Information processing system and information processing method

Abstract

To provide an information processing system and an information processing method excellent in convenience, usefulness or reliability for predicting an examination result and preparing an opinion draft.SOLUTION: In the system composed of three components, a component 120 receives a range PC of a patent request, a notice NRFR of reasons for rejection, a draft DArg1 and a predicted Fcst. The component 130 receives the instruction statement Pt1, transmits the prediction to the component 110, and performs processing using the large language model LLM. The component 110 receives and shares the range of the patent request, the notification of reasons for rejection, and the draft of the opinion, receives the prediction, and transmits it to the component 120. Component 120 comprises two subcomponents. The first sub-component performs processing using a database and a search engine. The database includes audit records, and the search engine extracts a list of audit records in response to the query.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] One embodiment of the present invention relates to an information processing system, an information processing method, or a semiconductor device.

[0002] Note that one embodiment of the present invention is not limited to the above-mentioned technical field. The technical field of one embodiment of the invention disclosed in this specification relates to an object, a method, or a manufacturing method. Alternatively, one embodiment of the present invention relates to a process, a machine, a manufacture, or a composition of matter. Therefore, more specifically, examples of the technical field of one embodiment of the present invention disclosed in this specification include information processing devices, semiconductor devices, memory devices, driving methods thereof, and manufacturing methods thereof. [Background technology]

[0003] In recent years, there has been active development of language models using neural networks, with large-scale language models (LLMs) attracting particular attention. Large-scale language models are natural language processing models trained using large amounts of data. Large-scale language models can realize, for example, dialogue models that respond to user instructions. Non-Patent Document 1 discloses GPT-4 (Generative Pre-trained Transformer 4) (registered trademark) as a large-scale language model, and ChatGPT as a dialogue model.

[0004] The use of large-scale language models has significantly increased the capabilities of natural language processing models. However, as language models become larger, it is difficult to incorporate and operate language models in-house due to the equipment and cost involved. Therefore, one way to use language models is to use external services that provide language models. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Summary of ChatGPT / GPT-4 Research and Perspective Towards the Future of Large Language Models, Yiheng Liu et al. (Submitted on 4 Apr 2023, [online], Internet<URL:https: / / arxiv.org / abs / 2304.01852> Summary of the Invention [Problem to be solved by the invention]

[0006] An object of one embodiment of the present invention is to provide a novel information processing system with excellent convenience, usefulness, or reliability, or to provide a novel information processing method with excellent convenience, usefulness, or reliability, or to provide a novel information processing system, a novel information processing method, or a novel semiconductor device.

[0007] Note that the description of these problems does not preclude the existence of other problems. Note that one embodiment of the present invention does not necessarily solve all of these problems. Note that problems other than these will become apparent from the description of the specification, drawings, claims, etc., and it is possible to extract other problems from the description of the specification, drawings, claims, etc. [Means for solving the problem]

[0008] (1) One aspect of the present invention is an information processing system having a first component, a second component, and a third component.

[0009] The first component has a function of accepting claims, a notice of rejection relating to the claims, and a first draft argument relating to the notice of rejection and sending them to the third component, and a function of accepting and providing predictions.

[0010] The second component has a function of accepting the first directive and sending a prediction to the third component, and a function of performing processing using a large-scale language model, the large-scale language model having a function of generating a prediction according to the first directive.

[0011] The third component has a function of receiving claims, a notice of reasons for refusal, and a first draft argument and sharing them within the third component, and a function of receiving forecasts and sending them to the first component. The third component also has a first subcomponent and a second subcomponent.

[0012] The first subcomponent provides functionality for processing using a database and a search engine.

[0013] The database includes one or more examination records, and the examination record includes a field for storing an opinion record, a field for storing a notice record, a field for storing first information identifying the examiner in charge, and a field for storing second information identifying the technical field. The notice record includes a decision on the opinion record.

[0014] The search engine has a function of extracting a first examination record list from the database in response to a first query, and the first query requires that the technical field is the same as the scope of the patent claim and that the examiner is the same as the examiner responsible for the office action.

[0015] The second subcomponent has a function of creating a first instruction statement and sending it to the second component.

[0016] The first instruction includes a first instruction, a first review record list, and a first draft opinion, and the first instruction includes a procedure for generating a prediction by referring to the first review record list. The prediction includes a judgment on the first draft opinion.

[0017] This allows a user to predict the decision on the first draft argument of the examiner who issued the notice of refusal. The decision can also be predicted by referring to the first examination record list of the examiner who issued the notice of refusal. The decision can also be predicted from the opinion record and notice record stored in the first examination record list. Furthermore, for example, a user of the information processing system can reconsider the first draft argument by referring to the prediction. Furthermore, for example, a user of the information processing system can review the response policy by referring to the prediction. As a result, a novel information processing system with excellent convenience, usefulness, and reliability can be provided.

[0018] (2) Another aspect of the present invention is the above information processing system, wherein the first component has a function of accepting a response policy and sending it to the third component, and a function of accepting a second draft opinion and providing it.

[0019] The second component has a function of receiving the second instruction sentence and transmitting the second draft opinion to the third component, and a function of performing processing using a large-scale language model, and the large-scale language model has a function of generating the second draft opinion in accordance with the second instruction sentence.

[0020] The third component has a function of receiving the response policy and sharing it within the third component, and a function of receiving the second draft opinion and sending it to the first component.

[0021] The search engine has a function to extract a second list of examination records from the database in response to a second query, which requires that the technical field is the same as the scope of the claims, the examiner is the same as the examiner responsible for the office action, and the opinion record is similar to the response policy.

[0022] The second subcomponent has a function of creating a second instruction statement and sending it to the second component.

[0023] The second instruction sentence includes second instructions and a second review record list, and the second instructions include a procedure for generating a second draft opinion by referring to the second review record list.

[0024] This makes it possible to generate a second draft argument based on the response policy. Furthermore, for example, it is possible to generate a second draft argument that overturns the decision on the first draft argument. Furthermore, for example, it is possible to generate a second draft argument that is more likely to be accepted by the examiner who issued the notice of reasons for refusal. As a result, it is possible to provide a novel information processing system that is highly convenient, useful, and reliable.

