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 .
Status of Claims
In response to communications filed on 24 June 2025, claims 1-3, 5-7, 9-10, 12-16, 22-25, 28, and 33-34 are presently pending in the application, of which, claims 1 and 22 are presented in independent form. The Examiner acknowledges amended claims 1-3, 5-7, 9-10, 12-16, 22-25, 28, and 33-34 and cancelled claims 4, 8, 11, 17-21, 26-27, 29-32, and 35-40.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 25 July 2025 and 24 June 2025, respectively, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings, filed 24 June 2025, have been reviewed and accepted by the Examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1-3, 5-7, 9-10, 12-16, 22-25, 28, and 33-34 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being unpatentable by Fan, Miao, et al (U.S. 2020/0193095 and known hereinafter as Fan).
As per claim 1, Fan teaches a work support system, comprising:
a memory (e.g. Fan, see Figure 5, paragraph [0082], which discloses memory); and
a processor (e.g. Fan, see Figure 5, paragraph [0082], which discloses a processor coupled to memory.) comprising:
a division unit system configured to generate a plurality of divided document data by dividing a plurality of inquiry items included in document data of an authority's inquiry per inquiry item (e.g. Fan, see paragraph [0034], which discloses the computing device may be configured to divide the questions and answers based on a certain level of granularity to obtain more textual items.);
a first acquisition unit system configured to acquire a plurality of filled-in divided document data in each of which an answer content for an inquiry content of a corresponding one of the plurality of inquiry items has been input (e.g. Fan, see paragraphs [0032-0034], which discloses the computing device first extracts a question feature expression of a question and an answer feature of an answer with respect to the question.);
a second acquisition unit system configured to acquire filled-in document data that is document data in one file format, generated from the plurality of filled-in divided document data acquired (e.g. Fan, see paragraph [0037-0041], which discloses the divided question and answers are extracted into textual elements that include specific format such as Chinese, Japanese, etc language formats of the divided question and answers.); and
a storage unit system configured to store each of the plurality of filled-in divided document data acquired, in a database in association with the filled-in document data (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 2, <> teaches the work support system according to claim 1, further comprising wherein the processor further comprising:
an attribute information extraction unit system configured to extract attribute information from the document data of the authority's inquiry or from the filled-in document data (e.g. Fan, see paragraphs [0034-0037, 0041], which discloses vectorization expression that the examiner interprets as attribute information for the divided question and answer textual items..), wherein the storage unit system stores each of the plurality of filled-in divided document data acquired, in the database in association with the attribute information and the filled-in document data (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 3, <> teaches the work support system according to claim 2, wherein the attribute information includes at least one type of information selected from a timing of the authority's inquiry, a disease category of the inquiry content, an event about which an inquiry is made, an article of the inquiry content, and a timing of an answer to the authority's inquiry (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 5, <> teaches the work support system according to claim 1,further comprising wherein the processor further comprising:
a memory unit system configured to store a trained model trained by using a training data set including:
the inquiry content and the answer content included in each of the plurality of filled- in divided document data (e.g. Fan, see paragraphs [0040-0045], which discloses a learning network that evaluates the quality of the answer (e.g. inquiry content), where the vectorized expression divide the question and answers into textual items.); and
a keyword imagined from the inquiry content and the answer content included in each of the plurality of filled-in divided document data (e.g. Fan, see paragraphs [0040-0045], which discloses a vectorized expression includes key words, where features extraction module may be configured to extract keywords from the question feature expression.);
wherein a third acquisition unit system acquires a keyword imaginable from each inquiry content and each answer content included in the filled-in document data acquired, by inputting each inquiry content and each answer content included in the filled-in document data acquired, into the trained model (e.g. Fan, see paragraphs [0040-0045], which discloses a learning network that allows questions and answers to be evaluated for quality, based at least in part on keywords.).
As per claim 6, <> teaches the work support system according to claim 5, further comprising wherein the processor further comprising:
a search unit system configured to search out filled-in divided document data matching a predetermined condition from among the plurality of filled-in divided document data acquired and stored in the database (e.g. Fan, see paragraph [0037-0041], which discloses the divided question and answers are extracted into textual elements that include specific format such as Chinese, Japanese, etc language formats of the divided question and answers.);
wherein the search system outputs attribute information, the filled-in document data, or the keyword stored in association with the filled-in divided document data that is searched out, upon receiving a details display instruction (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 7, <> teaches the work support system according to claim 6, wherein the filled-in divided document data matching the predetermined condition includes:
the plurality of filled-in divided document data including a predetermined character string;
the plurality of filled-in divided document data, the keyword associated with which matches the predetermined character string;
the plurality of filled-in divided document data, the attribute information associated with which includes a timing of the authority's inquiry or a timing of an answer to the authority's inquiry that falls within a predetermined period;
the plurality of filled-in divided document data, the attribute information associated with which includes a disease category that is a predetermined disease category;
the plurality of filled-in divided document data, the attribute information associated with which includes an event about which an inquiry is made, that is a predetermined event; or
the plurality of filled-in divided document data, the attribute information associated with which includes an article that is a predetermined article (e.g. Fan, see paragraphs [0040-0045], which discloses a vectorized expression includes key words, where features extraction module may be configured to extract keywords from the question feature expression.).
