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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/16/2026 has been entered.
Response to Amendment
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-19 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 9, and 18 recite “generating …”, “determining … one or more confidence score …”, “determining … that a first request … is related to the second request”, “determining that the first data associates with first request …”, and “generating … a third data …”. These limitations, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “processor”. For example, but for the “processor” language, these steps in the context of this claim encompasses the user manually generating an input query, determining how similar the input query to the example queries recorded on a piece of paper, determining if they are related, determining if there is a response associated with the matched query, and if so, generate an output for the input query based on the response. All of these steps can be performed in the mind and/or using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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 additional elements - using a processor to perform these steps. The use of a processor is recited at a high-level of generality (i.e., as a generic computer device performing a generic computer function) 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 claim does 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 steps of “obtaining” and “causing” are merely for the purpose of data gathering and/or insignificant extra-solution activity that amount 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 claim is not patent eligible.
Regarding claims 2-8, 10-17, 19, and 21, the additional steps and/or elements under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components in the context of this claim encompasses the user manually performing these steps. All of these steps can be performed in the mind and/or using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. The additional element of “machine learning models” is recited at a high-level of generality (i.e., as a generic computer device performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
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 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Xiao et al. (USPN 11709873, hereinafter Xiao) in view Zhang et al. (USPN 10762438, hereinafter Zhang).
Regarding claims 1, 9, and 18, Xiao discloses a method, system, and processor comprising: one or more processors (figure 8) to:
obtaining first data representative of one or more associations between at least one or more first requests and one or more responses (figure 6, step 602, obtaining QA pairs);
generating, based at least on input data associated with a second request from a user, second data representative of text associated with the second request (figure 6, step 604, an input query);
determining, based at least on one or more machine learning models processing at least a portion of the first data and the second data representative of the text associated with the second request, one or more confidence scores associated with the one or more first requests (figure 6, steps 604-606, determining a similarity between the input query and the questions in the QA pairs);
determining, based at least on the one or more confidence scores, that a first request of the one or more first requests is related to the second request (figure 6, step 608, determining if there is a match or similar enough between the input query and the QA pairs);
determining that the first data associates with first request with a response of the one or more responses (figure 6, step 610, a QA pair includes an association between a question and an answer); and
causing, using at least the third data, an output associated with the response (figure 6, step 610 and/or paragraph 99, selecting the answer to provide to the user).
Xiao fails to explicitly disclose, however, Zhang teaches generating, based at least on the first data associating the first request with the response, third data associated with the response (col. 12, line 49 to col. 13, line 30, generating third data (various ways of formatting the response discussed in this section) associated with the response based on context of the question).
Since Xiao and Zhang are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of formatting a response based on context of the question. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding claims 2-3 and 10-11, Xiao further discloses wherein the determining the one or more confidence scores associated with the one or more first requests comprises determining, based at least on the one or more machine learning models processing the at least the portion of the first data and the second data, at least: a first confidence score representative of a similarity between the second request and the first request; and a second confidence score representative of a similarity between the second request and a third request from the one or more first requests (col. 4, lines 15-35, “similarity values indicating levels of similarity between the question defined by the input query and the candidate questions from the one or more question and answer spaces can be determined by comparing the text of the input query to text of the candidate questions in the candidate question and answer pairs of the one or more question and answer spaces”); wherein the determining that the first request of the one or more first requests is related to the second request comprises: determining that the first confidence score is greater than the second confidence score (col. 4, lines 15-35, “the similarity condition can indicate that the similarity value for the candidate question is to be a highest similarity value among the determined similarity values”); and selecting the first request based at least on the first confidence score being greater than the second confidence score (col. 4, lines 15-35, “the similarity condition can indicate that the similarity value for the candidate question is to be a highest similarity value among the determined similarity values”).
Regarding claims 4 and 13, Xiao further discloses determining that the first data associates the first request with the response comprises: determining that the first data represents a group that includes the first request and the response (col. 9, lines 15-25, “A question and answer space includes answerable questions and answers associated with the questions”).
