DETAILED ACTION
In response to communication filed on 05 June 2025, this is first Office Action of the merits.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 for JP 2024-093833.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Interpretation
Claims 1 and 9 recite “using the initial passage and the additional passage”. These claim limitations appear to be citing intended use in terms of what the initial passage and the additional passages are used for. Examiner suggests amending the claim to recite the functionality performed by the claimed method, instead of reciting what the claim elements are used for.
Claims 4 and 12 recite “using a score”. These claim limitations appear to be citing intended use in terms of what the score is used for. Examiner suggests amending the claim to recite the functionality performed by the claimed method, instead of reciting what the claim elements are used for.
Claims 7-8 and 15 recite “using a language model”. These claim limitations appear to be citing intended use in terms of what the language model is used for. Examiner suggests amending the claim to recite the functionality performed by the claimed method, instead of reciting what the claim elements are used for.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-8 are recited as being directed to an “apparatus”. Claims 9-15 are recited as being directed to a “method”.
Regarding claim 1,
Step 2A: Prong One:
Claim 1 recites limitations:
… with reference to association information including a strength of association between the passages included in the passage set, and the initial passage; and…
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to refer to the association information that includes a strength of association between plurality of passages.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 1 further recites limitations:
An information processing apparatus comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 1 further recites limitations:
acquire a query;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly, the claim limitations as a whole above appear to be gathering data and do not appear to integrate the abstract idea into a practical application.
Claim 1 further recites limitations:
perform first retrieval processing of retrieving an initial passage related to the query from a passage set including a plurality of passages;
perform second retrieval processing of retrieving an additional passage from the passage set…
perform third retrieval processing of performing retrieval processing using the initial passage and the additional passage.
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data being received and do not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 1 further recites limitations:
An information processing apparatus comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 1 further recites limitations:
acquire a query;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Receiving or transmitting data over a network, e.g., using the Internet to gather data” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 1 further recites limitations:
perform first retrieval processing of retrieving an initial passage related to the query from a passage set including a plurality of passages;
perform second retrieval processing of retrieving an additional passage from the passage set…
perform third retrieval processing of performing retrieval processing using the initial passage and the additional passage.
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Storing and retrieving information in memory” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 9 incorporates substantively all the limitations of claim 1 in a method form (wherein claim limitations - An information processing method comprising: by at least one processor configured to execute the instructions, the instructions being stored in at least one memory: in Step 2A: Prong Two as these claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application. These claim limitations in Step 2B appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and is rejected under the same rationale.
Regarding claim 2,
Step 2A: Prong One:
Claim 2 recites limitations:
… with reference to a directed graph including one or a plurality of edges defined by one or a plurality of passage pairs included in the association information.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to refer to a directed graph including one or a plurality of edges defined by one or a plurality of passage pairs included in the association information.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 2 further recites limitations:
… the processor executes the instructions…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 2 further recites limitations:
wherein, in the second retrieval processing,… to retrieve the additional passage from the passage set.
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data being received and do not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 2 further recites limitations:
… the processor executes the instructions…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 2 further recites limitations:
wherein, in the second retrieval processing,… to retrieve the additional passage from the passage set.
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Storing and retrieving information in memory” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 10 incorporates substantively all the limitations of claim 2 in a method form and is rejected under the same rationale.
Regarding claim 3,
Step 2A: Prong One:
Claim 3 recites limitations:
… with reference to a first score which indicates a strength of association between a passage pair defining each of the one or plurality of edges and is calculated in advance without referring to the query, and a second score which indicates the strength of association between the passage pair defining each of the one or plurality of edges and is calculated with reference to the query.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to refer to a first score that indicates a strength of association between passage pair that is calculated without referring to the query data and a second score that indicates the strength of association between the passage pair based on the query data.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 3 further recites limitations:
… at least one processor executes the instructions…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 3 further recites limitations:
wherein, in the second retrieval processing,… to retrieve the additional passage from the passage set.
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data being received and do not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 3 further recites limitations:
… at least one processor executes the instructions…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 3 further recites limitations:
wherein, in the second retrieval processing,… to retrieve the additional passage from the passage set.
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Storing and retrieving information in memory” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 11 incorporates substantively all the limitations of claim 3 in a method form and is rejected under the same rationale.
