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 .
The instant application having Application No. 19/319,030 filed on 09/04/2025 is presented for examination by the Examiner. Claims 1-20 are currently pending in the present application.
Drawings
The drawings filed 09/04/2025 are accepted for examination purposes.
Information Disclosure Statement
As required by M.P.E.P. 609, the Applicant's submission of the Information Disclosure Statement dated 09/04/2025 is acknowledged by the Examiner and the cited references have been considered in the examination of the claims now pending.
Claim Rejections - 35 USC § 102
5. 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.
6. Claims 1-20 rejected under 35 U.S.C. 102(a)(2) as being anticipated by Neilesh Chorakhalikar (US-20260025344-A1).
As per claim 1, Chorakhalikar teaches “A method for evaluating multimodal retrieval augmented generation (RAG) performance, comprising”:
“generating an internal response from a user input and a RAG database,” ([0067], [0071]-[0072]);
“generating a relevancy score for quantifying a relevance of the internal response to information retrieved from the RAG database based on the user input and a correctness score quantifying accuracy of the internal response to the information retrieved from the RAG database,’ ([0068]-[0069]);
“generating a combined score from the relevancy score and correctness score,” ([0069]); and
“selectively performing a task based on the relevancy score, the correctness score,
or the combined score,” ([0069]-[0070]).
As per claim 2, Chorakhalikar further shows “wherein determining the relevancy score further comprises:
automatically modifying the user input to achieve a higher combined score,” ([0069]-[0070]).
As per claim 3, Chorakhalikar further shows “partitioning and categorizing the internal response into spans and calculating the relevancy score for the spans,’ ([0068], [0075]-[0076]).
As per claim 4, Chorakhalikar further shows “partitioning and categorizing the internal response into spans, the spans being either objective or subjective, and calculating the correctness score for the objective spans,” ([0068], [0075]-[0076]).
As per claim 5, Chorakhalikar further shows “evaluating a top-k number of results for the relevancy score,” ([0075]-[0076]).
As per claim 6, Chorakhalikar further shows “embedding information in the RAG database into vector embeddings,” ([0073]).
As per claim 7, Chorakhalikar further shows “wherein the RAG database includes information that is a different modality than the user input,” ([0068]-[0069]).
As per claim 8, Chorakhalikar teaches “A system for evaluating multimodal retrieval augmented generation (RAG) performance, comprising”:
“a processor,” (fig. 12C); and
a memory storing computer-readable instructions that, when executed by the processor, cause the system to:
generate an internal response from a user input and a RAG database,” ([0067], [0071]-[0072]);;
“generate a relevancy score for quantifying a relevance of the internal response to information retrieved from the RAG database based on the user input and a correctness score quantifying accuracy of the internal response to the information retrieved from the RAG database,” ([0068]-[0069]);
“generate a combined score from the relevancy score and correctness score,” ([0069]); and
“selectively perform a task based on the relevancy score, the correctness score, or the combined score,” ([0069]-[0070]).
As per claim 9, Chorakhalikar further shows “wherein the memory further causes the system to:
automatically modify the user input to achieve a higher combined score,” ([0069]-[0070]).
As per claim 10, Chorakhalikar further shows “wherein the memory further causes the system to:
partition and categorize the internal response into spans and calculate the relevancy score for the spans,” ([0068], [0075]-[0076]).
As per claim 11, Chorakhalikar further shows “partition and categorize the internal response into spans, the spans being either objective or subjective, and calculate the correctness score for the objective spans,” ([0068], [0075]-[0076]).
As per claim 12, Chorakhalikar further shows “wherein the memory further causes the system to:
evaluate a top-k number of results for the relevancy score,” ([0075]-[0076]).
As per claim 13, Chorakhalikar further shows “wherein the memory further causes the system to:
embed information in the RAG database into vector embeddings,” ([0073]).
As per claim 14, Chorakhalikar further shows “wherein the RAG database includes information that is a different modality than the user input,” ([0068]-[0069]).
As per claim 15, Chorakhalikar teaches “A computer program product comprising a non-transitory computer-readable storage medium containing computer program code, the computer program code when executed by one or more processors causes the one or more processors to perform operations, the computer program code comprising instructions to:
generate an internal response from a user input and a RAG database,’ ([0067], [0071]-[0072]);
“generate a relevancy score for quantifying a relevance of the internal response to information retrieved from the RAG database based on the user input and a correctness score quantifying accuracy of the internal response to the information retrieved from the RAG database,” ([0068]-[0069]);;
“generate a combined score from the relevancy score and correctness score,” ([0069]); and
“selectively perform a task based on the relevancy score, the correctness score, or the combined score,” ([0069]-[0070]).
As per claim 16, Chorakhalikar further shows “wherein the computer program code further includes instructions to:
automatically modify the user input to achieve a higher combined score,” ([0069]-[0070]).
As per claim 17, Chorakhalikar further shows “wherein the computer program code
further includes instructions to:
partition and categorize the internal response into spans and calculate the relevancy score for the spans,” ([0068], [0075]-[0076]).
As per claim 18, Chorakhalikar further shows “wherein the computer program code further includes instructions to:
partition and categorize the internal response into spans, the spans being either objective or subjective, and calculate the correctness score for the objective spans,” ([0068], [0075]-[0076]).
As per claim 19, Chorakhalikar further shows “wherein the computer program code further includes instructions to:
evaluate a top-k number of results for the relevancy score,” ([0075]-[0076]).
As per claim 20, Chorakhalikar further shows “wherein the computer program code further includes instructions to:
embed information in the RAG database into vector embeddings,” ([0073]).
Allowable Subject Matter
7. Claims 1-20 would be allowable if rewritten or amended to overcome the rejections as set forth in this Office action.
Conclusion
8. The prior art made of record, listed on PTO 892 provided to Applicant is considered to have relevancy to the claimed invention. Applicant should review each identified reference carefully before responding to this office action to properly advance the case in light of the prior art.
Contact Information
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIM T NGUYEN whose telephone number is (571)270-1757. The examiner can normally be reached on Mon-Thurs 6-4:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Aug. 05, 2026
/KIM T NGUYEN/Primary Examiner, Art Unit 2153
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