[0025] (3) Another aspect of the present invention is the above information processing system, wherein the first component has a function of accepting a specification relating to the claims and a reference relating to a notice of rejection and sending them to the third component, and a function of accepting and providing a response policy list.

[0026] The second component has a function of accepting a third directive and sending an analysis result to the third component, a function of accepting a fourth directive and sending a correspondence table to the third component, and a function of accepting a fifth directive and sending a response policy list to the third component. It also has a function of performing processing using a large-scale language model, and the large-scale language model has a function of generating an analysis result according to the third directive, a function of generating a correspondence table according to the fourth directive, and a function of generating a response policy list according to the fifth directive.

[0027] The third component has a function of accepting specifications and references and sharing them within the third component, and a function of accepting a response policy list and sending it to the first component.

[0028] The second subcomponent has a function of creating a third directive, a fourth directive, and a fifth directive and sending them to the second component.

[0029] The third instruction sentence includes a third instruction, a description, claims, a notice of rejection, and a reference, and the third instruction includes a procedure for analyzing the notice of rejection using the description, claims, and reference to generate an analysis result.

[0030] The fourth instruction sentence includes a fourth instruction and the analysis result. The fourth instruction includes a procedure for generating a correspondence table from the analysis result. The correspondence table includes claims and reasons for refusal related to the claims. The claims are included in the scope of the claims, and the reasons for refusal are included in the notice of reasons for refusal.

[0031] The fifth instruction sentence includes a fifth instruction, an analysis result, and a correspondence table. The fifth instruction includes a procedure for generating a response policy list, and each rejection reason in the response policy list lists multiple response policies. Each response policy also indicates its advantages and disadvantages.

[0032] This allows the claims, description, and cited references to be used to analyze a notice of rejection and generate analysis results. Furthermore, a correspondence table that matches claims with rejection reasons can be generated from the analysis results. Furthermore, a response policy list listing multiple response policies can be generated and provided from the analysis results and the correspondence table. Furthermore, advantages and disadvantages of each response policy can be indicated. Furthermore, for example, a user of the information processing system can select a response policy by referring to the response policy list. As a result, a novel information processing system with excellent convenience, usefulness, and reliability can be provided.

[0033] (4) One aspect of the present invention is an information processing method having a first phase, wherein the first phase includes first to eighth steps.

[0034] In the first step of the first phase, the first component accepts the claims, the office action relating to the claims, and the first draft argument relating to the office action, and sends them to the second component.

[0035] In the second step of the first phase, the second component receives the claims, the notice of reasons for refusal, and the first draft argument and shares them within the second component, which includes a first subcomponent and a second subcomponent.

[0036] In the third step of the first phase, the first subcomponent extracts a first review record list from the database in response to the first query.

[0037] The first query requires that the technical field be the same as the claims and that the examiner be the same as the office action.

[0038] The database also includes one or more examination records, each of which includes a field for storing an opinion record, a field for storing a notice record, a field for storing first information identifying the examiner in charge, and a field for storing second information identifying the technical field. The notice record includes a decision on the opinion record.

[0039] In the fourth step of the first phase, the second subcomponent creates and sends a first instruction statement to the third component.

[0040] The first instruction includes a first instruction, a first review record list, and a first draft opinion, and the first instruction includes a procedure for generating a prediction using the first review record list.

[0041] In the fifth step of the first phase, a third component accepts the first directive and generates a prediction using a large-scale language model.

[0042] In the sixth step of the first phase, the third component sends the prediction to the second component.

[0043] In the seventh step of the first phase, the second component accepts the prediction and sends it to the first component.

[0044] In the eighth step of the first phase, the first component provides a forecast.

[0045] This makes it possible to predict the decision on the first draft argument of the examiner who issued the notice of refusal. Furthermore, the decision can be predicted by referring to the first examination record list of the examiner who issued the notice of refusal. Furthermore, the decision can be predicted from the opinion record and notice record stored in the first examination record list. Furthermore, for example, a user of the information processing system can reconsider the first draft argument by referring to the prediction. Furthermore, for example, a user of the information processing system can review the response policy by referring to the prediction. As a result, a novel information processing method that is highly convenient, useful, and reliable can be provided.

[0046] (5) Another aspect of the present invention is the information processing method described above, which has a second phase. Note that the first phase follows the second phase, and the second phase includes the first to eighth steps.

[0047] In the first step of the second phase, the first component accepts and transmits the response policy, claims, and office action to the second component.

[0048] In the second step of the second phase, the second component accepts the response policy, claims, and office action and shares them within the second component.

[0049] In a third step of the second phase, the first subcomponent extracts a second review record list from the database in response to the second query.

[0050] The second query requires that the technical field be the same as the claims, the examiner be the same as the office action, and the record be similar to the response policy.

[0051] In the fourth step of the second phase, the second subcomponent creates and sends a second directive to the third component.

[0052] The second instruction sentence includes second instructions and a second examination record list, and the second instructions include a procedure for generating a second draft opinion by referring to the second examination record list.

[0053] In the fifth step of the second phase, the third component accepts the second directive and generates a second draft opinion using the large-scale language model.

[0054] In the sixth step of the second phase, the third component transmits the second draft opinion to the second component.

[0055] In the seventh step of the second phase, the second component accepts and transmits the second draft opinion to the first component.

[0056] In the eighth step of the second phase, the first component provides a second draft opinion.

[0057] This makes it possible to generate a second draft argument based on a response policy. Furthermore, for example, it is possible to generate a second draft argument that overturns the decision on the first draft argument. Furthermore, for example, it is possible to generate a second draft argument that is more likely to be accepted by the examiner who issued the notice of reasons for refusal. As a result, it is possible to provide a novel information processing method that is highly convenient, useful, and reliable.

[0058] (6) Another aspect of the present invention is the information processing method described above, further comprising a third phase, wherein the second phase follows the third phase, and the third phase includes the first to sixteenth steps.

[0059] In the first step of the third phase, the first component accepts claims, a description relating to the claims, an office action relating to the claims, and citations relating to the office action, and sends them to the second component.

[0060] In the second step of the third phase, the second component accepts the claims, specification, office actions, and references and shares them within the second component.

[0061] In the third step of the third phase, the second subcomponent creates and sends a third directive to the third component.

