DETAILED ACTION
In response to communication filed on 08 July 2026, claims 1, 4, 8-13 and 15 are amended. Claims 1-15 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see “Specification”, filed 08 July 2026, have been carefully considered. Based on the amended title, the objection has been withdrawn.
Applicant’s arguments, see “Rejections Under 35 U.S.C. § 101”, filed 08 July 2026, have been carefully considered but are not considered to be persuasive.
APPLICANT’S ARGUMENT: Applicant argues that along these lines, the independent claims indicate the association information is calculated in advance, prior to retrieval processing. This is not merely a recitation of timing, but rather reflects a fundamental change in how the computer system operates. Specifically, by pre-calculating and storing association information between passages using a language model, the system eliminates the need to recalculate the association information during retrieval. These features provide a specific technical improvement by reducing the computational burden during retrieval and improving processing speed. Applicant submits that the claims, as amended, are directed to a specific technological improvement in retrieval processing (i.e., improving processing efficiency by avoiding repeated calculations during retrieval) rather than merely implementing an abstract idea on a generic computer.
EXAMINER’S RESPONSE: Examiner has carefully considered the argument but respectfully disagrees. Regarding the claim limitations “the association information is calculated in advance”. The amended claim language related to the calculation of association information in advance has been identified a mental process based on evaluation. If the information is calculated in advance then it can still be performed mentally based on the evaluation. According to MPEP [2106.05 (a) (II)] "However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology". Therefore these limitations identified as abstract idea cannot be considered to be an improvement in technology. Also, the claim limitations with respect to storing the information have been identified as insignificant extra-solution activity specifically a post solution activity. The functionality of storing an association cannot be considered to be an improvement in technology. 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.
APPLICANT’S ARGUMENT: Applicant further argues that the claims in the present application restructure when association information is calculated (in advance of performing the second retrieval processing, not during retrieval) to avoid unnecessary computational burden during retrieval.
EXAMINER’S RESPONSE: Examiner has carefully considered the argument but respectfully disagrees. Regarding the claim limitations “the association information is calculated in advance”. The amended claim language related to the calculation of association information in advance has been identified a mental process based on evaluation. If the information is calculated in advance then it can still be performed mentally based on the evaluation. According to MPEP [2106.05 (a) (II)] "However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology". Therefore these limitations identified as abstract idea cannot be considered to be an improvement in technology.
Applicant’s arguments, see “Rejections Under 35 U.S.C. § 103”, filed 08 July 2026, have been carefully considered but are not considered to be persuasive.
APPLICANT’S ARGUMENT: Applicant argues that the cited portions of AlShikh relate to relationships involving passages, summaries, and/or a knowledge graph. AlShikh does not disclose information representing a strength of association between passage.
EXAMINER’S RESPONSE: Examiner has carefully considered the argument but respectfully disagrees. According to CFR 1.111 (b), "Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references". Similarly the arguments are general allegations and do not appear to clarify how AlShikh does not teach the above argued limitations. As a result, the above argument is not considered to be persuasive.
The other arguments are related to newly added limitations and are addressed in the rejection below.
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, the association information being calculated in advance of performing the second retrieval processing; 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 and calculation being performed in advance.
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 based on a prompt 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 based on a prompt 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.
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 prior to retrieving of passages based on the association information; 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 and this takes place prior to retrieving the 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 inputting the plurality of passages to 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 inputting the plurality of passages to 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.
Regarding claim 9,
Step 2A: Prong One:
Claim 9 recites limitations:
… with reference to association information including a strength of association between the passages included in the passage set, and the initial passage, the association information being calculated in advance of performing the second retrieval processing…
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 and calculation being performed in advance.
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 9 further recites 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,
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 9 further recites limitations:
acquiring 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 9 further recites limitations:
retrieving an initial passage related to the query from a passage set including a plurality of passages;
retrieving an additional passage from the passage set…
performing retrieval processing based on a prompt including 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 9 further recites 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,
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 9 further recites limitations:
acquiring 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 9 further recites limitations:
retrieving an initial passage related to the query from a passage set including a plurality of passages;
retrieving an additional passage from the passage set…
performing retrieval processing based on a prompt including 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.
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:
… a predetermined number of additional passages having highest scores from the passage set and the score is 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 a predetermined number of passages having highest scores and the scores are obtained by aggregating the first score and the second score 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...
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...