Regarding claim 4,
Step 2A: Prong One:
Claim 4 recites limitations:
… by using a score obtained by aggregating the first score and the second score for each of the one or plurality of edges.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine refer to a scores obtained based on aggregation of scores for each of the one or plurality of edges.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 4 further recites limitations:
… at least one processor executes the instructions…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 4 further recites limitations:
wherein, in the second retrieval processing,… to retrieve the additional passage from the passage set.
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data being received and do not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 4 further recites limitations:
… at least one processor executes the instructions…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 4 further recites limitations:
wherein, in the second retrieval processing,… to retrieve the additional passage from the passage set.
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Storing and retrieving information in memory” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 12 incorporates substantively all the limitations of claim 4 in a method form and is rejected under the same rationale.
Regarding claim 5,
Step 2A: Prong One:
Claim 5 recites limitations:
… with reference to a partial directed graph which is obtained with reference to the initial passage and the association information and forms a part of the directed graph.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to determine refer to a partial directed graph which is obtained with reference to the initial passage and the association information and forms a part of the directed graph .
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 5 further recites limitations:
… at least one processor executes the instructions…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 5 further recites limitations:
wherein, in the second retrieval processing,… to retrieve the additional passage from the passage set.
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data being received and do not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 5 further recites limitations:
… at least one processor executes the instructions…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 5 further recites limitations:
wherein, in the second retrieval processing,… to retrieve the additional passage from the passage set.
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Storing and retrieving information in memory” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 13 incorporates substantively all the limitations of claim 5 in a method form and is rejected under the same rationale.
Regarding claim 6,
Step 2A: Prong One:
Claim 6 recites limitations:
generate the passage set including the plurality of passages included in the sentence group; and
calculate the association information including the strength of association between the plurality of passages included in the passage set.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to generate the passage set and calculate the association information including the strength of association between the plurality of passages.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 6 further recites limitations:
the at least one processor executes the instructions to further
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 6 further recites limitations:
acquire input data including a sentence group;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly, the claim limitations as a whole above appear to be gathering data and do not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 6 further recites limitations:
the at least one processor executes the instructions to further
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 6 further recites limitations:
acquire input data including a sentence group;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Receiving or transmitting data over a network, e.g., using the Internet to gather data” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 14 incorporates substantively all the limitations of claim 6 in a method form and is rejected under the same rationale.
Regarding claim 7,
Step 2A: Prong One:
Claim 7 recites limitations:
wherein, in the calculation of the association information… to calculate the association information…
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to calculate the association information.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 7 further recites limitations:
… the at least one processor executes the instructions…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 7 further recites limitations:
… by using a language model.
These claim limitations are recited at a high level of generality and amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 7 further recites limitations:
… the at least one processor executes the instructions…
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 7 further recites limitations:
… by using a language model.
These claim limitations are recited at a high level of generality and amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer and these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 15 incorporates substantively all the limitations of claim 7 in a method form and is rejected under the same rationale.
Regarding claim 8,
Step 2A: Prong One:
Claim 8 recites limitations:
generate a passage set including a plurality of passages included in the sentence group; and
calculate,… association information which includes a strength of association between the plurality of passages included in the passage set and is referred to in retrieval processing; and.
These claim limitations appear to be reciting a “Mental Process” including evaluation.
A human mind can mentally evaluate to generate the passage set and calculate the association information including the strength of association between the plurality of passages.
Step 2A - Prong Two:
The abstract idea does not appear to be integrated into a practical application with the recitation of the following claim language.
Claim 8 further recites limitations:
An information processing apparatus comprising:
at least one memory configured to store instructions; and at least one processor configured to execute the instructions to:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to integrate the abstract idea into a particular practical application.
Claim 8 further recites limitations:
acquire input data including a sentence group;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly, the claim limitations as a whole above appear to be gathering data and do not appear to integrate the abstract idea into a practical application.
Claim 8 further recites limitations:
… by using a language model,…
These claim limitations are recited at a high level of generality and amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 8 further recites limitations:
store the association information in association with the plurality of passages.
