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 .
Response to Arguments
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.
Applicant essentially argues that the prior art on record fails to disclose the use of a topic-clustering machine learning model to segment text input into categories corresponding to topics. However, Su et al. (USPG 2023/0237270, hereinafter Su) teaches exactly this process by utilizing a machine learning model to segment text input into topic clusters. Text segments in each cluster belong to a category.
Regarding the 101 rejection, the additional elements of “topic-clustering machine model” and “question-answering machine learning model” or combination of these elements are 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 into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, the claims are directed to an abstract idea.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1 and 13 recite “segmenting …”, “determining a targeted segment …”, and “determining an answer …”. 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 dividing a text into a plurality of segments, determining a targeted segment, and determining an answer to the query based on the targeted segment. 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 and machine learning model 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 step of receiving is 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.
Similar to independent claims above, the steps of dependent claims 2-12 and 14-20, 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 claims may also include additional elements that are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic component. Accordingly, these additional elements do 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.
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 1-2, 7, 13, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (USPN 10242049) in view of Su et al. (USPG 2023/0237270, hereinafter Su).
Regarding claim 1, Ma teaches a method of extracting information from a summary of service, the method comprising:
receiving a summary of service and a query related to the summary of service (paragraphs 103-104, obtaining a set document through search and retrieval operation, “service” is merely a text with an intended use);
segmenting the summary of service into one or more data categories to create a structured summary of service (paragraphs 111-115, segmenting documents into segments);
determining a targeted segment of the structured summary of service based on the query (process in figure 3 and/or paragraphs 114-120, ranking candidate segments to obtain a final segment; see relevant portions of the reference for “further ranking answers”); and
determining an answer to the query using a question-answering machine learning model and the targeted segment (process in figures 3-4, returning answer to the question based on the filtering process described in figure 3; also see paragraphs 136-137, machine learning model).
Ma fails to explicitly disclose, however, Su teaches the use of topic clustering to segment text into one or more data categories, wherein each category corresponds to a topic identified by a top-clustering machine learning model (abstract section and paragraphs 20, 68, 88, and/or 104; these sections discusses the use of a machine learning model to segment text into topic segments; topic segment text belonging to a particular cluster have the same category).
Since Ma and Su 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 using a machine learning model to segment text into topic segements. 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 13, Ma discloses a system for extracting information from a summary of service, the system comprising:
one or more processors; and one or more non-transitory, processor-readable storage medium, wherein the one or more non-transitory, processor-readable storage medium comprises one or more programming instructions that, when executed, cause the one or more processors to:
receive service data (paragraph 137, training the model with the result);
train a question-answering machine learning model using the service data (paragraph 137, training the model with the result);
receive a summary of service and a query related to the summary of service (paragraphs 103-104, obtaining a set document through search and retrieval operation);
segment the summary of service into one or more data categories to create a structured summary of service (paragraphs 111-115, segmenting documents into segments);
determine a targeted segment of the structured summary of service based on the query (process in figure 3 and/or paragraphs 116-120; see relevant portions of the reference for “further ranking answers”); and
determine an answer to the query using the question-answering machine learning model and the targeted segment (process in figures 3-4, returning answer to the question based on the filtering process described in figure 3; also see paragraphs 136-137, machine learning model).
Ma fails to explicitly disclose, however, Su teaches the use of topic clustering to segment text into one or more data categories, wherein each category corresponds to a topic identified by a top-clustering machine learning model (abstract section and paragraphs 20, 68, 88, and/or 104; these sections discusses the use of a machine learning model to segment text into topic segments).
Since Ma and Su 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 using a machine learning model to segment text into topic segements. 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, Ma further discloses the method of claim 1, further comprising: receiving service data; and training the question-answering machine learning model using the service data (paragraph 137, training the model with the result).
Regarding claims 7 and 16, Ma further discloses wherein the question-answering machine learning model is a natural language processor (paragraph 26, “in the model learning models, tools such as dependency syntax analysis are used as a basis to generate a training corpus and training model dynamics, so as to identify the type of the opinion of the question”; this is natural language processing).
Claims 3-4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Su, and further in view of Yamron et al. (USPN 6052657, hereinafter Yamron).
Regarding claims 3, Ma fails to explicitly disclose, however, Yamron teaches the method of claim 1, wherein segmenting the summary of service into one or more data categories comprises: generating the structured summary of service using a topic-clustering machine learning model (col. 7, lines 55 to col. 8, line 10, result of segmentation and clustering can represent the summary of service).
