements based on the answer information and the standard answer information (col. 9, lines 50-65).
As per claim 13, Upadhyay further teaches wherein training the large model based on the sample query statements, the model identifier of the sample service model, and the reference labels of the sample query statements, and obtaining the target large model comprises: generating a third prompt word based on the sample query statements and the model identifier of the sample service model; inputting the third prompt word into the large model, and obtaining, by the large model, predicted labels of the sample query statements based on the third prompt word; determining a loss function of the large model based on the predicted labels and the reference labels, and adjusting model parameters of the large model based on the loss function until training is completed to obtain the target large model (col. 12, lines 11-35).
As per claim 14, Upadhyay further teaches wherein, before determining the at least one model identifier of the at least one candidate service model based on the query statement, the method further comprises: receiving the query statement sent by a client through a software development kit (SDK) component; parsing the query statement, obtaining key information from the query statement, and generating a standard query statement based on the key information, wherein the key information at least comprises the at least one model identifier of the at least one candidate service model and context information of the candidate service model (col. 11, lines 20-67; col. 12, lines 53-62).
As per claim 15, Bharadwaj further teaches wherein, before determining the at least one model identifier of the at least one candidate service model based on the query statement, the method further comprises: converting a communication protocol of the standard query statement and authenticating and verifying user identity information corresponding to the standard query statement (para. 34-39).
As per claims 16 and 17, these independent claims recite several elements that are similar to the elements recited in claim 1, except in the context of a computer-readable medium, and a device system, respectively. Therefore, they are rejected at least for the same reasons as claim 1.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEBBIE M LE whose telephone number is (571)272-4111. The examiner can normally be reached 9:00-5:00.
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/DEBBIE M LE/Primary Examiner, Art Unit 2168 June 3, 2026
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