These claim limitations as a whole have been identified as insignificant extra-solution activity specifically a post solution activity. Per MPEP 2106.05(g) “when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim”. MPEP in 2016.05(g) also provides examples of activities that the courts have found to be insignificant extra-solution activity of which one of them is “Consulting and updating an activity log”. Similarly the above recited claim limitations as a whole above appear to be reciting the process of storing information and does not appear to integrate the abstract idea into a practical application.
Step 2B:
The abstract idea does not appear to be significantly more with the recitation of the following claim language.
Claim 8 further recites limitations:
An information processing apparatus comprising:
at least one memory configured to store instructions; and at least one processor configured to execute the instructions to:
These claim limitations appear to be to merely add the use of generic computer components which are merely executing the abstract idea within a computer device (see MPEP 2106.05(b)) and do not appear to amount to significantly more.
Claim 8 further recites limitations:
acquire input data including a sentence group;
These claim limitations as a whole have been identified as insignificant extra-solution activity. Per MPEP 2106.05(g) “An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent”. Similarly the claim limitations as a whole above appear to be gathering data in terms of requests, data and content being received and appear to be conventional computer functionality. Also, MPEP 2106.05(d)(II) has identified “Receiving or transmitting data over a network, e.g., using the Internet to gather data” as conventional computer technology. Similarly, the claim limitations identified above appear to be receiving data. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
Claim 8 further recites limitations:
… by using a language model,…
These claim limitations are recited at a high level of generality and amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 8 further recites limitations:
store the association information in association with the plurality of passages.
These claim limitations as a whole have been identified as insignificant extra-solution activity specifically a post solution activity. Per MPEP 2106.05(g) “when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim”. MPEP in 2106.05(g) also provides examples of activities that the courts have found to be insignificant extra-solution activity of which one of them is “Consulting and updating an activity log”. Similarly the claim limitations as a whole above appear to be reciting the process of storing information. Also, MPEP 2106.05(d)(II) has identified “Storing and retrieving information in memory” as conventional computer technology. Similarly, the claim limitations identified above appear to be storing information. As a result, these claim limitations as a whole do not appear to amount to significantly more than the abstract idea itself.
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, 6-10 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Khanwalkar et al. (US 12,393,620 B1, hereinafter “Khanwalkar”) in view of AIShikh (US 2025/0371386 A1, hereinafter “AIShikh”).
Regarding claim 1, Khanwalkar teaches
An information processing apparatus comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: (see Khanwalkar, [col 2 lines 10-14] “including as a process; an apparatus; a system; a composition of matter… a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor).
acquire a query; (see Khanwalkar, [col 7 lines 23-31] “A large proportion of the search queries may include questions (also referred to as question queries, versus, for example, document queries for study materials) for which learners may need assistance to obtain answers and step-by-step explanations… students come to the online learning platform and enter (e.g., via copy/paste) questions into the search functionality provided by the online learning platform (e.g., search bar 114) to obtain an answer to the inputted question”; [col 33 lines 27-28] “The process begins at 802 when a question is received”; [col 36 line 49] “The process begins at 952 when a question is received”).
perform first retrieval processing of retrieving an initial passage related to the query from a passage set including a plurality of passages; (see Khanwalkar, [col 36 lines 6-27] “process 900 is executed subsequent to performing step 806 of process 800… when an answer and explanation to a question are received… using the answer and the explanation, a passage index is queried for relevant candidate passages. At 906, measures of semantic similarity between the candidate passages and the answer and/or explanation are determined… a subset of the candidate passages is selected… the top N passages that are extracted from different documents are selected ( e.g., subset of references that are of highest relevance based on highest semantic similarity and that are also extracted from different sources/documents).”; [col 36 lines 49-51] “At 954, using the question, a passage index is queried for relevant passages” – plurality of passages are retrieved).
