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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim recites the limitation "wherein when a document recommended according to a search result for a question included in the text data is included in the preset question-response data …”. There is insufficient antecedent basis for “the preset question-response data” in the claim. The language is interpreted as refereeing to “a preset question-response data”. Appropriate correction is required.
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.
1. Claims 1-6, 10 and 13-18 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo et al WO2020122331 A1 (“Yoo”) in view of Lee et al KR 20200063886 A (“Lee”)
Per Claim 1, Yoo discloses an intelligent response recommendation system for real- time voice counseling support, comprising:
a first terminal to which voice of a user is input (fourth page, eighth para.);
a call center system that allocates a second terminal to the first terminal to connect a call between the first terminal and the second terminal, and extracts voice data from a voice call between the first terminal and the second terminal (As illustrated in FIG. 1, the counselor terminal 300 is a computer and a voice communication device, and is a device capable of both voice calls and chatting using the first communication network 500A and/or the second communication network 500B. Can be. At this time, the voice call may mean exchanging and receiving a conversation using a voice between the user and the subject …, sixth page, sixth para.);
a media gateway that performs circuit switching with the call center system and shares the voice data (The second communication network 500B according to an embodiment of the present invention may mean a communication network that mediates transmission and reception of voice signals between respective components of the consultation system. For example, the second communication network 500B may include a public switched telephone network (PSTN) …, fifth page, eighth para.);
a STT server that converts the voice data received from the media gateway into text data (At this time, the server 100 may store the user's voice in text or provide it to the counselor terminal 300. For example, the server 100 may utilize the STT (Speech to text) technology to textize and store the user's voice …, eighth page, second para.);
a third terminal that is connected to the second terminal, and receives the text data and displays response data corresponding to the text data in a state in which the call between the first terminal and the second terminal is connected (The server 100 according to an embodiment of the present invention may generate a counseling session page for a user in response to the request received in step S34. (S35) Also, an apparatus for providing counseling information according to an embodiment of the present invention 310, the server 100 may receive the display data of the consultation session page …, eighth page, fifth para.); and
a support server that analyzes the text data received from the third terminal, generates the response data, and transmits the response data to the third terminal (In one embodiment of the present invention, the chatbot may analyze a user's request using a learned artificial neural network and provide an appropriate response to it…., fifth page, fourth para.; the present invention can not only enable a real-time conversation between the user and the counselor, but also provide an analysis result updated based on the real-time conversation content to the counselor …, ninth page, eleventh para.)
Yoo does not explicitly disclose a support server that analyzes the text data received from the third terminal to recognize a language, generates the response data corresponding to the language recognized from the text data
However, this feature is taught by Lee (The natural language processing layer 210 may perform natural language processing on a customer registration text corresponding to a customer's question using a natural language processing engine, and analyze the meaning of the customer registration text based on the natural language processing. Specifically, the natural language processing layer 210 may perform morpheme analysis on the customer registration text using the natural language processing engine, thereby removing unnecessary words from the customer registration text and extracting only meaningful words. can do. Also, the natural language processing layer unit 210 may perform parsing on the extracted meaningful words to derive the intent for the customer registration text. …, third page, last para. – fourth page, first para.; sixth page, seventh para.)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Lee with the system of Yoo in arriving at the missing features of Yoo, because such combination would have resulted in improved accuracy as well as providing a satisfactory answer to a customer's question (Lee, fifth page).
Per Claim 2, Yoo in view of Lee discloses the intelligent response recommendation system for real-time voice counseling support of claim 1,
Yoo discloses: a relay server connected to the media gateway and transmitting the text data converted by the STT server to the third terminal, wherein the media gateway transmits the voice data to the STT server to request text conversion, and transmits the text data converted by the STT server to the relay server (In addition, when the user uses the 'call method', the server 100 may request a consultation for the user from an agent who processes the user's request in a call method. At this time, the server 100 may store the user's voice in text or provide it to the counselor terminal 300. For example, the server 100 may utilize the STT (Speech to text) technology to textize and store the user's voice or provide it to the counselor terminal 300. The counseling information providing apparatus 310 (that is, the counselor terminal 300) according to an embodiment of the present invention may receive a request for a user's counseling processing from the server 100. (S33) In other words, one of the present invention The server 100 according to the embodiment may transmit a user's consultation processing request to the counselor terminal 300 with reference to the communication method identified in step S32 and the preprocessed message. The consultation processing request that the server 100 transmits to the counselor terminal 300 may include an initial message (or initial request) of the user …, Eighth page, second para.).
