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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 6, 2025 is being considered by the examiner.
Claim Rejections - 35 USC § 102
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, 19 and 21 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Berajawala et al. (U.S. Publication No. 2016/0098737 A1, hereinafter referred to as “Berajawala”), which incorporates Duboue et al. (U.S. Publication No. 2011/0125734 A1, hereinafter referred to as “Duboue”) by reference.
Regarding claim 1, Berajawala discloses a method, comprising: (e.g., paragraph [0023])
retrieving, based on an input target question, k support documents related to the target question, wherein k is a positive integer greater than 1; (“Categorizing the questions, such as in terms of roles, type of information, tasks, or the like, associated with the question, in each document of a corpus of data may allow the QA system to more quickly and efficiently identify documents containing content related to a specific query.” – “documents” plural is disclosed and is considered to be “k documents is a positive integer greater than 1”)(e.g., paragraphs [0035], [0098] and [0103])
performing, based on the k support documents, information aggregation to obtain target aggregation information, (“Documents may be clustered based on similar topics in a manner generally known in the art of question answering. A lookup operation on these clusters may be performed to identify clusters related to topics in an input question, e.g., clusters associated with “Manchester United” or “Soccer Teams” since it can be deduced from the question that Manchester United is a soccer team. Documents that reside within the identified cluster(s) are given a higher passage affinity score than documents that reside outside the cluster. Moreover, questions associated with a given document or portion of content in the corpus, that have a same lexical answer type (LAT) and focus as information presented in the document are given a higher affinity score.” “the QA system may identify another QA system pipeline that operates on a corpus having higher affinity scored documents for the input question in a manner similar to that described above with regard to identifying related questions. That is, the QA system may have access to the question/evidence passage/affinity score mappings for documents in the various other corpora used by other QA system pipelines. From this mapping, a determination of a corpus that contains evidence passages (documents) that have a higher affinity to questions similar to that of the input question may be made and the QA system pipeline utilizing the corpus may be identified. The input question may then be redirected to the identified QA system pipeline for processing and results returned to the original QA system which may then provide them to the submitter of the original input question.”)(e.g., paragraphs [0067] and [0103]) wherein the information aggregation comprises information encoding and exchanging; and (“FIG. 1 depicts a schematic diagram of one illustrative embodiment of a question/answer creation (QA) system 100 in a computer network 102. One example of a question/answer generation which may be used in conjunction with the principles described herein is described in U.S. Patent Application Publication No. 2011/0125734, which is herein incorporated by reference in its entirety.” U.S. Patent Application Publication No. 2011/0125734 A1 provides: “More particularly, in one embodiment, FIG. 2A shows a machine learning implementation where the "answer ranking" module 60 includes a trained model component 70 produced using a machine learning techniques from prior data. The prior data may encode information on features of candidate answers, the features of passages the candidate answers come, the scores given to them by Candidate Answer Scoring modules 40, and whether the candidate answer was correct or not. In other words, machine learning algorithms can be applied to the entire content of the CASes together with the information about correctness of the candidate answer. Such prior data is readily available for instance in technical services support functions, or in more general setting on Internet, where many websites list questions with correct answers.”)(e.g., paragraph [0037] – and paragraphs [0037], [0088] and [0093] and of the incorporated by reference PG Pub No. 2011/0125734 A1)
generating, based on the target aggregation information, a target answer corresponding to the target question. (“Based on the application of the queries to the corpus of data, a set of hypotheses, or candidate answers to the input question, are generated by looking across the corpus of data for portions of the corpus of data that have some potential for containing a valuable response to the input question.” “This management may comprise any operation that modifies the corpus or corpora with regard to the evidence passages associated with candidate answers generated for an input question. That is, the QA system may process an input question and generate a set of candidate answers and/or a final answer that is returned to the submitter of the input question. Affinity information, confidence measures, user feedback information, and the like, may be used to determine how well the input question was answered by the various candidate answers and/or final answer as well how useful the evidence passages that are the sources of the candidate answers, or provide supporting evidence for the candidate answers, actually do support the candidate answer as a correct answer for the input question.”)(e.g., figures 7 and 9 and paragraphs [0041], [0093] and [0094]).