perform second retrieval processing of retrieving an additional passage from the passage set with reference to measures of semantic similarity… (see Khanwalkar, [col 36 lines 6-27] “process 900 is executed subsequent to performing step 806 of process 800… when an answer and explanation to a question are received… using the answer and the explanation, a passage index is queried for relevant candidate passages. At 906, measures of semantic similarity between the candidate passages and the answer and/or explanation are determined… a subset of the candidate passages is selected… the top N passages that are extracted from different documents are selected ( e.g., subset of references that are of highest relevance based on highest semantic similarity and that are also extracted from different sources/documents).”; [col 36 lines 49-51] “At 954, using the question, a passage index is queried for relevant passages” – plurality of passages are retrieved) the initial passage; and (see Khanwalkar, [col 36 lines 6-27] “process 900 is executed subsequent to performing step 806 of process 800… when an answer and explanation to a question are received… using the answer and the explanation, a passage index is queried for relevant candidate passages. At 906, measures of semantic similarity between the candidate passages and the answer and/or explanation are determined… a subset of the candidate passages is selected… the top N passages that are extracted from different documents are selected ( e.g., subset of references that are of highest relevance based on highest semantic similarity and that are also extracted from different sources/documents).”; [col 36 lines 49-51] “At 954, using the question, a passage index is queried for relevant passages” – plurality of passages are retrieved).
… the initial passage and the additional passage (see Khanwalkar, [col 36 lines 6-27] “process 900 is executed subsequent to performing step 806 of process 800… when an answer and explanation to a question are received… using the answer and the explanation, a passage index is queried for relevant candidate passages. At 906, measures of semantic similarity between the candidate passages and the answer and/or explanation are determined… a subset of the candidate passages is selected… the top N passages that are extracted from different documents are selected ( e.g., subset of references that are of highest relevance based on highest semantic similarity and that are also extracted from different sources/documents).”; [col 36 lines 49-51] “At 954, using the question, a passage index is queried for relevant passages” – plurality of passages are retrieved).
Khanwalkar does not explicitly teach association information including a strength of association between the passages included in the passage set, and perform third retrieval processing of performing retrieval processing using the initial passage and the additional passage.
However, AlShikh discloses knowledge graph and teaches
association information including a strength of association between the passages included in the passage set, and (see AlShikh, [0098] “a knowledge graph produced by knowledge graph generation with summarization 310 may include one or more trees like tree 800 and nodes and relationships corresponding to textual passages and relationships such as shown in FIG. 5… the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0102] “includes retrieving textual passages and textual summaries from a knowledge graph”).
perform third retrieval processing of performing retrieval processing using relationships between rankings and summaries (see AlShikh, [0103]-[0104] “a first textual passage based on a first ranking… a third textual summary based on a second ranking with respect to the natural language textual sequence are retrieved… third textual passage is retrieved based on a first ranking with respect to a relationship of the third textual passage and the natural language textual sequence and a third textual summary summarizing textual information in a vicinity of the third textual passage is retrieved”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of strength of association between the passages, third retrieval process, directed graph, edges within passage pairs, sentence group, as being disclosed and taught by AlShikh, in the system taught by Khanwalkar to yield the predictable results of providing improved input context to a large language model which utilize less resources to effectively retrieve the relevant information (see AlShikh, [0034] “Problems such as these may be mitigated by using a knowledge graph incorporating textual summaries to provide improved input context to a language model which may utilize less resources, such as computing, memory, and power. The quality of an answer in part depends on the quality of retrieval of relevant information, so improving the retrieval of information may also improve the quality of the resulting answer”).
Claim 9 incorporates substantively all the limitations of claim 1 in a method form (see Khanwalkar, [col 2 lines 19-22] “a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time”; [col 38 line 61] “A method, comprising”) and is rejected under the same rationale.
Regarding claim 2, the proposed combination of Khanwalkar and AlShikh teaches
wherein, in the second retrieval processing, the processor executes the instructions to retrieve the additional passage from the passage set reference to measures of semantic similarity (see Khanwalkar, [col 36 lines 6-27] “process 900 is executed subsequent to performing step 806 of process 800… when an answer and explanation to a question are received… using the answer and the explanation, a passage index is queried for relevant candidate passages. At 906, measures of semantic similarity between the candidate passages and the answer and/or explanation are determined… a subset of the candidate passages is selected… the top N passages that are extracted from different documents are selected ( e.g., subset of references that are of highest relevance based on highest semantic similarity and that are also extracted from different sources/documents).”; [col 36 lines 49-51] “At 954, using the question, a passage index is queried for relevant passages” – plurality of passages are retrieved; [col 2 lines 10-14] “including as a process; an apparatus; a system; a composition of matter… a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor) a directed graph including (see AlShikh, [0091] “The relationships in knowledge graph 500 include those that are directional”) one or a plurality of edges defined by one or a plurality of passage pairs included in the association information (see AlShikh, [0098] “the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve by comparing the natural language textual sequence ( or a portion thereof) against textual passages in the knowledge graph… to compute pairwise similarity or distance between embeddings of textual passages and embedding(s) of the natural language textual sequence”). The motivation for the proposed combination is maintained.