Per Claim 3, Yoo in view of Lee discloses the intelligent response recommendation system for real-time voice counseling support of claim 1,
Yoo discloses wherein the STT server analyzes the voice data, converts customer voice data input through the first terminal into customer text data, converts voice data of a customer service representative input through the second terminal into counselor text data, and transmits the text data (Abstract; In addition, when the user uses the 'call method', the server 100 may request a consultation for the user from an agent who processes the user's request in a call method. At this time, the server 100 may store the user's voice in text or provide it to the counselor terminal 300. For example, the server 100 may utilize the STT (Speech to text) technology to textize and store the user's voice or provide it to the counselor terminal 300…. Eighth page, second para.).
Per Claim 4, Yoo in view of Lee discloses the intelligent response recommendation system for real-time voice counseling support of claim 1,
Yoo discloses wherein the support server analyzes a meaning included in the text data, searches for information on the text data, and generates the response data based on search results (Abstract; fifth age, fourth para.; ninth page, third para.)
Lee discloses wherein the support server analyzes a meaning included in the text data, searches for information on the text data, and generates the response data based on search results (Abstract).
Per Claim 5, Yoo in view of Lee discloses the intelligent response recommendation system for real-time voice counseling support of claim 4,
Yoo discloses wherein the support server generates a recommended response based on a search result corresponding to the meaning included in the text data among the search results, and transmits the recommended response to the third terminal as the response data (Abstract; fifth age, fourth para.; ninth page, third – eleventh para.)
Per Claim 6, Yoo in view of Lee discloses the intelligent response recommendation system for real-time voice counseling support of claim 4,
Yoo discloses the support server transmits the response data for the question to the third terminal (ninth page, eleventh para.)
Lee discloses wherein when a document recommended according to a search result for a question included in the text data is included in the preset question-response data, the support server transmits the response data for the question (The natural language processing layer unit 210 may extract past query responses for the same type as the customer's question from the database 300 based on the result of the analysis (meaning about the customer registration text). That is, the natural language processing layer unit 210 may determine the type of the customer's question based on the result of the analysis, and then extract the past query correspondence of the same type as the question from the database 300.…, fourth page, fourth para.).
Per Claim 10, Yoo in view of Lee discloses the intelligent response recommendation system for real-time voice counseling support of claim 1,
Lee discloses: wherein the support server comprises: a query classifier that classifies a sentence type of data into one of a keyword, an interrogative sentence, a plain text, and an exclamation sentence based on analysis results of the text data (fourth page, first – fourth para.);
a keyword search engine that searches for data classified into the keyword by the query classifier and recommends a document (fourth page, first – fourth para.);
a natural language search engine that searches for data classified into the interrogative sentence by the query classifier and recommends a document (The call center support system 200 may determine the type of the question based on the result of the analysis, and then extract past query correspondence of the same type …, third page, third para.);
a response generation engine that extracts a response corresponding to a meaning included in the text data from the document recommended by one of the keyword search engine and the natural language search engine (The call center support system 200 may determine the type of the question based on the result of the analysis, and then extract past query correspondence of the same type …, third page, third para.); and
a response generator that generates the response data based on the response extracted by the answer generation engine (The call center support system 200 may determine the type of the question based on the result of the analysis, and then extract past query correspondence of the same type …, third page, third para.).