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 inputting the plurality of passages to 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 inputting the plurality of passages to 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.
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 1-3, 6-11 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”) further in view of Boxwell et al. (US 2020/0218988 A1, hereinafter “Boxwell”).
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”).
… 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).
… 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, (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 perform first retrieval processing of retrieving an initial passage; perform second retrieval processing of retrieving an additional passage; association information including a strength of association between the passages included in the passage set, and the association information being calculated in advance of performing the second retrieval processing; and perform third retrieval processing of performing retrieval processing based on a prompt including the initial passage and the additional passage.
However, AlShikh discloses knowledge graph and teaches
perform first retrieval processing of first textual passage (see AlShikh, [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve”; [0081] “a first textual passage and summaries associated with the first textual passage may be encoded into a first intermediate output, text based on the natural language textual sequence”).
perform second retrieval processing of second textual passage… (see AlShikh, [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve”; [0081] “a second textual passage and summaries associated with the second textual passage may be encoded into a second intermediate output”) 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”) the association information being calculated… (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”) performing the second retrieval processing; and (see AlShikh, [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve”; [0081] “a second textual passage and summaries associated with the second textual passage may be encoded into a second intermediate output”).
perform third retrieval processing of third textual passage performing retrieval processing (see AlShikh, [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve”; [0081] “a third textual passage (retrieved based on a comparison between text based on the natural language textual sequence and relationships in the knowledge graph)”; [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) based on a prompt including an input (see AlShikh [0030] “provided in the input to the model (which may also be referred to as a prompt)”).
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”).
The proposed combination of Khanwalkar and AlShikh does not explicitly teach the association information being calculated in advance of performing the second retrieval processing.
However, Boxwell discloses knowledge graph and teaches
data being processed with a knowledge graph in advance of retrieving passages (see Boxwell, [0055]-[0058] “a corpus of unstructured data 140 is being processed in association with a knowledge graph. The knowledge graph is derived from one or more data sources containing a plurality of source passages and includes a set of nodes, and a set of edges. During training phase, unstructured data is extracted from corpus 140, and a knowledge graph is built… the knowledge graph may associate and store, for each edge of the generated graph, a plurality of source passages from which the edge was generated… GLM engine 129 analyzes each edge of the generated KG… GLM 129 chooses a labeled edge between two nodes (syntactic entities), retrieves source passages associated with the chosen edge” – the knowledge graph is generated in advance prior to retrieving source passages).
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 creating structures in advance, 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”).
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 inputting the plurality of passages to a language model,… (see Khanwalkar, [col 8 lines 34-35] “a generative model (such as a Large Language Model (LLM)) is utilized”; [col 29 lines 39-51] “the prompt includes instructions to the generative model to use the included passages and its internal knowledge… the input to the generative model (the question to be answered) is augmented based on relevant passages retrieved from a passage index”) and is referred to in retrieval processing; and… retrieving of passages based on 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).
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, association information prior to the retrieving of passages based on the association information; and 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… 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”; [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”).
The proposed combination of Khanwalkar and AlShikh does not explicitly teach association information prior to the retrieving of passages.
However, Boxwell discloses knowledge graph and teaches
data being processed with a knowledge graph prior to retrieving passages (see Boxwell, [0055]-[0058] “a corpus of unstructured data 140 is being processed in association with a knowledge graph. The knowledge graph is derived from one or more data sources containing a plurality of source passages and includes a set of nodes, and a set of edges. During training phase, unstructured data is extracted from corpus 140, and a knowledge graph is built… the knowledge graph may associate and store, for each edge of the generated graph, a plurality of source passages from which the edge was generated… GLM engine 129 analyzes each edge of the generated KG… GLM 129 chooses a labeled edge between two nodes (syntactic entities), retrieves source passages associated with the chosen edge” – the knowledge graph is generated in advance prior to retrieving source passages).
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 creating structures prior, 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”).
Regarding claim 9, Khanwalkar teaches
An information processing method comprising: by at least one processor configured to execute the instructions, the instructions being stored in at least one memory, (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”).
acquiring 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”).
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).
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,… (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) retrieving the additional 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).
performing retrieval processing based on 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 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; the association information being calculated in advance of retrieving the additional passage; and a prompt including 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”) the association information being calculated… (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”).
a prompt including an input (see AlShikh [0030] “provided in the input to the model (which may also be referred to as a prompt)”).
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”).