Claim 10 incorporates substantively all the limitations of claim 2 in a method form and is rejected under the same rationale.
Regarding claim 6, the proposed combination of Khanwalkar and AlShikh teaches
the at least one processor executes the instructions to further (see Khanwalkar, [col 2 lines 10-14] “including as a process; an apparatus; a system; a composition of matter… a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor).
acquire input data including a sentence group; (see AlShikh, [0069] “a textual summary may be obtained that summarizes text in a vicinity of one or more textual passages. The vicinity may, for example, be based on a certain number of tokens, words, or sentences that are before and/or after one or more textual passages”).
generate the passage set including the plurality of passages (see Khanwalkar, [col 23 lines 51-53] “the passage index includes passages that are extracted from a document store such as knowledge base 148”) included in the sentence group; and (see AlShikh, [0069] “a textual summary may be obtained that summarizes text in a vicinity of one or more textual passages. The vicinity may, for example, be based on a certain number of tokens, words, or sentences that are before and/or after one or more textual passages”).
calculate the association information including the strength of association between the plurality of passages included in the passage set (see AlShikh, [0098] “a knowledge graph produced by knowledge graph generation with summarization 310 may include one or more trees like tree 800 and nodes and relationships corresponding to textual passages and relationships such as shown in FIG. 5… the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0102] “includes retrieving textual passages and textual summaries from a knowledge graph”; [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve by comparing the natural language textual sequence ( or a portion thereof) against textual passages in the knowledge graph… to compute pairwise similarity or distance between embeddings of textual passages and embedding(s) of the natural language textual sequence”). The motivation for the proposed combination is maintained.
Claim 14 incorporates substantively all the limitations of claim 6 in a method form and is rejected under the same rationale.
Regarding claim 7, the proposed combination of Khanwalkar and AlShikh teaches
wherein, in the calculation of the association information, (see AlShikh, [0098] “a knowledge graph produced by knowledge graph generation with summarization 310 may include one or more trees like tree 800 and nodes and relationships corresponding to textual passages and relationships such as shown in FIG. 5… the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0102] “includes retrieving textual passages and textual summaries from a knowledge graph”; [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve by comparing the natural language textual sequence ( or a portion thereof) against textual passages in the knowledge graph… to compute pairwise similarity or distance between embeddings of textual passages and embedding(s) of the natural language textual sequence”) the at least one processor executes the instructions to (see Khanwalkar, [col 2 lines 10-14] “including as a process; an apparatus; a system; a composition of matter… a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor) calculate the association information (see AlShikh, [0098] “a knowledge graph produced by knowledge graph generation with summarization 310 may include one or more trees like tree 800 and nodes and relationships corresponding to textual passages and relationships such as shown in FIG. 5… the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0102] “includes retrieving textual passages and textual summaries from a knowledge graph”; [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve by comparing the natural language textual sequence ( or a portion thereof) against textual passages in the knowledge graph… to compute pairwise similarity or distance between embeddings of textual passages and embedding(s) of the natural language textual sequence”) by using a language model (see Khanwalkar, [col 2 lines 43-46] “Described herein are embodiments and example implementations of utilizing generative models (e.g., models with transformer-based architectures, such as Large Language Models (LLMs)”). The motivation for the proposed combination is maintained.
Claim 15 incorporates substantively all the limitations of claim 7 in a method form and is rejected under the same rationale.
Regarding claim 8, Khanwalkar teaches
An information processing apparatus comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: (see Khanwalkar, [col 2 lines 10-14] “including as a process; an apparatus; a system; a composition of matter… a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor).
generate a passage set including a plurality of passages included in the passage index… (see Khanwalkar, [col 23 lines 51-53] “the passage index includes passages that are extracted from a document store such as knowledge base 148”).