Per claim 13, Yoo discloses an operation method of an intelligent response recommendation system for real-time voice counseling support, comprising:
a step of extracting voice data for a voice call by connecting a first terminal and a second terminal through a communication network (As illustrated in FIG. 1, the counselor terminal 300 is a computer and a voice communication device, and is a device capable of both voice calls and chatting using the first communication network 500A and/or the second communication network 500B. Can be. At this time, the voice call may mean exchanging and receiving a conversation using a voice between the user and the subject …, sixth page, sixth para.);
a step of converting the voice data into text data (At this time, the server 100 may store the user's voice in text or provide it to the counselor terminal 300. For example, the server 100 may utilize the STT (Speech to text) technology to textize and store the user's voice …, eighth page, second para.);
a step of transmitting the text data to a third terminal connected to the second terminal (the server 100may store the user's voice in text or provide it to the counselor terminal 300. For example, the server100 may utilize the STT (Speech to text) technology to textize and store the user's voice or provide it to the counselor terminal 300…. The server 100 according to an embodiment of the present invention may generate a counseling session page for a user in response to the request received in step S34. (S35) Also, an apparatus for providing counseling information according to an embodiment of the present invention 310, the server 100 may receive the display data of the consultation session page …, eighth page, second - fifth para.);
a step in which the support server receiving the text data from the third terminal analyzes the text data (In one embodiment of the present invention, the chatbot may analyze a user's request using a learned artificial neural network and provide an appropriate response to it…., fifth page, fourth para.; the present invention can not only enable a real-time conversation between the user and the counselor, but also provide an analysis result updated based on the real-time conversation content to the counselor …, ninth page, eleventh para.)
a step of searching data for a question or a keyword included in the text data (Abstract; In the present invention, 'Chatting Bot' is a device that performs appropriate processing in response toa user's request, and may mean a virtual interaction subject provided by the chatbot server 400. In one embodiment of the present invention, the chatbot may analyze a user's request using a learned artificial neural network and provide an appropriate response to it. In addition, in another embodiment of the present invention, the chatbot may classify a user's request based on a preset rule and provide a response according to the classification result …, fifth age, fourth para.);
a step of generating response data based on search results (In the present invention, 'Chatting Bot' is a device that performs appropriate processing in response toa user's request, and may mean a virtual interaction subject provided by the chatbot server 400. In one embodiment of the present invention, the chatbot may analyze a user's request using a learned artificial neural network and provide an appropriate response to it. In addition, in another embodiment of the present invention, the chatbot may classify a user's request based on a preset rule and provide a response according to the classification result …, fifth age, fourth para.); and
a step in which the third terminal receives and displays the response data (the present invention can not only enable a real-time conversation between the user and the counselor, but also provide an analysis result updated based on the real-time conversation content to the counselor …, ninth page, eleventh para.)
Yoo does not explicitly disclose a step in which the support server receiving the text data from the third terminal recognizes a language or a step of searching data for a question or a keyword included in the text data according to a language recognition result
However, these features are taught by Lee:
a step in which the support server receiving the text data from the third terminal recognizes a language (The natural language processing layer 210 may perform natural language processing on a customer registration text corresponding to a customer's question using a natural language processing engine, and analyze the meaning of the customer registration text based on the natural language processing. Specifically, the natural language processing layer 210 may perform morpheme analysis on the customer registration text using the natural language processing engine, thereby removing unnecessary words from the customer registration text and extracting only meaningful words. can do. Also, the natural language processing layer unit 210 may perform parsing on the extracted meaningful words to derive the intent for the customer registration text.…, third page, last para. – fourth page, first para.)
a step of searching data for a question or a keyword included in the text data according to a language recognition result (The natural language processing layer unit 210 may extract past query responses for the same type as the customer's question from the database 300 based on the result of the analysis (meaning about the customer registration text). That is, the natural language processing layer unit 210 may determine the type of the customer's question based on the result of the analysis, and then extract the past query correspondence of the same type as the question from the database 300. …, fourth page, fourth para.)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Lee with the method of Yoo in arriving at the missing features of Yoo, because such combination would have resulted in improved accuracy as well as providing a satisfactory answer to a customer's question (Lee, fifth page).
Per Claim 14, Yoo in view of Lee discloses the operation method of an intelligent response recommendation system for real-time voice counseling support of claim 13,
Lee discloses: wherein the step of recognizing a language comprises: a step of refining unnecessary text from the text data (fourth page, first para.);
a step of classifying a sentence type for a sentence or a word included in the text data (fourth page, first para.); and
a step of analyzing morphemes of the text data (fourth page, first para.).