[0062] The third instruction sentence includes a third instruction, a description, claims, a notice of rejection, and a reference, and the third instruction includes a procedure for analyzing the notice of rejection using the description, claims, and reference to generate an analysis result.

[0063] In the fourth step of the third phase, the third component accepts the third directive and generates an analysis result using the large-scale language model.

[0064] In the fifth step of the third phase, the third component sends the analysis results to the second component.

[0065] In the sixth step of the third phase, the second component accepts the analysis results and shares them within the second component.

[0066] In the seventh step of the third phase, the second subcomponent creates and sends a fourth directive to the third component.

[0067] The fourth instruction includes a fourth instruction and an analysis result, and the fourth instruction includes a procedure for generating a correspondence table from the analysis result. The correspondence table includes claims and reasons for refusal related to the claims. The claims are included in the scope of the claims, and the reasons for refusal are included in the notice of reasons for refusal.

[0068] In the eighth step of the third phase, the third component accepts the fourth directive and generates a correspondence table using a large-scale language model.

[0069] In the ninth step of the third phase, the third component sends the correspondence table to the second component.

[0070] In the tenth step of the third phase, the second component accepts the correspondence table and shares it within the second component.

[0071] In the eleventh step of the third phase, the second subcomponent creates and sends a fifth directive to the third component.

[0072] The fifth instruction sentence includes a fifth instruction, an analysis result, and a correspondence table. The fifth instruction includes a procedure for generating a response policy list. Note that each rejection reason in the response policy list lists multiple response policies. In addition, each response policy is indicated with its advantages and disadvantages.

[0073] In the twelfth step of the third phase, the third component accepts the fifth directive and generates a list of response policies using a large-scale language model.

[0074] In the thirteenth step of the third phase, the third component sends the response policy list to the second component.

[0075] In the fourteenth step of the first phase, the second component accepts and sends the response policy list to the first component.

[0076] In the fifteenth step of the third phase, the first component accepts and provides a response policy list.

[0077] In the sixteenth step of the third phase, the first component waits for input of a response policy.

[0078] This allows a notice of rejection to be analyzed using the claims, description, and cited references, and analysis results to be generated. Furthermore, a correspondence table that matches claims with reasons for rejection can be generated from the analysis results. Furthermore, a response policy list listing multiple response policies can be generated and provided from the analysis results and the correspondence table. Furthermore, advantages and disadvantages of each response policy can be indicated. Furthermore, for example, a user of the information processing system can select a response policy by referring to the response policy list. As a result, a novel information processing method that is highly convenient, useful, and reliable can be provided. [Effects of the Invention]

[0079] One embodiment of the present invention can provide a novel information processing system with excellent convenience, usefulness, or reliability, or a novel information processing method with excellent convenience, usefulness, or reliability, or a novel information processing system, a novel information processing method, or a novel semiconductor device.

[0080] Note that the description of these effects does not preclude the existence of other effects. Note that one embodiment of the present invention does not necessarily have all of these effects. Note that effects other than these will become apparent from the description in the specification, drawings, claims, etc., and it is possible to extract other effects from the description in the specification, drawings, claims, etc. [Brief explanation of the drawings]

[0081] [Figure 1] FIG. 1 is a diagram illustrating a configuration of an information processing system according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating a configuration of components used in the information processing system according to the embodiment. [Figure 3] 3A to 3C are diagrams illustrating the configuration of data used in the information processing system according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating the configuration of a directive used in the information processing system according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating a configuration of an information processing system according to an embodiment. [Figure 6] FIG. 6 is a diagram illustrating a configuration of components used in the information processing system according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating the configuration of a directive used in the information processing system according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating a configuration of an information processing system according to an embodiment. [Figure 9] FIG. 9 is a diagram illustrating a configuration of components used in the information processing system according to the embodiment. [Figure 10] FIG. 10 is a diagram illustrating the structure of a directive used in the information processing system according to the embodiment. [Figure 11] FIG. 11(A) is a diagram explaining the structure of an instruction statement used in an information processing system according to an embodiment, and FIG. 11(B) is a diagram explaining the structure of data used in an information processing system according to an embodiment. [Figure 12] FIG. 12(A) is a diagram explaining the structure of an instruction statement used in the information processing system according to the embodiment, and FIG. 12(B) is a diagram explaining the structure of data used in the information processing system according to the embodiment. [Figure 13]FIG. 13 is a diagram illustrating the configuration of an information processing device used in the information processing system according to the embodiment. [Figure 14] FIG. 14 is a diagram illustrating an information processing method according to an embodiment. [Figure 15] FIG. 15 is a diagram illustrating an information processing method according to an embodiment. [Figure 16] FIG. 16 is a diagram illustrating an information processing method according to an embodiment. [Figure 17] FIG. 17 is a diagram illustrating an information processing method according to an embodiment. [Figure 18] FIG. 18 is a diagram illustrating an information processing method according to an embodiment. [Figure 19] FIG. 19 is a diagram illustrating an information processing method according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0082] An information processing system according to one aspect of the present invention includes a first component, a second component, and a third component.

[0083] The first component has a function of accepting claims, a notice of rejection relating to the claims, and a first draft argument relating to the notice of rejection and sending them to the third component, and a function of accepting and providing predictions.

[0084] The second component has a function of accepting the first directive and sending a prediction to the third component, and a function of performing processing using a large-scale language model, the large-scale language model having a function of generating a prediction according to the first directive.

[0085] The third component has a function of receiving claims, a notice of reasons for refusal, and a first draft argument and sharing them within the third component, and a function of receiving forecasts and sending them to the first component. The third component also has a first subcomponent and a second subcomponent.

[0086] The first subcomponent provides functionality for processing using a database and a search engine.

[0087] The database includes one or more examination records. The examination record includes a field for storing an opinion record, a field for storing a notice record, a field for storing first information identifying the examiner in charge, and a field for storing second information identifying the technical field. The notice record includes a decision on the opinion record.

[0088] The search engine has a function of extracting a first examination record list from the database in response to a first query, and the first query requires that the technical field is the same as the scope of the patent claim and that the examiner is the same as the examiner responsible for the office action.

[0089] The second subcomponent has a function of creating a first instruction statement and sending it to the second component.