… by using a language model,… (see Khanwalkar, [col 2 lines 43-46] “Described herein are embodiments and example implementations of utilizing generative models (e.g., models with transformer-based architectures, such as Large Language Models (LLMs)”) and is referred to in retrieval processing; and (see Khanwalkar, [col 36 lines 6-27] “process 900 is executed subsequent to performing step 806 of process 800… when an answer and explanation to a question are received… using the answer and the explanation, a passage index is queried for relevant candidate passages. At 906, measures of semantic similarity between the candidate passages and the answer and/or explanation are determined… a subset of the candidate passages is selected… the top N passages that are extracted from different documents are selected ( e.g., subset of references that are of highest relevance based on highest semantic similarity and that are also extracted from different sources/documents).”; [col 36 lines 49-51] “At 954, using the question, a passage index is queried for relevant passages” – plurality of passages are retrieved).
Khanwalkar does not explicitly teach acquire input data including a sentence group; the sentence group; calculate, association information which includes a strength of association between the plurality of passages included in the passage set, store the association information in association with the plurality of passages.
However, AlShikh discloses knowledge graph and teaches
acquire input data including a sentence group; (see AlShikh, [0069] “a textual summary may be obtained that summarizes text in a vicinity of one or more textual passages. The vicinity may, for example, be based on a certain number of tokens, words, or sentences that are before and/or after one or more textual passages”).
the sentence group; (see AlShikh, [0069] “a textual summary may be obtained that summarizes text in a vicinity of one or more textual passages. The vicinity may, for example, be based on a certain number of tokens, words, or sentences that are before and/or after one or more textual passages”).
calculate,… association information which includes a strength of association between the plurality of passages included in the passage set (see AlShikh, [0098] “a knowledge graph produced by knowledge graph generation with summarization 310 may include one or more trees like tree 800 and nodes and relationships corresponding to textual passages and relationships such as shown in FIG. 5… the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0102] “includes retrieving textual passages and textual summaries from a knowledge graph”; [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve by comparing the natural language textual sequence ( or a portion thereof) against textual passages in the knowledge graph… to compute pairwise similarity or distance between embeddings of textual passages and embedding(s) of the natural language textual sequence”).
store the association information in association with the plurality of passages (see AIShikh, [0034] “the resulting knowledge graph may be able to capture relationships between textual passages across several dimensions. The resulting knowledge graph may be able to more effectively store numerical data, tables, and code”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of strength of association between the passages, sentence group and storing the association information as being disclosed and taught by AlShikh, in the system taught by Khanwalkar to yield the predictable results of providing improved input context to a large language model which utilize less resources to effectively retrieve the relevant information (see AlShikh, [0034] “Problems such as these may be mitigated by using a knowledge graph incorporating textual summaries to provide improved input context to a language model which may utilize less resources, such as computing, memory, and power. The quality of an answer in part depends on the quality of retrieval of relevant information, so improving the retrieval of information may also improve the quality of the resulting answer”).
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Khanwalkar in view of AIShikh further in view of Boxwell et al. (US 2020/0218988 A1, hereinafter “Boxwell”).
