Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application
Claims 1-10 are currently pending in this case and have been examined and addressed below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/07/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 – 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claims 1-8 and 10 are drawn to a machine. Claim 9 is drawn to a process. As such, claims 1-10 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Independent Claim 1: An information processing apparatus comprising at least one processor, the at least one processor carrying out:
a query generating process for generating a query that instructs generation of at least one question sentence which asks about a matter serving as a basis for determining a degree of nursing care required;
a generation control process for inputting, into a generative model that has been trained by machine learning to generate a question sentence in accordance with an input query, the query which has been generated in the query generating process, and causing the generative model to generate the at least one question sentence;
a presentation process for presenting, to a target person, the at least one question sentence that has been generated by the generative model;
and a recording process for recording an answer of the target person to the at least one question sentence.
Independent Claim 9: A care needs assessment support method comprising:
a query generating process for generating a query that instructs generation of at least one question sentence which asks about a matter serving as a basis for determining a degree of nursing care required;
a generation control process for inputting, into a generative model that has been trained by machine learning to generate a question sentence in accordance with an input query, the query which has been generated in the query generating process, and causing the generative model to generate the at least one question sentence;
a presentation process for presenting, to a target person, the at least one question sentence that has been generated by the generative model;
and a recording process for recording an answer of the target person to the at least one question sentence, the query generating process, the generation control process, the presentation process, and the recording process each being carried out by at least one processor.
Independent Claim 10: A non-transitory computer-readable recording medium recording therein a care needs assessment support program for causing a computer to function as:
a query generating means for generating a query that instructs generation of at least one question sentence which asks about a matter serving as a basis for determining a degree of nursing care required;
a generation control means for inputting, into a generative model that has been trained by machine learning to generate a question sentence in accordance with an input query, the query which is generated by the query generating means, and causing the generative model to generate the at least one question sentence;
a presentation means for presenting, to a target person, the at least one question sentence that has been generated by the generative model;
and a recording means for recording an answer of the target person to the at least one question sentence.
(Examiner notes: The above claim terms underlined are additional elements that fall under Step 2A - Prong Two analysis section detailed below)
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people). Therefore, generating a query, generate a question sentence, present the question sentence, and record an answer to the question sentence are directed to managing personal interactions or personal behavior.
The dependent claim 2 is directed to a question asked by a survey staff member who carries out a survey for assessment of a nursing care level or a question asked by an assessment committee member of a care needs assessment committee.
The dependent claim 3 is directed to the answer to the question sentence that has been presented causes a new question sentence to be generated related to the question sentence.
The dependent claim 4 is directed to a survey report generating process for using the recorded answer of the target person to generate a survey report showing a result of an oral survey for assessment of a nursing care level.
The dependent claim 5 is directed to generates the query that includes a document describing a survey item for determining a nursing care level and that instructs generation of a question on the basis of the document.
The dependent claim 6 is directed to decentralizes or divides, in a period up to a time point at which an oral survey is to be ended, a presentation timing at which a plurality of question sentences that the at least one question sentence comprises are presented.
The dependent claim 7 is directed to an inconsistency detecting process for detecting inconsistency in the answer of the target person to the at least one question sentence that has been presented.
The dependent claim 8 is directed to generate an answer to a question to generate an answer which is to a question from a survey staff member who carries out a survey for assessment of a nursing care level or from an assessment committee member of a care needs assessment committee and which is based on the recorded answer.