[0090] The first instruction includes a first instruction, a first list of the examination record, a notice of reasons for refusal, and a first draft argument, and the first instruction includes a procedure for generating a prediction by referring to the first list of the examination record. The prediction includes a decision on the first draft argument.

[0091] This allows a user to predict the decision on the first draft argument of the examiner who issued the notice of refusal. The decision can also be predicted by referring to the first examination record list of the examiner who issued the notice of refusal. The decision can also be predicted from the opinion record and notice record stored in the first examination record list. Furthermore, for example, a user of the information processing system can reconsider the first draft argument by referring to the prediction. Furthermore, for example, a user of the information processing system can review the response policy by referring to the prediction. As a result, a novel information processing system with excellent convenience, usefulness, and reliability can be provided.

[0092] The embodiments will be described in detail with reference to the drawings. However, the present invention is not limited to the following description, and it will be readily understood by those skilled in the art that various changes in form and details can be made without departing from the spirit and scope of the present invention. Therefore, the present invention should not be interpreted as being limited to the description of the embodiments shown below. In the configuration of the invention described below, the same parts or parts having similar functions will be denoted by the same reference numerals in different drawings, and repeated explanations will be omitted.

[0093] In this specification, ordinal numbers such as "first" and "second" are used to avoid confusion between components and do not limit the number of components or the order of the components (for example, the order of processes or the order of stacking). Furthermore, even if a term does not have an ordinal number in this specification, an ordinal number may be added in the claims to avoid confusion between the components. Even if a term has an ordinal number in this specification, a different ordinal number may be added in the claims. Even if a term has an ordinal number in this specification, the ordinal number may be omitted in the claims.

[0094] In the drawings accompanying this specification, components are classified by function and shown as block diagrams that are independent of each other, but in reality, it is difficult to completely separate components by function, and one component may be involved in multiple functions.

[0095] (Embodiment 1) In this embodiment, an information processing system of one embodiment of the present invention will be described with reference to FIGS.

[0096] FIG. 1 is a diagram illustrating the configuration of an information processing system according to an embodiment of the present invention.

[0097] FIG. 2 is a diagram illustrating a configuration of components used in an information processing system according to an embodiment of the present invention.

[0098] Fig. 3(A) is a diagram illustrating the configuration of a database used in an information processing system of one embodiment of the present invention, Fig. 3(B) is a diagram illustrating the configuration of an examination record list extracted from the database, and Fig. 3(C) is a diagram illustrating the configuration of an examination record list different from the examination record list shown in Fig. 3(B).

[0099] FIG. 4 is a diagram illustrating the configuration of instruction statements transmitted and received within an information processing system according to an embodiment of the present invention.

[0100] FIG. 5 is a diagram illustrating the configuration of an information processing system according to an embodiment of the present invention.

[0101] FIG. 6 is a diagram illustrating a configuration of components used in an information processing system according to an embodiment of the present invention.

[0102] FIG. 7 is a diagram illustrating the configuration of instruction statements transmitted and received within an information processing system according to an embodiment of the present invention.

[0103] FIG. 8 is a diagram illustrating the configuration of an information processing system according to an embodiment of the present invention.

[0104] FIG. 9 is a diagram illustrating a configuration of components used in an information processing system according to an embodiment of the present invention.

[0105] FIG. 10 is a diagram illustrating the configuration of instruction statements transmitted and received within an information processing system according to an aspect of the present invention.

[0106] FIG. 11A illustrates the structure of an instruction sent and received within an information processing system according to one embodiment of the present invention, and FIG. 11B illustrates the structure of a correspondence table to be generated.

[0107] Figure 12(A) is a diagram explaining the configuration of an instruction statement sent and received within an information processing system of one embodiment of the present invention, and Figure 12(B) is a diagram explaining the configuration of a response policy list to be generated.

[0108] FIG. 13 is a block diagram illustrating a configuration of an information processing device that can be used in the information processing system of one embodiment of the present invention.

[0109] <Configuration example 1 of information processing system> The information processing system described in this embodiment includes a component 110, a component 130, and a component 120 (see FIG. 1). Note that the information processing device that performs the functions of the component 110, the information processing device that performs the functions of the component 130, and the information processing device that performs the functions of the component 120 each include a calculation device and a communication device. Furthermore, the respective communication devices can be connected, for example, using a network 51 to configure an information processing system according to one embodiment of the present invention.

[0110] <Component 110 Configuration Example 1> The component 110 has a function to receive claims PC, a notice of refusal NRFR, and a draft argument DArg1, and transmit them to the component 120. The notice of refusal NRFR relates to the claims PC, and the draft argument DArg1 relates to the notice of refusal NRFR.

[0111] For example, the claims for which patent examination has been requested can be used as the claims PC. The Office Action issued for the claims can be used as the Office Action NRFR. A draft argument in response to the Office Action NRFR can be used as the draft argument DArg1.

[0112] Also, for example, a user 99 of the information processing system inputs the claims PC, the notice of reasons for refusal NRFR, and the draft opinion DArg1 into the component 110. Specifically, the user of the information processing system inputs the information into the component 110 using an input device such as a keyboard, a mouse, an eye-gaze input device, or a microphone.

[0113] The component 110 also has a function of receiving the predicted Fcst from the component 120 and providing it to, for example, a user 99 of the information processing system. Specifically, the predicted Fcst is provided to the user 99 of the information processing system using an output device such as a display device, a speaker, a printer, or a storage device.

[0114] <Component 130 Configuration Example 1> The component 130 has a function of receiving the directive Pt1, a function of transmitting the prediction Fcst to the component 120, and a function of performing processing using the large-scale language model LLM.

[0115] 《Example 1 of Large-Scale Language Model LLM》 The large-scale language model LLM has a function of generating a prediction Fcst in accordance with a directive Pt1.

[0116] For example, large scale language models such as GPT-3 (registered trademark), GPT-3.5, GPT-4 (registered trademark), LaMDA, Llama2 or Llama3 can be used for the large scale language model LLM.

[0117] <Component 120 Configuration Example 1> The component 120 has a function of accepting, for example, claims PC, a notice of reasons for refusal NRFR, and a draft opinion DArg1 from the component 110 and sharing them within the component 120 (see FIG. 2).

[0118] The component 120 also has a function of receiving the predicted Fcst and transmitting it to the component 110.