As per claim 9, <> teaches the work support system according to claim 1, further comprising wherein the processor further comprising:
a first generation unit system configured to generate a first training data set in which the inquiry content and the answer content included in the plurality of filled-in divided document data acquired by the first acquisition unit system are associated with each other, and transmit the first training data set generated, to instruct training processing on a language model (e.g. Fan, see paragraphs [0042-0046], which discloses a learning network that evaluates the quality of the answer of the textual items.).
As per claim 10, <> teaches the work support system according to claim 9, further comprising wherein the processor further comprising:
a second generation unit system configured to generate a second training data set for performing self-supervised training, by collecting information contributing to generation of answer content via a network at a predetermined cycle, and instruct training processing on the language model based on the second training data set generated (e.g. Fan, see paragraphs [0043-0048], which discloses a sequential correlation between textual item and other textual items in the question, where if there are multiple answers to be evaluated for a question.).
As per claim 12, <> teaches the work support system according to claim 9, further comprising wherein the processor further comprising:
a utilization unit system configured to, when the plurality of divided document data are generated by the division unit system from the document data of the authority's inquiry to be processed, transmit the inquiry content of each of the plurality of inquiry items to the language model having been subjected to the training processing, to acquire the answer content for the inquiry content of each of the plurality of inquiry items, wherein the first acquisition unit system acquires the plurality of filled-in divided document data generated based on the answer content acquired by the utilization unit system (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 13, <> teaches the work support system according to claim 1, further comprising wherein the processor further comprising:
a generation unit system configured to generate a training data set in which submitted document data submitted to an authority and the inquiry content of each of the plurality of inquiry items included in the document data of the authority's inquiry are associated with each other, and transmit the training data set generated, to instruct training processing on a language model (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 14, <> teaches the work support system according to claim 13, further comprising wherein the processor further comprising:
a material generation unit system configured to generate submission document data to be submitted to the authority (e.g. Fan, see paragraphs [0034-0037, 0041], which discloses vectorization expression that the examiner interprets as attribute information for the divided question and answer textual items..); and
a utilization unit system configured to, when the submission document data to be submitted to the authority is transmitted to the language model having been subjected to the training processing (e.g. Fan, see paragraphs [0034-0037, 0041], which discloses vectorization expression that the examiner interprets as attribute information for the divided question and answer textual items..), repeat:
a process for acquiring the inquiry content about the submission document data to be submitted to the authority (e.g. Fan, see paragraphs [0034-0037, 0041], which discloses vectorization expression that the examiner interprets as attribute information for the divided question and answer textual items..); and
a process for transmitting to the language model having been subjected to the training processing, the submission document data to be submitted to the authority that is revised based on the inquiry content about the submission document data to be submitted to the authority (e.g. Fan, see paragraphs [0034-0037, 0041], which discloses vectorization expression that the examiner interprets as attribute information for the divided question and answer textual items..);
wherein the material generation unit system transmits to the authority, the submission document data to be submitted to the authority that is revised by the inquiry content being repeatedly acquired by the utilization unit system (e.g. Fan, see paragraphs [0034-0037, 0041], which discloses vectorization expression that the examiner interprets as attribute information for the divided question and answer textual items..).
As per claim 15, <> teaches the work support system according to claim 13, further comprising wherein the processor further comprising:
a utilization unit system configured to, when the plurality of filled-in divided document data are generated by transmitting the inquiry content included in the plurality of divided document data to the language model having been subjected to the training processing and acquiring the answer content generated for the inquiry content, the plurality of divided document data being generated by the division unit system from the document data of the authority's inquiry to be processed (e.g. Fan, see paragraphs [0034-0037, 0041], which discloses vectorization expression that the examiner interprets as attribute information for the divided question and answer textual items..), repeat:
a process for transmitting the plurality of filled-in divided document data to the language model having been subjected to the training processing, to acquire the inquiry content about the plurality of filled-in divided document data, and a process for transmitting the inquiry content acquired, to the language model having been subjected to the training processing, to acquire the answer content for the inquiry content acquired (e.g. Fan, see paragraphs [0034-0037, 0041], which discloses vectorization expression that the examiner interprets as attribute information for the divided question and answer textual items..);
wherein the first acquisition unit system acquires the plurality of filled-in divided document data generated by the answer content being repeatedly acquired by the utilization unit system (e.g. Fan, see paragraphs [0034-0037, 0041], which discloses vectorization expression that the examiner interprets as attribute information for the divided question and answer textual items.).