Each of these steps of the preceding dependent claims 2-8 only serve to further limit or specify the features of independent claims 1, 9, and 10 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
As such, the Examiner concludes that the preceding claims recite an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
Claims 1 and 3-8 recite the use of an information processing apparatus comprising at least one processor, the at least one processor , in this case to a query generating process for generating a query that instructs generation of at least one question sentence which asks about a matter serving as a basis for determining a degree of nursing care required, a generation control process for inputting, a presentation process for presenting the one question sentence, a recording process for recording an answer to the question sentence, a survey report generating process for using the recorded answer of the target person to generate a survey report showing a result of an oral survey for assessment of a nursing care level, generates the query that includes a document describing a survey item for determining a nursing care level and that instructs generation of a question on the basis of the document, decentralizes or divides, in a period up to a time point at which an oral survey is to be ended, a presentation timing at which a plurality of question sentences that the at least one question sentence comprises are presented, carries out an inconsistency detecting process for detecting inconsistency in the answer to the question sentence that has been presented, only recites the information processing apparatus comprising at least one processor, the at least one processor as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claims 1-3 and 9-10 recite the use of a generative model that has been trained by machine learning, in this case to generate a question sentence in accordance with an input query, to learn (i) a question asked by a survey staff member who carries out a survey for assessment of a nursing care level or (ii) a question asked by an assessment committee member of a care needs assessment committee, generate a new question sentence related to the at least one question sentence, only recites the generative model that has been trained by machine learning as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 8 recites the use of at least one processor carries out a process for causing a language model that has been trained by machine learning, in this case to generate an answer to a question to generate an answer which is to a question from a survey staff member who carries out a survey for assessment of a nursing care level or from an assessment committee member of a care needs assessment committee and which is based on the recorded answer, only recites the at least one processor carries out a process for causing a language model that has been trained by machine learning as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2)) amounting to instruction to implement the abstract idea using a general purpose computer.
Claim 9 recites the use of the query generating process, the generation control process, the presentation process, and the recording process each being carried out by at least one processor, only as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
Claim 10 recites the use of a non-transitory computer-readable recording medium recording therein a care needs assessment support program for causing a computer to function as a query, a generation control, a presentation, and a recording, in this case to generating a query that instructs generation of at least one question sentence which asks about a matter serving as a basis for determining a degree of nursing care required, inputting, presenting the one question sentence, recording an answer to the question sentence, only recites the A non-transitory computer-readable recording medium recording therein a care needs assessment support program for causing a computer to function as a query, a generation control, a presentation, and a recording as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception.
As discussed above in “Step 2A – Prong 2”, the identified additional elements, such as the information processing apparatus, processor, generative model trained by machine learning, language model trained by machine learning, the query generating process, the generation control process, the presentation process, and the recording process each being carried out by at least one processor, and non-transitory computer-readable recording medium recording therein a care needs assessment support program for causing a computer to function as a query, a generation control, a presentation, and a recording in independent claims 1, 9, and 10 and dependent claims 2-8 are equivalent to adding the words “apply it” on a generic computer. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the computer and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”). Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements are directed to generic computer component and functions being used to perform the abstract idea.
This conclusion is based on a factual determination. Applicant’s own disclosure in paragraphs [0014] and [0113-0114] acknowledges that the “information processing apparatus 1 includes a query generating section 101, a generation control section 102, a presentation section 103, and a recording section 104… and… Some or all of the functions of the information processing apparatuses 1 and 1A can be realized by hardware such as an integrated circuit (IC chip), or can be realized by software…and… In a case where some or all of the functions of the information processing apparatuses 1 and 1A are realized by software, the information processing apparatuses 1 and lA are each realized by, for example, a computer that executes instructions of a program that is software realizing the foregoing functions”. Paragraph [0116] discloses “Examples of the processor C1 encompass a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a tensor processing unit (TPU), a quantum processor, a microcontroller, and a combination thereof”. The disclosure also acknowledges in paragraphs [0016] and [0032] “natural language, a language model that has learned, by machine learning, an arrangement of components (such as words) of a sentence described in natural language and an arrangement of sentences in text may be used as the generative mode…and… the generative model 2, which is a language model, makes it possible to generate a question sentence in text format from a query in text format”. Additionally, paragraph [0118] discloses “Examples of the recording medium M encompass a tape, a disk, a card, a semiconductor memory, and a programmable logic circuit”.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-10 are not eligible subject matter under 35 USC 101.