Per Claim 15, Yoo in view of Lee discloses the operation method of an intelligent response recommendation system for real-time voice counseling support of claim 13,
Lee discloses: wherein the step of recognizing a language comprises: a step of analyzing the text data and classifying the data into one of a keyword, an interrogative sentence, a plain text, and an exclamation sentence (fourth page, second para.); and
a step of ignoring data other than the keyword and the interrogative sentence and analyzing next data (fourth page, first para.).
Per Claim 16, Yoo in view of Lee discloses the operation method of an intelligent response recommendation system for real-time voice counseling support of claim 15,
Lee discloses wherein the step of searching data comprises: a step of recommending a document by searching for data classified into the keyword (fourth page, first – fourth para.); and
a step of recommending a document by searching for data classified into the interrogative sentence (The call center support system 200 may determine the type of the question based on the result of the analysis, and then extract past query correspondence of the same type …, third page, third para.);
Per Claim 17, Yoo in view of Lee discloses the operation method of an intelligent response recommendation system for real-time voice counseling support of claim 13,
Lee discloses wherein in the step of generating response data, a recommended response is generated based on a search result corresponding to a meaning included in the text data among a plurality of search results (Abstract).
Per Claim 18, Yoo in view of Lee discloses the operation method of an intelligent response recommendation system for real-time voice counseling support of claim 13,
Lee discloses wherein in the step of generating response data, when a document recommended according to the search result is a preset question-and-answer data, a response corresponding to the question is generated as the response data (The natural language processing layer unit 210 may extract past query responses for the same type as the customer's question from the database 300 based on the result of the analysis (meaning about the customer registration text). That is, the natural language processing layer unit 210 may determine the type of the customer's question based on the result of the analysis, and then extract the past query correspondence of the same type as the question from the database 300.…, fourth page, fourth para.).
2. Claims 7-9, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo in view of Lee as applied to claims 1, 4 and 13 above, and further in view of Kim et al KR 20110048675 A (“Kim”)
Per Claim 7, Yoo in view of Lee discloses the intelligent response recommendation system for real-time voice counseling support of claim 4,
Yoo discloses the support server generates the response data comprising a plurality of responses and transmits the response data to the third terminal (In one embodiment of the present invention, the chatbot may analyze a user's request using a learned artificial neural network and provide an appropriate response to it…., fifth page, fourth para.; the present invention can not only enable a real-time conversation between the user and the counselor, but also provide an analysis result updated based on the real-time conversation content to the counselor …, ninth page, eleventh para.)
Yoo in view of Lee does not explicitly disclose wherein when a document recommended according to a search result for a question included in the text data is a general document, the support server generates the response data comprising a plurality of responses and transmits the response data to the third terminal
However, this feature is taught by Kim (The tagging unit 30 tags and stores the keywords recognized through the voice recognition unit 20 with related documents searched by the counselor …, sixth page, third para.; the counselor hears the
customer's request and finds a relevant document that can be answered through a search (S420), and explains the appropriate information to the customer. At the same time, the voice recognition unit 20 operates together to recognize the keywords present in the conversation between the customer and the counselor in real time (S425). Next, search for the presence or absence of tagging information related to the recognized keywords(S430), and if there is tagging information, the tagging information is extracted from the tagging DB 35(S435), and the related document based on the tagging information is retrieved …, tenth page, fourth – seventh para.)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Kim with the method of Yoo in view of Lee in arriving at the missing features of Yoo in view of Lee, because such combination would have resulted in improving the work environment of the counselor while reducing search time (Kim, fourth page).
Per Claim 8, Yoo in view of Lee discloses the intelligent response recommendation system for real-time voice counseling support of claim 1,
Yoo in view of Lee does not explicitly disclose wherein the support server maps a question included in the text data and the derived response data, stores the mapped data in a database, and calculates statistics on the question and a response.