Regarding claim 19, Berajawala discloses a computer program product comprising instructions that are stored on a non-transitory computer-readable storage medium and that, when executed by one or more processors, cause an apparatus to: (e.g., paragraphs [0005], [0023] and claim 11)
retrieve, based on an input target question, k support documents related to the target question, wherein k is a positive integer greater than 1; (“Categorizing the questions, such as in terms of roles, type of information, tasks, or the like, associated with the question, in each document of a corpus of data may allow the QA system to more quickly and efficiently identify documents containing content related to a specific query.” – “documents” plural is disclosed and is considered to be “k documents is a positive integer greater than 1”)(e.g., paragraphs [0035], [0098] and [0103])
perform information aggregation on the k support documents to obtain target aggregation information, (“Documents may be clustered based on similar topics in a manner generally known in the art of question answering. A lookup operation on these clusters may be performed to identify clusters related to topics in an input question, e.g., clusters associated with “Manchester United” or “Soccer Teams” since it can be deduced from the question that Manchester United is a soccer team. Documents that reside within the identified cluster(s) are given a higher passage affinity score than documents that reside outside the cluster. Moreover, questions associated with a given document or portion of content in the corpus, that have a same lexical answer type (LAT) and focus as information presented in the document are given a higher affinity score.” “the QA system may identify another QA system pipeline that operates on a corpus having higher affinity scored documents for the input question in a manner similar to that described above with regard to identifying related questions. That is, the QA system may have access to the question/evidence passage/affinity score mappings for documents in the various other corpora used by other QA system pipelines. From this mapping, a determination of a corpus that contains evidence passages (documents) that have a higher affinity to questions similar to that of the input question may be made and the QA system pipeline utilizing the corpus may be identified. The input question may then be redirected to the identified QA system pipeline for processing and results returned to the original QA system which may then provide them to the submitter of the original input question.”)(e.g., paragraphs [0067] and [0103]) wherein the information aggregation comprises information encoding and exchanging; and (“FIG. 1 depicts a schematic diagram of one illustrative embodiment of a question/answer creation (QA) system 100 in a computer network 102. One example of a question/answer generation which may be used in conjunction with the principles described herein is described in U.S. Patent Application Publication No. 2011/0125734, which is herein incorporated by reference in its entirety.” U.S. Patent Application Publication No. 2011/0125734 A1 provides: “More particularly, in one embodiment, FIG. 2A shows a machine learning implementation where the "answer ranking" module 60 includes a trained model component 70 produced using a machine learning techniques from prior data. The prior data may encode information on features of candidate answers, the features of passages the candidate answers come, the scores given to them by Candidate Answer Scoring modules 40, and whether the candidate answer was correct or not. In other words, machine learning algorithms can be applied to the entire content of the CASes together with the information about correctness of the candidate answer. Such prior data is readily available for instance in technical services support functions, or in more general setting on Internet, where many websites list questions with correct answers.”)(e.g., paragraph [0037] – and paragraphs [0037], [0088] and [0093] and of the incorporated by reference PG Pub No. 2011/0125734 A1)
generate, based on the target aggregation information, a target answer corresponding to the target question. (“Based on the application of the queries to the corpus of data, a set of hypotheses, or candidate answers to the input question, are generated by looking across the corpus of data for portions of the corpus of data that have some potential for containing a valuable response to the input question.” “This management may comprise any operation that modifies the corpus or corpora with regard to the evidence passages associated with candidate answers generated for an input question. That is, the QA system may process an input question and generate a set of candidate answers and/or a final answer that is returned to the submitter of the input question. Affinity information, confidence measures, user feedback information, and the like, may be used to determine how well the input question was answered by the various candidate answers and/or final answer as well how useful the evidence passages that are the sources of the candidate answers, or provide supporting evidence for the candidate answers, actually do support the candidate answer as a correct answer for the input question.”)(e.g., figures 7 and 9 and paragraphs [0041], [0093] and [0094]).