[0119] Furthermore, component 120 includes subcomponent 120A and subcomponent 120B. For ease of explanation, in this specification, a configuration including a single function or multiple functions will be referred to as a component or subcomponent.

[0120] <<Subcomponent 120A Configuration Example 1>> The subcomponent 120A has a function of performing processing using a database DB and a search engine SE.

[0121] [Database configuration example] The database DB contains one or more examination records RExm (see Figure 3(A)). Note that one row in the figure corresponds to one examination record RExm. Note that the examination record RExm can be obtained, for example, from the patent office of the application country.

[0122] The examination record RExm includes a field for storing the identification information Id, a field for storing the opinion record RArg, a field for storing the notice record RNtc, a field for storing information IDExm that identifies the examiner in charge, and a field for storing information TchF that identifies the technical field. The notice record RNtc includes a judgment on the opinion record RArg.

[0123] [Search engine SE configuration example 1] The search engine SE has a function to extract the examination record list ExmL1 from the database DB in response to the query Que1 (see Figure 3(B)). Note that the query Que1 requires that the technical field is the same as the scope of claims PC and that the examiner is the same as the examiner responsible for the Office Action NRFR.

[0124] For example, in the examination record list ExmL1, all fields that store information TchF that identifies the technical field are technical field TchF_1, and all fields that store information IDExm that identifies the examiner in charge are examiner IDExm_1.

[0125] The amendments and arguments accepted in a case with identification information Id Id_1 are recorded in the argument record RArg_1. The decision of examiner IDExm_1 on the argument recorded in the argument record RArg_1 is recorded in the notice record RNtc_1.

[0126] <<Configuration example 1 of subcomponent 120B>> The subcomponent 120B has a function of creating an instruction statement Pt1 and sending it to the component 130.

[0127] [Example of directive Pt1] The instruction Pt1 includes an instruction g1(), an examination record list ExmL1, and a draft opinion DArg1 (see Figure 4). It is also preferable to include a notice of reasons for refusal NRFR. The instruction g1() includes a procedure for generating a predicted Fcst by referring to the examination record list ExmL1.

[0128] The predicted Fcst includes a judgment on the draft opinion DArg1. Specifically, a judgment that the RFR of refusal set forth in the NRFR can be resolved by the draft opinion DArg1 can be used for the predicted Fcst. Alternatively, a judgment that the RFR of refusal set forth in the NRFR cannot be resolved by the draft opinion DArg1 can be used for the predicted Fcst.

[0129] The examination record list ExmL1 is extracted from the database DB using information TchF that identifies the technical field and information IDExm that identifies the examiner in the query Que1. By including the opinion record RArg and notice record RNtc extracted in the examination record list ExmL1 in the instruction Pt1, the large-scale language model LLM can perform in-context learning of the tendency of the examiner's judgments identified by the information IDExm, specifically, the tendency of their responses to opinions.

[0130] For example, the following paragraph of text can be used as directive Pt1.

[0131] "##Opinion:Opinion Record RArg_1 ##Notice:Notice Record RNtc_1 ##Opinion:Opinion Record RArg_2 ##Notice:Notice Record RNtc_2 Examiners tend to respond in the manner described above. Please prepare a Notice of Action that is the examiner's response to the draft opinion DArg1 below. ##Opinion: Proposal DArg1 ##Notification:”

[0132] Note that the "##Opinion:" above is a heading, and the "Opinion Record RArg_1" and "Opinion Record RArg_2" following "##Opinion:" are opinion records RArg extracted in the examination record list ExmL1. Also, the "Draft Opinion DArg1" following "##Opinion:" is a draft opinion that uses the large-scale language model LLM to infer the examiner's judgment.

[0133] Furthermore, the above "##Notice:" are all headings, and the "Notice Record RNtc_1" and "Notice Record RNtc_2" following "##Notice:" are the notice records RNtc extracted in the examination record list ExmL1. Furthermore, "Notice Record RNtc_1" following "##Notice:" is a record of the notice in which the examiner responded to "Opinion Record RArg_1," and "Notice Record RNtc_2" is a record of the notice in which the examiner responded to "Opinion Record RArg_2."

[0134] The final "##Notice:" is a heading that prompts the large-scale language model LLM to output. Also, "The examiner has the tendency to respond as described above. Please prepare a notice that is the examiner's response to the draft opinion DArg1 below." corresponds to instruction g1().

[0135] This allows the examiner who issued the Office Action NRFR to predict the decision on the draft opinion DArg1. Furthermore, the decision can be predicted by referring to the examination record list ExmL1 of the examiner who issued the Office Action NRFR. Furthermore, the decision can be predicted by referring to the opinion record RArg and notice record RNtc stored in the examination record list ExmL1. Furthermore, for example, a user of the information processing system can reconsider the draft opinion DArg1 by referring to the predicted Fcst. Furthermore, for example, a user of the information processing system can review the response policy RP by referring to the predicted Fcst. As a result, a novel information processing system with excellent convenience, usefulness, and reliability can be provided.

[0136] <Configuration example 2 of information processing system> The information processing system described in this embodiment includes a component 110, a component 130, and a component 120 (see FIG. 5). The information processing system shown in FIG. 5 generates a draft opinion DArg2 based on a response policy RP input by a user of the information processing system, for example.

[0137] <Component 110 configuration example 2> The component 110 has a function of accepting a response policy RP and transmitting it to the component 120. For example, a user 99 of the information processing system inputs the response policy RP to the component 110. Specifically, the user of the information processing system inputs the response policy RP to the component 110 using an input device such as a keyboard, a mouse, or an eye-gaze input device.

[0138] The component 110 also has a function of receiving, for example, a draft opinion DArg2 from the component 120 and providing it to the user 99 of the information processing system.

[0139] <Component 130 configuration example 2> The component 130 has a function of receiving the directive Pt2 and transmitting the draft opinion DArg2 to the component 120, and a function of performing processing using the large-scale language model LLM.

[0140] 《Large-scale language model LLM configuration example 2》 The large-scale language model LLM has the function of generating a draft opinion DArg2 in accordance with directive Pt2.

[0141] <Component 120 configuration example 2> The component 120 has a function of accepting a response policy RP from the component 110, for example, and sharing it within the component 120 (see FIG. 6).