Regarding claim 4, Ma further discloses the method of claim 3, wherein segmenting the summary of service into one or more data categories further comprises: receiving one or more summaries of service (process in figure 3, receiving one or more documents). Ma fails to explicitly disclose, however, Yamron teaches transforming the one or more summaries of service into a training set using contextual information (col. 7, lines 55 to col. 8, line 10, “contextual information” can be the topic that the user specifies); and training the topic-clustering machine learning model using the training set (col. 7, lines 55 to col. 8, line 10, “contextual information” can be the topic that the user specifies; also see col. 4, lines 25-50).
Since Ma and Yamron 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 using a machine learning model to segment and cluster textual input. 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).
Claim 14 includes subject matters similar to that of claims 3-4. Therefore, claim 14 is rejected for the same reasons as discussed in claims 3-4 above.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Su, further in view of Yamron, and further in view of Bukhamsin et al. (USPG 2023/0385324, hereinafter Bukhamsin).
Regarding claims 5 and 15, the modified Ma still fails to explicitly disclose, however, Bukhamsin teaches wherein the topic-clustering machine learning model is a natural language processor (paragraph 19, using NLP to “determine one or more clusters of topics”).
Since the modified Ma and Yamron 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 using NLP model as topic-clustering model. 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).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Su, and further in view of Yamron, and further in view of Williams et al. (USPG 2019/0347668, hereinafter Williams).
Regarding claim 6, the modified Ma still fails to explicitly disclose, however, Williams teaches the method of claim 3, wherein the topic-clustering machine learning model is a Doc2Vec model (paragraphs 83 and 88).
Since the modified Ma and Williams 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 using Doc2vec model as topic-clustering model. 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).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Su, and further in view of Xu et al. (USPG 2023/0245201, hereinafter Xu).
Regarding claim 8, Ma fails to explicitly disclose, however, Xu teaches the method of claim 1, wherein the question-answering machine learning model is a Bidirectional Encoder Representations from Transformers (BERT) model (paragraphs 12 and/or 26, “One such QA model may be a Bidirectional Encoder Representations from Transformers (BERT)”).
Since Ma and Xu 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 using BERT model as topic-clustering model. 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).
Claim 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Su, and further in view of Guggilla et al. (USPG 2020/0073882, hereinafter Guggilla).
Regarding claims 9 and 17, Ma fails to explicitly disclose, however, Guggilla teaches wherein the service comprises at least one of: a medical diagnosis, a car repair estimate, a home repair estimate, or a life insurance summary (figure 5A).
Since Ma and Guggilla 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 readily recognize that the service can be any one of a car repair estimate and home repair estimate. 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).
Claim 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Su, and further in view of Metaxas et al. (USPG 2021/0357648, hereinafter Metaxas).
Regarding claims 10 and 18, Ma fails to explicitly disclose, however, Metaxas teaches wherein the service comprises a medical diagnosis and wherein the targeted segment comprises at least one of patient information, patient history, physical examination, home medicine, pertinent results, management, and discharge planning (paragraphs 67 and 79).
Since Ma and Metaxas 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 readily recognize that the service can be a medical-related service. 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).
Claim 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Su, and further in view of Sivakumar et al. (USPG 2024/0095270, hereinafter Sivakumar).
Regarding claims 11 and 19, Ma fails to explicitly disclose, however, Sivakumar teaches wherein determining a targeted segment of the structured summary of service based on the query comprises comparing a topic of each segment in the structured summary of service with a topic associated with the query (abstract section and/or paragraph 108).
Since Ma and Sivakumar 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 comparing the topic of query to topic of cluster to determine a match. 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).
Claims 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ma in view of Su, and in view of Sivakumar, and further in view of Thoniparambil et al. (USPG 2023/0196020, hereinafter Thoniparambil)
Regarding claims 12 and 20, the modified Ma still fails to explicitly disclose, however, Thoniparambil teaches generating the topic associated with the query by using a topic-clustering machine learning model on the query (paragraphs 26 and 29, using machine learning model to process query into topic and subtopics).
Since Ma and Thoniparambil 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 generating a topic for the query. 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. Keng et al. (USPG 2015/0127653) teach a method of segmenting social media data that is considered pertinent to the claimed invention.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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