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 1-5 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Sayapaneni (US-20230245774-A1)[hereinafter Sayapaneni], in view of Khan et al. (US-20240013928-A1)[hereinafter Khan].
As per Claim 1, Sayapaneni discloses an information processing apparatus comprising at least one processor in paragraphs [0018] and [0026] and [0064] (a computer system (synonymous to an information processing apparatus) including at least one processor), the at least one processor carrying out: a query generating process for generating a query that instructs generation of at least one question sentence in paragraphs [0045-0046] and [0048] and Figure 6 (a query generating process for a user query that instructs a question which asks the user about a symptom (synonymous to a matter serving as a basis for determining a degree of nursing care required)); a generation control process for inputting, into a generative model that has been trained by machine learning to generate a question sentence in accordance with an input query, the query which has been generated in the query generating process, and causing the generative model to generate the at least one question sentence in paragraphs [0045-0047] and [0058-0059] and [0066] and Figure 6 (a generation control process for inputting, into the converse system, wherein the converse system is a conversational artificial intelligence that uses a clinical BERT model (synonymous to a generative model) that has been trained by machine learning to generate a question in accordance to the user query, the query which was generated in the query generating process, and causes the converse system to generate a question); a presentation process for presenting, to a target person, the at least one question sentence that has been generated by the generative model in paragraphs [0061-0062] and Figure 6 (presenting, to a user, the question that has been generated by the clinical BERT model); and a recording process for recording an answer of the target person to the at least one question sentence in paragraph [0061] and Figure 6 (collecting the response of the user to the question).
Sayapaneni discloses a query generating process for generating a question and a generation control process for inputting a query into a generative model to generate a question, but Sayapaneni does not disclose the query generation and generation control processes generating a question to determine a degree of nursing care. However, Khan discloses a query generating process for generating a query that instructs generation of at least one question sentence which asks about a matter serving as a basis for determining a degree of nursing care required in paragraphs [0003] and [0062] and [0106] (a prompt generating process for generating a prompt that asks about patient information (synonymous to a matter serving basis) for determining a level of care required); a generation control process for inputting, into a generative model that has been trained by machine learning to generate a question sentence in accordance with an input query, the query which has been generated in the query generating process, and causing the generative model to generate the at least one question sentence in paragraphs [0062] and [0106] (inputting, into a large language model that has been trained by machine learning to generate patient-specific prompts, a prompt generated in the prompt generating process, and causing the large language model to generate a question).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention an information processing apparatus that has a query generating process, a generation control process, a presentation process, and a recording process, as disclosed by Sayapaneni, to be combined with query generating process and generation control process generating a question to determine a degree of nursing care required, as disclosed by Khan, for the purpose of efficiently providing better or best care by prioritizing patients who need more or most attention [0053-0055].
As per Claim 2, Sayapaneni and Khan disclose the information processing apparatus according to claim 1, Sayapaneni also discloses wherein the generative model is a language model that has been trained, by machine learning in paragraphs [0066] and [0068] and [0075] (the clinical BERT model is a language model that has been trained by machine learning).
Sayapaneni discloses a language model being trained by machine learning, but does not disclose the language model learning a question asked by a survey staff member who carries out a survey for assessment of a nursing care level or a question asked by an assessment committee member of a care needs assessment committee. However, Khan discloses wherein the generative model is a language model that has been trained, by machine learning, to learn one or both of (i) a question asked by a survey staff member who carries out a survey for assessment of a nursing care level and (ii) a question asked by an assessment committee member of a care needs assessment committee in paragraphs [0061-0062] and [0106] (the large language model trained by machine learning, to learn a question asked by a survey by a healthcare provider (synonymous to a survey staff member) for assessment of a level of care).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention an information processing apparatus that has a query generating process, a generation control process, a presentation process, and a recording process, as disclosed by Sayapaneni, to be combined with the generative model being a language model that has been trained by machine learning to learn a question asked by a survey staff member who carries out a survey for assessment of a nursing care level or a question asked by an assessment committee member of a care needs assessment committee, as disclosed by Khan, for the purpose of efficiently providing better or best care by prioritizing patients who need more or most attention [0053-0055].