However, this feature is taught by Kim (The tagging unit 30 tags and stores the keywords recognized through the voice recognition unit 20 with related documents searched by the counselor 9 to provide information on the request information of the customer, sixth page, third para.; The statistics unit 50 provides or updates various statistical data by analyzing information stored in a tagging DB or an indexing DB.
, seventh page, first para.)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Kim with the method of Yoo in view of Lee in arriving at the missing features of Yoo in view of Lee, because such combination would have resulted in improving the work environment of the counselor while reducing search time (Kim, fourth page).
Per Claim 9, Yoo in view of Lee and Kim discloses the intelligent response recommendation system for real-time voice counseling support of claim 8,
Kim discloses wherein when an acknowledgement for the response data is received, the support server matches the acknowledgement to the response data and stores the matched data in the database (The tagging unit 30 tags and stores the keywords recognized through the voice recognition unit 20 with related documents searched by the counselor 9 to provide information on the request information of the customer, sixth page, third para.).
Per Claim 19, Yoo in view of Lee discloses the operation method of an intelligent response recommendation system for real-time voice counseling support of claim 13,
Yoo in view of Lee does not explicitly disclose wherein in the step of generating response data, when a document recommended according to the search result is a general document, the response data comprising a plurality of responses is generated
However, this feature is taught by Kim (The tagging unit 30 tags and stores the keywords recognized through the voice recognition unit 20 with related documents searched by the counselor …, sixth page, third para.; the counselor hears the customer's request and finds a relevant document that can be answered through a search (S420), and explains the appropriate information to the customer. At the same time, the voice recognition unit 20 operates together to recognize the keywords present in the conversation between the customer and the counselor in real time (S425). Next, search for the presence or absence of tagging information related to the recognized keywords(S430), and if there is tagging information, the tagging information is extracted from the tagging DB 35(S435), and the related document based on the tagging information is retrieved …, tenth page, fourth – seventh para.)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Kim with the method of Yoo in view of Lee in arriving at the missing features of Yoo in view of Lee, because such combination would have resulted in improving the work environment of the counselor while reducing search time (Kim, fourth page).
Per Claim 20, Yoo in view of Lee discloses the operation method of an intelligent response recommendation system for real-time voice counseling support of claim 13,
Yoo in view of Lee does not explicitly disclose a step in which the support server maps the question included in the text data and the derived response data and storing the mapped data in a database, a step of, when an acknowledgement for the response data is received, matching the acknowledgement to the response data and storing the matched data or a step of calculating statistics on the question and a response
However, these features are taught by Kim:
further comprising: a step in which the support server maps the question included in the text data and the derived response data and storing the mapped data in a database (The tagging unit 30 tags and stores the keywords recognized through the voice recognition unit 20 with related documents searched by the counselor 9 to provide information on the request information of the customer, sixth page, third para.; The statistics unit 50 provides or updates various statistical data by analyzing information stored in a tagging DB or an indexing DB, seventh page, first para.)
a step of, when an acknowledgement for the response data is received, matching the acknowledgement to the response data and storing the matched data (The tagging unit 30 tags and stores the keywords recognized through the voice recognition unit 20 with related documents searched by the counselor 9 to provide information on the request information of the customer, sixth page, third para.; The statistics unit 50 provides or updates various statistical data by analyzing information stored in a tagging DB or an indexing DB, seventh page, first para.) and
a step of calculating statistics on the question and a response.
(The tagging unit 30 tags and stores the keywords recognized through the voice recognition unit 20 with related documents searched by the counselor 9 to provide information on the request information of the customer, sixth page, third para.; The statistics unit 50 provides or updates various statistical data by analyzing information stored in a tagging DB or an indexing DB, seventh page, first para.)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Kim with the method of Yoo in view of Lee in arriving at the missing features of Yoo in view of Lee, because such combination would have resulted in improving the work environment of the counselor while reducing search time (Kim, fourth page).
2. Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Yoo in view of Lee as applied to claim 1 above, and further in view of Yu et al US 2023/0012063 A1 (“Yu”) and Choi et al “Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks” (“Choi”)
Per Claim 11, Yoo in view of Lee discloses the intelligent response recommendation system for real-time voice counseling support of claim 1,
Lee discloses wherein the support server recognizes a language of the text data (third page, last para. – fourth page, first para.; sixth page, seventh para.)