Regarding claim 3, the proposed combination of Khanwalkar and AlShikh teaches
wherein, in the second retrieval processing, the at least one processor executes the instructions to retrieve the additional passage from the passage set with reference to measures of semantic similarity… (see Khanwalkar, [col 36 lines 6-27] “process 900 is executed subsequent to performing step 806 of process 800… when an answer and explanation to a question are received… using the answer and the explanation, a passage index is queried for relevant candidate passages. At 906, measures of semantic similarity between the candidate passages and the answer and/or explanation are determined… a subset of the candidate passages is selected… the top N passages that are extracted from different documents are selected ( e.g., subset of references that are of highest relevance based on highest semantic similarity and that are also extracted from different sources/documents).”; [col 36 lines 49-51] “At 954, using the question, a passage index is queried for relevant passages” – plurality of passages are retrieved; [col 2 lines 10-14] “including as a process; an apparatus; a system; a composition of matter… a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor) which indicates a strength of association (see AlShikh, [0098] “a knowledge graph produced by knowledge graph generation with summarization 310 may include one or more trees like tree 800 and nodes and relationships corresponding to textual passages and relationships such as shown in FIG. 5… the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0102] “includes retrieving textual passages and textual summaries from a knowledge graph”) between a passage pair defining each of the one or plurality of edges… (see AlShikh, [0098] “the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve by comparing the natural language textual sequence ( or a portion thereof) against textual passages in the knowledge graph… to compute pairwise similarity or distance between embeddings of textual passages and embedding(s) of the natural language textual sequence”; [0034] “Problems such as these may be mitigated by using a knowledge graph incorporating textual summaries to provide improved input context to a language model… the resulting knowledge graph may be able to capture relationships between textual passages across several dimensions. The resulting knowledge graph may be able to more effectively store numerical data, tables, and code”) which indicates the strength of association (see AlShikh, [0098] “a knowledge graph produced by knowledge graph generation with summarization 310 may include one or more trees like tree 800 and nodes and relationships corresponding to textual passages and relationships such as shown in FIG. 5… the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0102] “includes retrieving textual passages and textual summaries from a knowledge graph”) between the passage pair defining each of the one or plurality of edges and is calculated with reference to the query (see AlShikh, [0098] “the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve by comparing the natural language textual sequence (or a portion thereof) against textual passages in the knowledge graph… to compute pairwise similarity or distance between embeddings of textual passages and embedding(s) of the natural language textual sequence” – the knowledge graph utilizes natural language textual sequence that are interpreted as query).
The proposed combination of Khanwalkar and AlShikh does not explicitly teach a first score and is calculated in advance without referring to the query, and a second score.
However, Boxwell discloses knowledge graph and scoring and teaches
a first score… (see Boxwell, [0039] “The scores obtained from the various reasoning algorithms… Each resulting score is then weighted against a statistical model”) and is calculated in advance without referring to the query, (see Boxwell, [0018] “such structured data may be stored in a form of a knowledge graph. A knowledge graph is a structure used to model pairwise relations between objects or syntactic entities in a passage. A knowledge graph in this context can refer to a collection of entities or nodes and a collection of relations or edges that connect pairs of nodes” – knowledge graph is generated without referring to the query) and a second score (see Boxwell, [0042] “their relative scores or confidence measures calculated”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of scoring and calculating association without referring to the query as being disclosed and taught by Boxwell, in the system taught by the proposed combination of Khanwalkar and AlShikh to yield the predictable results of efficiently generating hypotheses, improving knowledge for machine learning process and enabling decision making (see Boxwell, [0021]-[0028] “a cognitive system is a specialized computer system, or set of computer systems… Generate and evaluate hypotheses… Improve knowledge and learn with each iteration and interaction through machine learning processes… Enable decision making at the point of impact”).
Claim 11 incorporates substantively all the limitations of claim 3 in a method form and is rejected under the same rationale.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Khanwalkar, AIShikh and Boxwell further in view of Beller et al. (US 2018/0053098 A1, hereinafter “Beller”).
Regarding claim 4, the proposed combination of Khanwalkar, AIShikh and Boxwell teaches
wherein, in the second retrieval processing, the at least one processor executes the instructions to retrieve the additional passage from the passage set… (see Khanwalkar, [col 36 lines 6-27] “process 900 is executed subsequent to performing step 806 of process 800… when an answer and explanation to a question are received… using the answer and the explanation, a passage index is queried for relevant candidate passages. At 906, measures of semantic similarity between the candidate passages and the answer and/or explanation are determined… a subset of the candidate passages is selected… the top N passages that are extracted from different documents are selected ( e.g., subset of references that are of highest relevance based on highest semantic similarity and that are also extracted from different sources/documents).”; [col 36 lines 49-51] “At 954, using the question, a passage index is queried for relevant passages” – plurality of passages are retrieved; [col 2 lines 10-14] “including as a process; an apparatus; a system; a composition of matter… a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor) the first score and (see Boxwell, [0039] “The scores obtained from the various reasoning algorithms… Each resulting score is then weighted against a statistical model”) the second score (see Boxwell, [0042] “their relative scores or confidence measures calculated”) for each of the one or plurality of edges (see Boxwell, [0065] “may utilize the generated one or more N-best passages to represent the labeled edge chosen at block 204. For example, the generated N-best passages may be utilized to assign scores”).