As per Claim 3, Sayapaneni and Khan disclose the information processing apparatus according to claim 1, Sayapaneni also discloses wherein the at least one processor carries out a process for inputting, into the generative model, the answer of the target person to the at least one question sentence that has been presented, and causing the generative model to generate a new question sentence related to the at least one question sentence in paragraphs [0058-0059] and [0066] and [0093] and [0099] (the processor carries out an iterative process for inputting, into the clinical BERT model, the response of the user to the question that was presented and causes the clinical BERT model to generate a new question related to question).
As per Claim 4, Sayapaneni and Khan disclose the information processing apparatus according to claim 1.
Sayapaneni does not disclose the following limitations. However, Khan discloses wherein the at least one processor carries out a survey report generating process for using the recorded answer of the target person to generate a survey report showing a result of an oral survey for assessment of a nursing care level in paragraphs [0096] and [0177] [0198-0200] and Figure 14B (a GUI generated prompt (synonymous to a survey report) using the recorded answer of the patient to generate the GUI generated prompt showing a patient prioritization score (synonymous to a results of a survey) for assessment of a level of care).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention an information processing apparatus that has a query generating process, a generation control process, a presentation process, and a recording process, as disclosed by Sayapaneni, to be combined with a survey report generating process, as disclosed by Khan, for the purpose of efficiently providing better or best care by prioritizing patients who need more or most attention [0053-0055].
As per Claim 5, Sayapaneni and Khan disclose the information processing apparatus according to claim 1.
Sayapaneni does not disclose the following limitations. However, Khan discloses wherein, in the query generating process, the at least one processor generates the query that includes a document describing a survey item for determining a nursing care level and that instructs generation of a question on the basis of the document in paragraphs [0061-0062] and [0068-0069] and [0081] and [0089] and [0098] and [0100] and [0106] (a file including healthcare data (synonymous to a document describing a survey item) for determining the level of care needed and generates a question based on the file).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention an information processing apparatus that has a query generating process, a generation control process, a presentation process, and a recording process, as disclosed by Sayapaneni, to be combined with a document describing a survey item for determining a nursing care level and the instructs generation of a question on the basis of the document, as disclosed by Khan, for the purpose of efficiently providing better or best care by prioritizing patients who need more or most attention [0053-0055].
As per Claim 9, Sayapaneni discloses a care needs assessment support method in paragraphs [0093] (an iterative process of receiving responses and asking questions to arrive at a most probable disease diagnosis or a recommendation to seek care assistance) comprising: a query generating process for generating a query that instructs generation of at least one question sentence in paragraphs [0045-0046] and [0048] and Figure 6 (a query generating process for a user query that instructs a question which asks the user about a symptom (synonymous to a matter serving as a basis for determining a degree of nursing care required)); a generation control process for inputting, into a generative model that has been trained by machine learning to generate a question sentence in accordance with an input query, the query which has been generated in the query generating process, and causing the generative model to generate the at least one question sentence in paragraphs [0045-0047] and [0058-0059] and [0066] and Figure 6 (a generation control process for inputting, into the converse system, wherein the converse system is a conversational artificial intelligence that uses a clinical BERT model (synonymous to a generative model) that has been trained by machine learning to generate a question in accordance to the user query, the query which was generated in the query generating process, and causes the converse system to generate a question); a presentation process for presenting, to a target person, the at least one question sentence that has been generated by the generative model in paragraphs [0061-0062] and Figure 6 (presenting, to a user, the question that has been generated by the clinical BERT model); and a recording process for recording an answer of the target person to the at least one question sentence in paragraphs [0061] and Figure 6 (collecting the response of the user to the question), the query generating process, the generation control process, the presentation process, and the recording process each being carried out by at least one processor in paragraphs [0018] and [0026] and [0064] (the query generating process, the generation control process, presenting the question, and collecting the responses are carried out by at least one processor).