Yoo in view of Lee does not explicitly disclose wherein the support server uses sentence bidirectional encoder representations from transformers (SBERT) being an artificial intelligence language model as a pre-learning language model, embeds sentences into specific vector values understandable by a computer in accordance with contextual and semantic characteristics of data included in the text data,
However, this feature is suggested by Yu that discloses wherein the support server uses bidirectional encoder representations from transformers (BERT) being an artificial intelligence language model as a pre-learning language model, embeds sentences into specific vector values understandable by a computer in accordance with contextual and semantic characteristics of data included in the text data (fig. 1; fig. 2; para. [0005]; para. [0011]; para. [0025]; para. [0027]; para. [0032]; para. [0044])
Yoo in view of Lee does not explicitly disclose the use of sentence bidirectional encoder representations from transformers (SBERT)
However, this feature is taught by Choi (Computing sentence embeddings from contextualized language models is an active, ongoing research problem. In our exploration for more elaborate sentence embedding models, we first consider Sentence-BERT (SBERT) [4], a modified BERT network with siamese and triplet network structures to derive semantically meaningful sentence embeddings. SBERT is computationally efficient and can compare sentences using only cosine-similarity at run-time …, second page, left column; sec. IIIC)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Yu with the method of Yoo in view of Lee in arriving at the missing features of Yoo in view of Lee, as well as to combine the teachings of Choi with the teachings of Yoo in view of Lee and Yu by substituting the sentence bidirectional encoder representations from transformers (SBERT) of Choi with the bidirectional encoder representations from transformers (SBERT) of Yu, because such combination would have resulted in improving upon deficiencies of current technology by the parallel optimization of document retrieval and reading comprehension tasks (Yu, para. [0027]), as well as deriving semantically meaningful sentence embeddings that enables BERT to be used for classification tasks (Choi, sec. 1)
Per Claim 12, Yoo in view of Lee discloses the intelligent response recommendation system for real-time voice counseling support of claim 1,
Yoo in view of Lee does not explicitly disclose wherein the support server uses a sentence BERT (SBERT) model being a language model and a machine reading comprehension (MRC) model being a machine reading comprehension model, searches for a document corresponding to a question or a keyword included in the text data, and generates response data based on search results
However, this feature is suggested by Yu that discloses wherein the support server uses a BERT (BERT) model being a language model and a machine reading comprehension (MRC) model being a machine reading comprehension model, searches for a document corresponding to a question or a keyword included in the text data, and generates response data based on search results (fig. 1; fig. 2; para. [0005]; para. [0011]; The reading score can be generated based on a reading comprehension model and the matching score can be generated by a document retrieval model. A weighted joint learning loss between the two models can be calculated.…, para. [0025]; para. [0027]; para. [0032]; para. [0044])
Yoo in view of Lee does not explicitly disclose the use of sentence bidirectional encoder representations from transformers (SBERT)
However, this feature is taught by Choi (Computing sentence embeddings from contextualized language models is an active, ongoing research problem. In our exploration for more elaborate sentence embedding models, we first consider Sentence-BERT (SBERT) [4], a modified BERT network with siamese and triplet network structures to derive semantically meaningful sentence embeddings. SBERT is computationally efficient and can compare sentences using only cosine-similarity at run-time …, second page, left column; sec. IIIC)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Yu with the method of Yoo in view of Lee in arriving at the missing features of Yoo in view of Lee, as well as to combine the teachings of Choi with the teachings of Yoo in view of Lee and Yu by substituting the sentence bidirectional encoder representations from transformers (SBERT) of Choi with the bidirectional encoder representations from transformers (SBERT) of Yu, because such combination would have resulted in improving upon deficiencies of current technology by the parallel optimization of document retrieval and reading comprehension tasks (Yu, para. [0027]), as well as deriving semantically meaningful sentence embeddings that enables BERT to be used for classification tasks (Choi, sec. 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 form.
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/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658
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