[0142] The component 120 also has a function of receiving the draft opinion DArg2 and transmitting it to the component 110.

[0143] [Search Engine SE Configuration Example 2] The search engine SE has a function to extract the examination record list ExmL2 from the database DB in response to the query Que2 (see Figure 3(C)). Note that the query Que2 requires that the technical field is the same as the scope of claims PC, that the examiner is the same as the person in charge of the office action NRFR, and that the written opinion record RArg is similar to the response policy RP. The query Que2 can also require that the examiner did not find any reasons for refusal in the submitted written opinion.

[0144] For example, in the examination record list ExmL2, all fields that store information TchF that identifies the technical field are technical field TchF_1, and all fields that store information IDExm that identifies the examiner in charge are examiner IDExm_1.

[0145] Furthermore, the opinion record RArg_1 stored in the field storing the opinion record RArg is similar to the response policy RP. Similarly, the opinion record RArg_m stored in the field storing the opinion record RArg is also similar to the response policy RP. For example, a policy of amending the claims to resolve the notified grounds for refusal, or a policy of refuting the notified grounds for refusal to resolve the notified grounds for refusal, etc., can be adopted as the response policy RP.

[0146] The amendments and arguments accepted in a case with identification information Id Id_1 are recorded in the argument record RArg_1. The decision of examiner IDExm_1 on the argument recorded in the argument record RArg_1 is recorded in the notice record RNtc_1.

[0147] In addition, the amendments and arguments accepted in a case with identification information Id Id_m are recorded in the argument record RArg_m. The decision of examiner IDExm_1 on the argument recorded in the argument record RArg_m is recorded in the notice record RNTc_m.

[0148] <<Configuration example 2 of subcomponent 120B>> The subcomponent 120B has a function of creating an instruction statement Pt2 and sending it to the component 130.

[0149] [Example of directive Pt2] The instruction Pt2 includes an instruction g2() and an examination record list ExmL2 (see FIG. 7). The instruction g2() also includes a procedure for generating a draft opinion DArg2 with reference to the examination record list ExmL2.

[0150] For example, the following paragraph of text can be used as directive Pt2.

[0151] "##Notice:Notice Record RNtc_1 ##Opinion:Opinion Record RArg_1 ##Notice:Notice Record RNtc_2 ##Opinion:Opinion Record RArg_2 The above is the exchange that takes place when the examiner grants permission based on the written opinion submitted in response to the notice. Please use the above as a reference to prepare an opinion that will make it easier for the examiner to grant permission for the notice below. ##Notice: Notice of Rejection NRFR ##Opinion:”

[0152] Note that the above "##Notice:" is a heading, and the "Notice Record RNtc_1" and "Notice Record RNtc_2" following "##Notice:" are the notice records RNtc extracted in the examination record list ExmL2. Also, the "Notice of Rejection NRFR" following "##Notice:" is the notice targeted by the draft opinion DArg2 to be generated by the large-scale language model LLM.

[0153] Furthermore, the above "##Opinion:" is a heading, and the "Opinion Record RArg_1" and "Opinion Record RArg_2" following "##Opinion:" are opinion records RArg extracted in the examination record list ExmL2. Furthermore, "Opinion Record RArg_1" following "##Opinion:" is a record of an opinion in response to notification record RNtc_1, and "Opinion Record RArg_2" is a record of an opinion in response to notification record RNtc_2.

[0154] The final "##Opinion:" is a heading that prompts the large-scale language model LLM to output. Also, "The above is the exchange that takes place when an opinion is submitted in response to a notice and the examiner gives permission. Please use the above as a reference to create an opinion that will make it easier for the examiner to give permission in response to the notice below." corresponds to instruction g2().

[0155] This makes it possible to generate a draft opinion DArg2 based on the response policy RP. Furthermore, for example, it is possible to generate a draft opinion DArg2 that overturns the decision on the draft opinion DArg1. Furthermore, for example, it is possible to generate a draft opinion DArg2 that is more likely to be accepted by the examiner who wrote the Notice of Rejection NRFR. As a result, it is possible to provide a novel information processing system that is highly convenient, useful, and reliable.

[0156] <Configuration example 3 of information processing system> The information processing system described in this embodiment includes components 110, 130, and 120 (see FIG. 8). The information processing system shown in FIG. 8 can analyze a notice of refusal NRFR using claims PC, specification PSpc, and cited references Ref, and generate a correspondence table Tbl that matches claims CL with reasons for refusal RFR. Furthermore, a response policy list RPL listing multiple response policies RP can be created from the analysis result AR and the correspondence table Tbl, and can be provided to, for example, a user 99 of the information processing system, along with their advantages and disadvantages.

[0157] <Component 110 Configuration Example 3> The component 110 has a function of accepting the specification PSpc relating to the claims PC and the reference Ref relating to the notice of rejection NRFR, and transmitting them to the component 120.

[0158] For example, a user 99 of the information processing system inputs the claims PC, the specification PSpc, the notice of reasons for refusal NRFR, and the cited reference Ref into the component 110. Specifically, the user of the information processing system uses an input device such as a keyboard, a mouse, or an eye-gaze input device to input the information into the component 110. Note that a file in which the information is recorded or a path specifying the location where the file in which the information is recorded can be used for input to the information processing system.

[0159] The component 110 also has a function of receiving, for example, a response policy list RPL from the component 120 and providing it to the user 99 of the information processing system.

[0160] <Component 130 Configuration Example 3> Component 130 has a function of receiving directive Pt3 and transmitting the analysis result AR to component 120, a function of receiving directive Pt4 and transmitting a correspondence table Tbl to component 120, and a function of receiving directive Pt5 and transmitting a response policy list RPL to component 120. It also has a function of performing processing using a large-scale language model LLM.

[0161] 《Large-scale language model LLM configuration example 3》 The large-scale language model LLM has a function to generate an analysis result AR according to a directive Pt3, a function to generate a correspondence table Tbl according to a directive Pt4, and a function to generate a response policy list RPL according to a directive Pt5.

[0162] <Component 120 Configuration Example 3> The component 120 has a function of accepting, for example, claims PC, specification PSpc, notice of reasons for refusal NRFR, and cited references Ref from the component 110 and sharing them within the component 120 (see FIG. 9).