Regarding claims 5 and 14, Xiao further discloses determining that the first data also associates the first request with a second response of the one or more responses (col. 16, lines 12-40, “the aggregator engine 317 can normalize the named entities (used as the answers in the candidate question and answer pairs) and can perform entity linking to associate the normalized named entities with a common entity ID. As noted above, there can be different ways of referring to the same person, place, or thing. In one illustrative example, the named entities “Barak Obama,” “Barak,” and “Obama” refer the same person. The aggregator engine 317 (or in some cases the corpus reader engine 316) can aggregate the three different named entities that refer to Barak Obama, and can link the aggregated named entities with an entity ID assigned to the named entity “Barak Obama.” Once the corpus reader engine 316 links the three entities “Barak Obama,” “Barak,” and “Obama” to the entity ID for “Barak Obama,” the corpus reader engine 316 knows that all three entities refer to the same common entity identified by the entity ID”); and selecting the response, from among the response and the second response, for generating the third data (col. 16, lines 12-40, picking one answer of the group of answers as output; “the aggregator engine 317 can normalize the named entities (used as the answers in the candidate question and answer pairs) and can perform entity linking to associate the normalized named entities with a common entity ID. As noted above, there can be different ways of referring to the same person, place, or thing. In one illustrative example, the named entities “Barak Obama,” “Barak,” and “Obama” refer the same person. The aggregator engine 317 (or in some cases the corpus reader engine 316) can aggregate the three different named entities that refer to Barak Obama, and can link the aggregated named entities with an entity ID assigned to the named entity “Barak Obama.” Once the corpus reader engine 316 links the three entities “Barak Obama,” “Barak,” and “Obama” to the entity ID for “Barak Obama,” the corpus reader engine 316 knows that all three entities refer to the same common entity identified by the entity ID”).
Regarding claims 6 and 15, Xiao further discloses wherein: the response includes an answer associated with the second request (process in figure 6, comparing input query to questions in the question-answer pairs); and the generating the third data associated with the response comprises generating the third data representative of the answer (process in figure 6, comparing input query to questions in the question-answer pairs to determine a match and to retrieve an answer associated with the matched question; also see claims 1-5 above for detailed discussion).
Regarding claims 7 and 16, Xiao further discloses determining that the response is associated with second text (as discussed in process of figure 6 and claims 1-6 above, comparing the input query (second text) against question-answer pairs); retrieving, from one or more databases, information associated with the second request (as discussed in process of figure 6 and claims 1-6 above, if the input query is determined similar to a question in the question-answer pairs, the answer associated with the matched question is retrieved); and generating a second response based at least on the second text and the information, wherein the generating the third data associated with the response comprises generating the third data representative of the second response (as discussed in process of figure 6 and claims 1-6 above, the retrieved answer (second response) is then forwarded to the user as the third data).
Regarding claim 8, Xiao fails to explicitly disclose, however, Zhang teaches the method of claim 1, wherein: the response includes a context associated with the second request; and the generating the third is based at least on the context (col. 12, line 49 to col. 13, line 30, formatting the response based on context of the question).
Since Xiao and Zhang are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of formatting a response based on context of the question. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding claim 12, Xiao further discloses the system of claim 10, wherein the determination that the second request is associated with the first request from the one or more first requests comprises: determining that a confidence score associated with the first request includes a highest confidence score from among the one or more confidence scores (col. 4, lines 15-35, “the similarity condition can indicate that the similarity value for the candidate question is to be a highest similarity value among the determined similarity values”); and determining the first request based at least on the confidence score including the highest confidence score (col. 4, lines 15-35, “the similarity condition can indicate that the similarity value for the candidate question is to be a highest similarity value among the determined similarity values”).
Regarding claims 17 and 20, Xiao further discloses the system of claim 9, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations (col. 6, line 39 to col. 7, line 3); a system implemented using an edge device; a system implemented using a robot; a system for performing conversational Al operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (col. 17, lines 6-20, Adobe Creative Cloud).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Xiao in view Zhang, and further in view of Li et al. (USPG 2019/0180009, hereinafter Li).
Regarding claim 21, the modified Xiao fails to explicitly disclose, however, Li further teaches the method of claim 1, further comprising selecting the response based at least on at least one of: randomly selecting the response from at least a portion of the one or more responses associated with the first request (paragraph 40, selecting an answer to a question randomly); the response not have been selected for a threshold period of time; or the response not have been selected for the user.
Since the modified Xiao and Li are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known technique of selecting an answer randomly. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pham et al. (USPG 2024/0160675) teach the use of multiple
models to process the same query. Agrawal et al. (USPG 2023/0101424) teach a
process for determine a first intent using a first model and a second intent using a
second model. These references are considered pertinent to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUYEN X VO whose telephone number is (571)272-7631. The examiner can normally be reached M-F, 8-4.
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/HUYEN X VO/Primary Examiner, Art Unit 2656
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