Sayapaneni discloses a query generating process for generating a question and a generation control process for inputting a query into a generative model to generate a question, but Sayapaneni does not disclose the query generation and generation control processes generating a question to determine a degree of nursing care. However, Khan discloses a query generating process for generating a query that instructs generation of at least one question sentence which asks about a matter serving as a basis for determining a degree of nursing care required in paragraphs [0003] and [0062] and [0106] (a prompt generating process for generating a prompt that asks about patient information (synonymous to a matter serving basis) for determining a level of care required); a generation control process for inputting, into a generative model that has been trained by machine learning to generate a question sentence in accordance with an input query, the query which has been generated in the query generating process, and causing the generative model to generate the at least one question sentence in paragraphs [0062] and [0106] (inputting, into a large language model that has been trained by machine learning to generate patient-specific prompts, a prompt generated in the prompt generating process, and causing the large language model to generate a question).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a care needs assessment method, as disclosed by Sayapaneni, to be combined with query generating process and generation control process generating a question to determine a degree of nursing care required, as disclosed by Khan, for the purpose of efficiently providing better or best care by prioritizing patients who need more or most attention [0053-0055].
As per Claim 10, Sayapaneni discloses a non-transitory computer-readable recording medium recording therein a care needs assessment support program for causing a computer to function as in paragraphs [0018-0019] and [0064] and [0093] (one or more non-transitory computer-readable media storing instructions for an iterative process of receiving responses and asking questions to arrive at a most probable disease diagnosis or a recommendation to seek care assistance, causing a computer to function as): a query generating means for generating a query that instructs generation of at least one question sentence which asks about a matter serving as a basis for determining a degree of nursing care required in paragraphs [0045-0046] and [0048] and Figure 6 (a query generating process for a user query that instructs a question which asks the user about a symptom (synonymous to a matter serving as a basis for determining a degree of nursing care required)); a generation control means for inputting, into a generative model that has been trained by machine learning to generate a question sentence in accordance with an input query, the query which is generated by the query generating means, and causing the generative model to generate the at least one question sentence in paragraphs [0045-0047] and [0058-0059] and [0066] and Figure 6 (a generation control process for inputting, into the converse system, wherein the converse system is a conversational artificial intelligence that uses a clinical BERT model (synonymous to a generative model) that has been trained by machine learning to generate a question in accordance to the user query, the query which was generated in the query generating process, and causes the converse system to generate a question); a presentation means for presenting, to a target person, the at least one question sentence that has been generated by the generative model in paragraphs [0061-0062] and Figure 6 (presenting, to a user, the question that has been generated by the clinical BERT model); and a recording means for recording an answer of the target person to the at least one question sentence in paragraph [0061] and Figure 6 (collecting the response of the user to the question).
Sayapaneni discloses a query generating process for generating a question and a generation control process for inputting a query into a generative model to generate a question, but Sayapaneni does not disclose the query generation and generation control processes generating a question to determine a degree of nursing care. However, Khan discloses a query generating means for generating a query that instructs generation of at least one question sentence in paragraphs [0003] and [0062] and [0106] (a prompt generating process for generating a prompt that asks about patient information (synonymous to a matter serving basis) for determining a level of care required); a generation control means for inputting, into a generative model that has been trained by machine learning to generate a question sentence in accordance with an input query, the query which is generated by the query generating means, and causing the generative model to generate the at least one question sentence in paragraphs [0062] and [0106] (inputting, into a large language model that has been trained by machine learning to generate patient-specific prompts, a prompt generated in the prompt generating process, and causing the large language model to generate a question).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention of a non-transitory computer-readable recording medium recording a care needs support program, as disclosed by Sayapaneni, to be combined with query generating process and generation control process generating a question to determine a degree of nursing care required, as disclosed by Khan, for the purpose of efficiently providing better or best care by prioritizing patients who need more or most attention [0053-0055].