The proposed combination of Khanwalkar and AlShikh does not explicitly teach the association information being calculated in advance of retrieving the additional passage.
However, Boxwell discloses knowledge graph and teaches
data being processed with a knowledge graph in advance of retrieving passages (see Boxwell, [0055]-[0058] “a corpus of unstructured data 140 is being processed in association with a knowledge graph. The knowledge graph is derived from one or more data sources containing a plurality of source passages and includes a set of nodes, and a set of edges. During training phase, unstructured data is extracted from corpus 140, and a knowledge graph is built… the knowledge graph may associate and store, for each edge of the generated graph, a plurality of source passages from which the edge was generated… GLM engine 129 analyzes each edge of the generated KG… GLM 129 chooses a labeled edge between two nodes (syntactic entities), retrieves source passages associated with the chosen edge” – the knowledge graph is generated in advance prior to retrieving source passages).
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 creating structures in advance, 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”).
Regarding claim 2, the proposed combination of Khanwalkar, AlShikh and Boxwell teaches
wherein, in the second retrieval processing, of second textual passage (see AlShikh, [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve”; [0081] “a second textual passage and summaries associated with the second textual passage may be encoded into a second intermediate output”) the 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) 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) 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 3, the proposed combination of Khanwalkar, AlShikh and Boxwell teaches
wherein, in the second retrieval processing, of second textual passage (see AlShikh, [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve”; [0081] “a second textual passage and summaries associated with the second textual passage may be encoded into a second intermediate output”) 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) 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) a first score (see Boxwell, [0039] “The scores obtained from the various reasoning algorithms… Each resulting score is then weighted against a statistical model”) 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”) 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”) 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 motivation for the proposed combination is maintained.
Claim 11 incorporates substantively all the limitations of claim 3 in a method form and is rejected under the same rationale.
Regarding claim 6, the proposed combination of Khanwalkar, AlShikh and Boxwell 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, AlShikh and Boxwell 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 inputting the plurality of passages to a language model (see Khanwalkar, [col 8 lines 34-35] “a generative model (such as a Large Language Model (LLM)) is utilized”; [col 29 lines 39-51] “the prompt includes instructions to the generative model to use the included passages and its internal knowledge… the input to the generative model (the question to be answered) is augmented based on relevant passages retrieved from a passage index”). 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.
Claims 4-5 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Khanwalkar, AIShikh and Boxwell 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, of second textual passage (see AlShikh, [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve”; [0081] “a second textual passage and summaries associated with the second textual passage may be encoded into a second intermediate output”) 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) retrieve a predetermined number of additional passages (see Khanwalkar, [col 25 lines 41-45] “the sorted candidate passages (that meet or exceed the semantic similarity threshold, and are sorted by semantic similarity) are evaluated to identify the top N passages by semantic similarity (e.g., top three passages) that are also each extracted from a different document”) having highest scores (see Boxwell, [0042] “having a highest-ranking score or confidence measure”) from the passage set and… (see Khanwalkar, [col 26 lines 4-9] “the selected passages for citation also be extracted from different documents facilitates diversification of citation document sources… a subset of the candidate passages was selected solely based on semantic similarity”) 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 the score is obtained by aggregating the first score and the second score.
However, Beller discloses evaluation score for each set of knowledge canvassing and teaches
the score is 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.
Regarding claim 5, the proposed combination of Khanwalkar, AIShikh and Boxwell teaches
wherein, in the second retrieval processing, of second textual passage (see AlShikh, [0075] “Knowledge graph retrieval 346, for example, may determine what textual passages to retrieve”; [0081] “a second textual passage and summaries associated with the second textual passage may be encoded into a second intermediate output”) 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) 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, AIShikh and Boxwell 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, 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 13 incorporates substantively all the limitations of claim 5 in a method form and is rejected under the same rationale.
Citation Of Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent No. US 9,679,254 B1 (Mawji et al.) teaches retrieving a system or peer trust score of a first entity, receiving, from a user device of a second entity, data indicating an attribute associated with the first entity, receiving a request for the trust score for the first entity from a user device of a third entity, receiving an indication of an activity to be performed in the future by the first entity and the third entity.
US Publication No. US 2020/0380037 A1 (Zhao et al.) teaches plurality of retrieval results such as first retrieval result, second retrieval result and third retrieval result.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/VAISHALI SHAH/Primary Examiner, Art Unit 2156
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