Regarding claim 21, Berajawala discloses an apparatus, comprising: a memory configured to store instructions; and one or more processors coupled to the memory and configured to execute the instructions to cause the apparatus to: (e.g., paragraphs [0006], [0037] and claim 20)
retrieve, based on an input target question, k support documents related to the target question, wherein k is a positive integer greater than 1; (“Categorizing the questions, such as in terms of roles, type of information, tasks, or the like, associated with the question, in each document of a corpus of data may allow the QA system to more quickly and efficiently identify documents containing content related to a specific query.” – “documents” plural is disclosed and is considered to be “k documents is a positive integer greater than 1”)(e.g., paragraphs [0035], [0098] and [0103])
perform, based on the k support documents, information aggregation to obtain target aggregation information, (“Documents may be clustered based on similar topics in a manner generally known in the art of question answering. A lookup operation on these clusters may be performed to identify clusters related to topics in an input question, e.g., clusters associated with “Manchester United” or “Soccer Teams” since it can be deduced from the question that Manchester United is a soccer team. Documents that reside within the identified cluster(s) are given a higher passage affinity score than documents that reside outside the cluster. Moreover, questions associated with a given document or portion of content in the corpus, that have a same lexical answer type (LAT) and focus as information presented in the document are given a higher affinity score.” “the QA system may identify another QA system pipeline that operates on a corpus having higher affinity scored documents for the input question in a manner similar to that described above with regard to identifying related questions. That is, the QA system may have access to the question/evidence passage/affinity score mappings for documents in the various other corpora used by other QA system pipelines. From this mapping, a determination of a corpus that contains evidence passages (documents) that have a higher affinity to questions similar to that of the input question may be made and the QA system pipeline utilizing the corpus may be identified. The input question may then be redirected to the identified QA system pipeline for processing and results returned to the original QA system which may then provide them to the submitter of the original input question.”)(e.g., paragraphs [0067] and [0103]) wherein the information aggregation comprises information encoding and exchanging; and (“FIG. 1 depicts a schematic diagram of one illustrative embodiment of a question/answer creation (QA) system 100 in a computer network 102. One example of a question/answer generation which may be used in conjunction with the principles described herein is described in U.S. Patent Application Publication No. 2011/0125734, which is herein incorporated by reference in its entirety.” U.S. Patent Application Publication No. 2011/0125734 A1 provides: “More particularly, in one embodiment, FIG. 2A shows a machine learning implementation where the "answer ranking" module 60 includes a trained model component 70 produced using a machine learning techniques from prior data. The prior data may encode information on features of candidate answers, the features of passages the candidate answers come, the scores given to them by Candidate Answer Scoring modules 40, and whether the candidate answer was correct or not. In other words, machine learning algorithms can be applied to the entire content of the CASes together with the information about correctness of the candidate answer. Such prior data is readily available for instance in technical services support functions, or in more general setting on Internet, where many websites list questions with correct answers.”)(e.g., paragraph [0037] – and paragraphs [0037], [0088] and [0093] and of the incorporated by reference PG Pub No. 2011/0125734 A1)
generate, based on the target aggregation information, a target answer corresponding to the target question. (“Based on the application of the queries to the corpus of data, a set of hypotheses, or candidate answers to the input question, are generated by looking across the corpus of data for portions of the corpus of data that have some potential for containing a valuable response to the input question.” “This management may comprise any operation that modifies the corpus or corpora with regard to the evidence passages associated with candidate answers generated for an input question. That is, the QA system may process an input question and generate a set of candidate answers and/or a final answer that is returned to the submitter of the input question. Affinity information, confidence measures, user feedback information, and the like, may be used to determine how well the input question was answered by the various candidate answers and/or final answer as well how useful the evidence passages that are the sources of the candidate answers, or provide supporting evidence for the candidate answers, actually do support the candidate answer as a correct answer for the input question.”)(e.g., figures 7 and 9 and paragraphs [0041], [0093] and [0094]).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 16-19 and 26-29 are rejected under 35 U.S.C. 103 as being unpatentable over Berajawala in view of Leviathan et al. (U.S. Patent No. 9,842,166 B1, hereinafter referred to as “Leviathan”).
Regarding claim 6, Berajawala discloses the method of claim 1. However, Berajawala, alone, does not appear to specifically disclose further comprising: presenting the target answer and answer source information in a target form, wherein the target form comprises a question answering card, and wherein the answer source information comprises source information of support documents respectively corresponding to answer segments of the target answer.
On the other hand, Berajawal in view of Leviathan, which relates to semi-structured question answering system (title), does disclose further comprising: presenting the target answer and answer source information in a target form, wherein the target form comprises a question answering card, and wherein the answer source information comprises source information of support documents respectively corresponding to answer segments of the target answer. (“A ranked listing of these related previously processed questions may be generated based on their degree of affinity to the evidence passage and may be displayed in a user selectable manner such that additional information with regard to the answers to these related previously processed questions may be accessed by the user. In this way, the user is able to explore other questions of interest that are directed to a similar topic, domain, area of interest, or the like.”)(Berajawala: e.g., figures 6, 7 and 9 and paragraph [0087])(“FIG. 3 illustrates an example of a user interface 300 illustrating an inferred search result based on an association with other related facts in the data graph, consistent with disclosed implementations. A search engine, such as search engine 110 of FIG. 1, may generate information used to display user interface 300 in responding to a request to show search results for a query that requests a specific piece of information about an entity (e.g., a target entity) in the graph-based data store. The user interface 300 may include search results 305 from the graph-based data store. The search results 305 may represent facts about an entity for which there is no direct answer to the search or relationship in the data graph associated with the search. In the example of FIG. 3, the query requested the grandfather of Karina Jones, but the information from the data graph alone does not include a mechanism to determine this information. However, the search engine can be enhanced with an algorithm used to determine this information based on information in the data graph. The user interface 300 may also include search result 310 from a document source. The search result 310 may thus represent documents determined to be responsive to the terms of the query.”)(Leviathan: e.g., figure 3 and col 9 lines 8-29).