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Sayapaneni (US-20230245774-A1)[hereinafter Sayapaneni], in view of Khan et al. (US-20240013928-A1)[hereinafter Khan], in view of Shriberg (US-20200118458-A1)[hereinafter Shriberg].
As per Claim 6, Sayapaneni and Khan disclose the information processing apparatus according to claim 1.
Sayapaneni and Khan do not disclose the following limitations. However, Shriberg discloses wherein the at least one processor decentralizes or divides, in a period up to a time point at which an oral survey is to be ended, a presentation timing at which a plurality of question sentences that the at least one question sentence comprises are presented in paragraphs [0258] and [0273-0276] and [0361] and Figures 16-17 (splits a period up to a time point at which an oral survey is to be stopped, queuing and dequeuing at which a plurality of questions including the question are presented (Examiner notes that queuing and dequeuing questions to be presented indicates a presentation timing at which the questions are presented)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention an information processing apparatus that has a query generating process, a generation control process, a presentation process, and a recording process, as disclosed by Sayapaneni and Khan, to be combined with dividing a period up to a time point at which the oral survey is to be ended, a presentation timing at which a plurality of question sentences are presented, as disclosed by Shriberg, for the purpose of improving workflow by generating engaging health monitoring surveys and an assessment tool that assesses dishonesty in patient answers [0004].
As per Claim 7, Sayapaneni and Khan disclose the information processing apparatus according to claim 1.
Sayapaneni and Khan do not disclose the following limitations. However, Shriberg discloses wherein the at least one processor carries out an inconsistency detecting process for detecting inconsistency in the answer of the target person to the at least one question sentence that has been presented in paragraphs [0042] and [0299] and [0307] and [0393] and [0429-0430] (evaluates the truthfulness of responses of the patient to the question that was presented (Examiner notes that evaluating the truthfulness of the response indicates detecting inconsistencies in the answers to the questions)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention an information processing apparatus that has a query generating process, a generation control process, a presentation process, and a recording process, as disclosed by Sayapaneni and Khan, to be combined with an inconsistency detecting process, as disclosed by Shriberg, for the purpose of improving workflow by generating engaging health monitoring surveys and an assessment tool that assesses dishonesty in patient answers [0004].
Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Sayapaneni (US-20230245774-A1)[hereinafter Sayapaneni], in view of Khan et al. (US-20240013928-A1)[hereinafter Khan], in view of Duboue (US-20110125734-A1)[hereinafter Duboue].
As per Claim 8, Sayapaneni and Khan disclose the information processing apparatus according to claim 1.
The combination of Sayapaneni and Khan discloses generating questions from a survey for assessment of a level of care, but the combination does not disclose the generation of an answer to a question based on the previously recorded answer. However, Duboue discloses wherein the at least one processor carries out a process for causing a language model that has been trained by machine learning to generate an answer to a question to generate an answer which is to a question from a survey staff member who carries out a survey for assessment of a nursing care level or from an assessment committee member of a care needs assessment committee and which is based on the recorded answer in paragraphs [0027] and [0029-0032] and [0035] and [0037] and [0097] (a natural language understanding module (synonymous to a language model that has been trained by machine learning) generating question answer pairs based on textual data (Examiner notes that generating question answer pairs indicates generating an answer to a question based on textual data (synonymous to a survey for assessment of a nursing care level) based on the previous answer)).
It would have been obvious to one of ordinary still in the art to include in the information processing apparatus that has a query generating process, a generation control process, a presentation process, and a recording process of the combination of Sayapaneni and Khan with the process for causing a language model that has been trained by machine learning to generate an answer to a question as taught by Duboue since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictably an information processing apparatus that has a query generating process, a generation control process, a presentation process, a recording process, and a process for question-answer (QA) generation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wei Yuan et al., “Improving Neural Question Generation using Deep Linguistic Representation”, (2021) teaches on question generation using natural language processing
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/K.N.W./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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