[0163] It also has a function of accepting a response policy list RPL and sending it to the component 110 .

[0164] <<Configuration example 3 of subcomponent 120B>> Subcomponent 120B has the function of creating directives Pt3, Pt4, and Pt5 and sending them to component 130.

[0165] [Example of directive Pt3] The instruction Pt3 includes an instruction g3(), the specification PSpc, the claims PC, the notice of refusal NRFR, and a reference Ref (see FIG. 10). The instruction g3() also includes a procedure for analyzing the notice of refusal NRFR using the specification PSpc, the claims PC, and the reference Ref, and generating an analysis result AR. The analysis result AR includes a summary of the notice of refusal NRFR.

[0166] For example, the following paragraph of text can be used as directive Pt3.

[0167] "##Claims:Claims PC ## Statement: Statement PSpc ##Notice: Notice of Rejection NRFR ##Reference: Reference Ref Using the above specification PSpc, claims PC, and cited references Ref, please perform the following analysis of the Office Action NRFR and create the analysis results AR. Please provide an overview of the Notice of Rejection (NRFR). Please summarize each rejection reason in the NRFR notice of rejection, along with the relevant claims PC and related cited references Ref. ##Analysis results:”

[0168] Note that the above "##Claims:" and "##Description:" are headings, and the "Specification PSpc" following "##Description:" is the specification for the invention described in Claim PC. Also, "##Notice:" and "##Citation:" are headings, and the "Citation Ref" following "##Citation:" is the reference described in the Office Action NRFR.

[0169] The last line, "##Analysis Result:" is a heading that prompts the large-scale language model LLM to output. Also, "Using the above-mentioned specification PSpc, claims PC, and cited references Ref, please carry out the following analysis of the Office Action NRFR and create the analysis result AR. Please provide an overview of the Office Action NRFR. Please summarize each reason for refusal in the Office Action NRFR, the relevant claims PC, and the related cited references Ref." is the part that corresponds to instruction g3().

[0170] [Example of directive Pt4 configuration] The instruction Pt4 includes an instruction g4() and an analysis result AR (see FIG. 11(A)). The instruction g4() also includes a procedure for generating a correspondence table Tbl from the notice of refusal NRFR, the claims PC, and the analysis result AR. The correspondence table Tbl includes claim CL and the reasons for refusal RFR related to claim CL (see FIG. 11(B)). The claims CL are included in the claims PC, and the reasons for refusal RFR are included in the notice of refusal NRFR. For example, the reason for refusal RFR_1 is the reason why claim CL_1 cannot be patented.

[0171] For example, the following paragraph of text can be used as directive Pt4.

[0172] "##Claims:Claims PC ##Notice: Notice of Rejection NRFR ##Analysis results:Analysis results AR Create a correspondence table Tbl from the analysis results AR above. ##Correspondence table:

[0173] Note that the above "##Claims:", "##Notice:" and "##Analysis Results:" are headings, and the "Analysis Results AR" following "##Analysis Results:" is the above analysis results AR.

[0174] The last line, "##Correspondence table:", is a heading that prompts the large-scale language model LLM to output. Also, "Create a correspondence table Tbl from the analysis results AR." corresponds to instruction g4().

[0175] [Example of directive Pt5] The instruction statement Pt5 includes an instruction g5(), an analysis result AR, and a correspondence table Tbl (see FIG. 12(A)). The instruction g5() also includes a procedure for generating a response policy list RPL. Each rejection reason RFR in the response policy list RPL lists multiple response policies RP (see FIG. 12(B)). Each response policy RP also indicates an advantage Adv and a disadvantage DAdv.

[0176] For example, rejection ground RFR_1 is the reason why claim CL_1 cannot be patented, and response strategies RP_1a and RP_1b are both measures to resolve rejection ground RFR_1. Also, advantage Adv_1a is an advantage of response strategy RP_1a, and drawback DAdv_1a is a drawback of response strategy RP_1a. Similarly, advantage Adv_1b is an advantage of response strategy RP_1b, and drawback DAdv_1b is a drawback of response strategy RP_1b.

[0177] For example, rejection reason RFR_2 is the reason why claim CL_2 cannot be patented, and response policy RP_2a and response policy RP_2b are both measures to resolve rejection reason RFR_2. Advantage Adv_2a is an advantage of response policy RP_2a, and drawback DAdv_2a is a drawback of response policy RP_2a. Similarly, advantage Adv_2b is an advantage of response policy RP_2b, and drawback DAdv_2b is a drawback of response policy RP_2b.

[0178] For example, the following paragraph of text can be used as directive Pt5.

[0179] "##Analysis result:Analysis result AR ##Correspondence table: Correspondence table Tbl Please propose multiple response strategies for each ground of rejection for each claim, and create a response strategy list (RPL) indicating the advantages and disadvantages of each response strategy. ##Response Policy List:

[0180] Note that the above "##Analysis results:" and "##Correspondence table:" are headings, the "Analysis results AR" following "##Analysis results:" is the above analysis results AR, and the "Correspondence table Tbl" following "##Correspondence table:" is the above correspondence table Tbl.

[0181] The last line, "##Response Policy List:", is a heading that prompts the large-scale language model LLM to output. Also, "For each reason for rejection, please propose multiple response policies. Also, please create a response policy list RPL by indicating the advantages and disadvantages of each response policy." corresponds to instruction g5().

[0182] This allows the analysis of the Notice of Rejection NRFR using the Claims PC, the Specification PSpc, and the Citations Ref to generate the analysis result AR. Furthermore, a correspondence table Tbl, which matches the Claims CL with the Reasons for Rejection RFR, can be generated from the analysis result AR. Furthermore, a response policy list RPL, which lists multiple response policies RP, can be generated and provided from the analysis result AR and the correspondence table Tbl. Furthermore, advantages and disadvantages of each response policy RP can be indicated. Furthermore, for example, a user of the information processing system can select a response policy RP by referring to the response policy list RPL. As a result, a novel information processing system with excellent convenience, usefulness, and reliability can be provided.

[0183] <Configuration example 4 of information processing system> The information processing system described in this embodiment includes a component 110, a component 120, and a component 130 (see FIG. 1).