Berajawala discloses corpus management based on question affinity. In Berajawala, a question Is processed by a Question Answering system to generate an answer to the question and a supporting evidence passage. However, Berajawala does not appear to specifically disclose the display and therefore, does not appear to specifically disclose a question answering card showing the answer and the source information. On the other hand, Leviathan does disclose a system where in response to a user asking a question, results are shown that includes the answer, along with information sources that support the answer. E.g., figure 3. This provides the user with the enhanced experience to see the answer along with further retrieving relevant documents related to the answer. Therefore, it would have been obvious to one of ordinary before the effective filing date of Applicant’s claimed invention to incorporate the display of showing the answer and the underlying data sources to the user to effectively convey the information of Berajawala and to provide an effective way for users to pose questions and to receive the answers to the questions by also seeing the underlying sources that support the answer data to provide an enhanced user experience.
Regarding claim 7, Berajawala in view of Leviathan discloses the method of claim 6. Leviathan further discloses wherein the source information comprises identities of a plurality of sources of the answer source information. (Berajawala: e.g., paragraph [0087])(result can show the answer 305, along with the sources of information 310)(figure 3).
Regarding claim 8, Berajawala in view of Leviathan discloses the method of claim 6. Berajawala in view of Leviathan further discloses further comprising displaying, on a current interface in a superimposed manner, description information corresponding to one of the answer segments when receiving a trigger signal corresponding to the one of the answer segments. (questions can be asked and answers / references are provided that provide support to answer.)(Berjawala: e.g., figures 5A-5D)(display provides answer portion, along with selectable links that can be selected to show underlying data.)(Leviathan: e.g., figure 3).
Regarding claim 9, Berajawala in view of Leviathan discloses the method of claim 6. Berajawala in view of Leviathan further discloses further comprising jumping to and displaying a first source web page corresponding to the one of the answer segments when receiving a trigger signal corresponding to a first one of the answer segments. (questions can be asked and answers / references are provided that provide support to answer.)(Berjawala: e.g., figures 5A-5D)(display provides answer portion, along with selectable links that can be selected to show underlying data. When link is selected (corresponds to answer segment), the link opens)(Leviathan: e.g., figure 3).
Claims 26-29 have substantially similar limitations as stated in claims 6-9, respectively; therefore, they are rejected under the same subject matter.
Allowable Subject Matter
Claims 2-5, 20 and 22-25 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: claim 2 requires “concatenating each of the k support documents and the target question to obtain k combination pairs; encoding the k combination pairs to obtain k first vectors respectively corresponding to the k combination pairs; concatenating the k first vectors to obtain a second vector; and fusing, based on the second vector, information in the k support documents to obtain a third vector indicating the target aggregation information.” These features when considered in combination with the required features of claim 1 are not disclosed in the prior art, alone or in combination. Claims 3-4 depend from claim 2, and are considered allowable for at least the same reasons as claim 2.
Claim 5 requires: concatenating each of the k support documents and the target question to obtain k combination pairs; determining, based on the target aggregation information, a first probability distribution and a second probability distribution, wherein the first probability distribution indicates a first probability that each word in a preset vocabulary is used as a currently output decoding word, and wherein the second probability distribution indicates a probability that each word in the k support documents is used as a currently output decoding word; fusing the first probability distribution and the second probability distribution to obtain a third probability distribution; and decoding the third probability distribution to obtain the target answer corresponding to the target question.” These features when considered in combination with the required features of claim 1 are not disclosed in the prior art, alone or in combination.
Claims 20 and 22-25 have substantially similar limitations as stated in claims 2 and 2-5, respectively; therefore, they objected to as allowable for substantially similar reasons.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon is considered pertinent to applicant's disclosure.
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/RICHARD L BOWEN/ Primary Examiner, Art Unit 2165