As per claim 16, <> teaches the work support system according to claim 9, further comprising wherein the processor further comprising:
an agent unit system configured to, upon receiving a search instruction based on the inquiry content included in the plurality of divided document data generated by the division unit system from the document data of the authority's inquiry to be processed, collect auxiliary information that the language model having been subjected to the training processing refers to for generating the answer content for the inquiry content (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.);
wherein the auxiliary information includes the answer content included in the plurality of filled-in divided document data suitable for the inquiry content of the search instruction, which are a search result collected by searching the database, an enterprise search result collected by searching a different database other than the database, or a Web search result collected by searching a Web server device (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 22, <> teaches a work support system, comprising:
a database configured to store registration data including a plurality of filled-in divided document data, which are obtained by answer contents being input respectively into a plurality of divided document data generated by dividing inquiry contents of a plurality of inquiry items included in document data of an authority's inquiry per inquiry item (e.g. Fan, see paragraph [0034], which discloses the computing device may be configured to divide the questions and answers based on a certain level of granularity to obtain more textual items.);
a search system configured to calculate a degree of similarity with respect to vector data obtained by vectorizing an inquiry content included in document data of an authority's inquiry to be processed (e.g. Fan, see paragraph [0037-0041], which discloses the divided question and answers are extracted into textual elements that include specific format such as Chinese, Japanese, etc language formats of the divided question and answers.), to thereby search out an inquiry content including vector data satisfying a predetermined similarity condition from the database (e.g. Fan, see paragraphs [0032-0034], which discloses the computing device first extracts a question feature expression of a question and an answer feature of an answer with respect to the question.); and
an output system configured to output the plurality of filled-in divided document data corresponding to the inquiry content searched out, or data that is included in the registration data corresponding to the inquiry content searched out and that is other than the plurality of filled- in divided document data (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 23, <> teaches the work support system according to claim 22, wherein the search system includes:
a morphological analysis system configured to perform a morphological analysis on inquiry content included in the document data of the authority's inquiry to be processed, by using a glossary including terms specific to pharmaceutical development (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.);
a vectorization system configured to vectorize the inquiry contents included in the document data of the authority's inquiry to be processed, based on each word extracted by the morphological analysis from the inquiry contents included in the document data of the authority's inquiry to be processed (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.); and
a similarity degree calculation system configured to calculate the degree of similarity with respect to the vector data generated by the vectorizing from the inquiry contents included in the document data of the authority's inquiry to be processed (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 24, <> teaches the work support system according to claim 23, wherein the vectorization system vectorizes each word extracted from the inquiry content included in the document data of the authority's inquiry to be processed with a value matching a frequency of appearance of a word, and wherein the vectorization system performs the vectorizing based on TF-IDF (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 25, <> teaches the work support system according to claim 24, wherein the vectorization system performs the vectorizing based on TF-IDF (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 28, <> teaches the work support system according to claim 22, wherein the search system searches out filled-in divided document data satisfying a predetermined filter condition from among the plurality of filled-in divided document data included in the registration data stored in the database (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 33, <> teaches the work support system according to claim 22, further comprising:
a training system configured to generate a trained model by performing training processing using a training data set including the inquiry content included in the document data of the authority's inquiry to be processed, the inquiry content searched out, and a result of evaluation (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.); and
a third updating system configured to update a vectorization system using the trained model (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
As per claim 34, <> teaches the work support system according to claim 33, wherein the training system generates the trained model by performing training processing on a model such that a degree of similarity between vector data that is output when the inquiry content included in the document data of the authority's inquiry to be processed, read out from the training data set, is input into the model data and the vector data that is output when the inquiry content searched out, read out from the training data set, is input into the model becomes close to a degree of similarity matching the result of evaluation read out from the training data set (e.g. Fan, see paragraphs [0034-0037], which discloses a vectorization expression that allows for the divided question and answers to be stored as textual items with vector coding in the computing device.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 June 11, 2026
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