The proposed combination of Khanwalkar, AIShikh and Boxwell does not explicitly teach by using a score obtained by aggregating the first score and the second score.
However, Beller discloses evaluation score for each set of knowledge canvassing and teaches
by using a score obtained by aggregating plurality of scores (see Beller, [0043] “the evaluation scores may be aggregated by averaging all of the evaluation scores”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of aggregating scores as being disclosed and taught by Beller, in the system taught by the proposed combination of Khanwalkar, AIShikh and Boxwell to yield the predictable results of improving results generated by a knowledge canvassing system (see Beller, [0003] “The present disclosure relates to evaluation and training of cognitive computing systems, and more specifically, to techniques and mechanisms for improving the results generated by a knowledge canvassing system”).
Claim 12 incorporates substantively all the limitations of claim 4 in a method form and is rejected under the same rationale.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Khanwalkar in view of AIShikh further in view of Beller et al. (US 2018/0053098 A1, hereinafter “Beller”).
Regarding claim 5, the proposed combination of Khanwalkar and AIShikh teaches
wherein, in the second retrieval processing, the at least one processor executes the instructions to retrieve the additional passage from the passage set with reference to measures of semantic similarity… (see Khanwalkar, [col 36 lines 6-27] “process 900 is executed subsequent to performing step 806 of process 800… when an answer and explanation to a question are received… using the answer and the explanation, a passage index is queried for relevant candidate passages. At 906, measures of semantic similarity between the candidate passages and the answer and/or explanation are determined… a subset of the candidate passages is selected… the top N passages that are extracted from different documents are selected ( e.g., subset of references that are of highest relevance based on highest semantic similarity and that are also extracted from different sources/documents).”; [col 36 lines 49-51] “At 954, using the question, a passage index is queried for relevant passages” – plurality of passages are retrieved; [col 2 lines 10-14] “including as a process; an apparatus; a system; a composition of matter… a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor) with reference to the initial passage and (see Khanwalkar, [col 36 lines 6-27] “process 900 is executed subsequent to performing step 806 of process 800… when an answer and explanation to a question are received… using the answer and the explanation, a passage index is queried for relevant candidate passages. At 906, measures of semantic similarity between the candidate passages and the answer and/or explanation are determined… a subset of the candidate passages is selected… the top N passages that are extracted from different documents are selected ( e.g., subset of references that are of highest relevance based on highest semantic similarity and that are also extracted from different sources/documents).”; [col 36 lines 49-51] “At 954, using the question, a passage index is queried for relevant passages” – plurality of passages are retrieved) the association information and (see AlShikh, [0098] “a knowledge graph produced by knowledge graph generation with summarization 310 may include one or more trees like tree 800 and nodes and relationships corresponding to textual passages and relationships such as shown in FIG. 5… the leaves of the trees included in the knowledge graph may correspond to textual passages and relationships may be represented as edges between the leaves”; [0102] “includes retrieving textual passages and textual summaries from a knowledge graph”) forms a part of the directed graph (see AlShikh, [0091] “The relationships in knowledge graph 500 include those that are directional”).
The proposed combination of Khanwalkar and AIShikh does not explicitly teach a partial directed graph which is obtained.
However, Beller discloses evaluation score for each set of knowledge canvassing and teaches
a partial directed graph which is obtained (see Beller, [0036] “where links is the minimum number of links in the knowledge graph 114 between the knowledge canvassing system output entity 208 and the partially matched benchmark output entity in either direction”; [0037] “partial credit may also be extended to returned passages from the knowledge canvassing system 106 from a benchmark input entity query”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of partial graphs as being disclosed and taught by Beller, in the system taught by the proposed combination of Khanwalkar and AIShikh to yield the predictable results of improving results generated by a knowledge canvassing system (see Beller, [0003] “The present disclosure relates to evaluation and training of cognitive computing systems, and more specifically, to techniques and mechanisms for improving the results generated by a knowledge canvassing system”).
Claim 12 incorporates substantively all the limitations of claim 4 in a method form and is rejected under the same rationale.
Conclusion
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/VAISHALI SHAH/Primary Examiner, Art Unit 2156
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