[0184] For example, an information processing system according to an embodiment of the present invention can be configured with an information processing device that performs the functions of component 110, an information processing device that performs the functions of component 120, and an information processing device that performs the functions of component 130. Note that the number of information processing devices that configure the information processing system according to an embodiment of the present invention is one or more. Furthermore, for example, the information processing system according to an embodiment of the present invention can be configured by connecting a plurality of information processing devices using a network 51.

[0185] When an information processing system according to one embodiment of the present invention is configured using a plurality of information processing devices, the load related to information processing can be distributed.

[0186] <Configuration example 1 of information processing device> The first configuration example of the information processing device described in this embodiment can be used for the component 110. The first configuration example of the information processing device can also be called a client computer. For example, a desktop computer can be used for the component 110.

[0187] The information processing device configuration example 1 can accept data input by a user of the information processing system of an embodiment of the present invention. Also, the information processing device configuration example 1 can provide the user with data output by the information processing system of an embodiment of the present invention.

[0188] For example, dedicated application software, a web browser, etc. run in the component 110. A user of the information processing system according to an embodiment of the present invention can access the information processing system via either of these components, thereby enjoying services using the information processing system according to an embodiment of the present invention.

[0189] <Configuration example 2 of information processing device> The second example of the configuration of the information processing device described in this embodiment can be used for the component 120. For example, the component 120 can be a workstation, a server computer, a supercomputer, or the like.

[0190] Moreover, it is preferable that the information processing device in configuration example 2 has a function as a parallel computer. By using it as a parallel computer, it is possible to perform large-scale calculations necessary for learning and inference of artificial intelligence (AI), for example.

[0191] Moreover, the configuration example 2 of the information processing device can perform processing using a natural language model using AI.

[0192] For example, it is preferable to be able to perform processing using natural language models such as GPT-3 (registered trademark), GPT-3.5, GPT-4 (registered trademark), LaMDA, Llama2, and Llama3.

[0193] <Configuration example 3 of information processing device> For example, the third example configuration of the information processing device described in this embodiment can be used for the component 130. The component 130 is larger in scale and has higher computing power than the component 120. For example, a large computer such as a server computer or a supercomputer can be used for the component 130.

[0194] Moreover, it is preferable that the information processing device in configuration example 3 has a function as a parallel computer. By using it as a parallel computer, it is possible to perform large-scale calculations necessary for AI learning and inference, for example.

[0195] Moreover, the information processing device configuration example 3 can perform processing using a natural language model using AI. In particular, it can execute processing using a general-purpose language model that can perform various natural language processing tasks.

[0196] For example, processing can be performed using natural language models such as GPT-3 (registered trademark), GPT-3.5, GPT-4 (registered trademark), LaMDA, Llama2, and Llama3. In particular, it is preferable to be able to perform processing using GPT-4 (registered trademark). For example, being able to perform processing using a large-scale language model that is larger than conventional natural language models can enable more natural document generation or dialogue.

[0197] Note that a person who provides a service using an information processing system according to an embodiment of the present invention does not necessarily have to own the information processing device of Configuration Example 3. For example, a service provider can use part of a service provided by another business or the like using Configuration Example 3 of the information processing device.

[0198] <Network 51 configuration example> The network 51 that can be used in the information processing system according to one embodiment of the present invention can connect multiple information processing devices. This allows the connected multiple information processing devices to transmit and receive data to and from each other. Furthermore, the load associated with information processing can be distributed.

[0199] When performing wireless communication, communication standards such as the fourth generation mobile communication system (4G), fifth generation mobile communication system (5G), and sixth generation mobile communication system (6G), or specifications standardized by IEEE such as Wi-Fi (registered trademark) and Bluetooth (registered trademark), can be used as communication protocols or communication technologies.

[0200] For example, a local network can be used for the network 51. Also, an intranet or an extranet can be used for the network 51. Also, a personal area network (PAN), a local area network (LAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), a global area network (GAN), etc. can be used for the network 51.

[0201] Furthermore, for example, a global network can be used for the network 51. Specifically, the Internet, which is the foundation of the World Wide Web (WWW), can be used.

[0202] Furthermore, a person who provides a service using an information processing system according to an embodiment of the present invention can provide the service using an information processing method according to an embodiment of the present invention via a network 51, for example.

[0203] When the information processing system according to an embodiment of the present invention is built within a local network, the possibility of confidential information leaking can be reduced, for example, compared to when the Internet is used.

[0204] <Configuration Example 4 of Information Processing Device> An information processing device 20 that can be used in an information processing system according to one embodiment of the present invention includes, for example, an input unit 21, a storage unit 22, a processing unit 23, an output unit 24, and a transmission path 25 (see FIG. 13).

[0205] In the drawings attached to this specification, the components are classified by function and shown as independent blocks in the block diagrams, but in reality, it is difficult to completely separate the components by function, and one component may be involved in multiple functions. For example, part of the processing unit 23 may function as the input unit 21. Also, one function may be involved in multiple components. For example, the processing performed by the processing unit 23 may be executed by different information processing devices depending on the processing.

[0206] Input section 21 The input unit 21 can receive data from outside the information processing device. For example, the input unit 21 receives data via a network 51.

[0207] The input unit 21 supplies the received data to one or both of the storage unit 22 and the processing unit 23 via a transmission path 25 .

[0208] 《Storage section 22》 The storage unit 22 has a function of storing a program executed by the processing unit 23. The storage unit 22 can also have a function of storing data generated by the processing unit 23 (for example, calculation results, analysis results, inference results), data accepted by the input unit 21, and the like.

[0209] The storage unit 22 may have a database. Furthermore, the information processing device may have a database separate from the storage unit 22. The information processing device may have a function to retrieve data from a database that exists outside the storage unit 22, outside the information processing device, or outside the information processing system. Furthermore, the information processing device may have a function to retrieve data from both its own database and an external database.

[0210] Either or both of a storage and a file server can be used as the memory unit 22. Also, the memory unit 22 can be a database that records the paths of files stored in the file server.

[0211] The storage unit 22 includes at least one of a volatile memory and a nonvolatile memory. Examples of the volatile memory include a dynamic random access memory (DRAM) and a static random access memory (SRAM). Examples of the nonvolatile memory include a resistive random access memory (ReRAM), a phase change random access memory (PRAM), a ferroelectric random access memory (FeRAM), a magnetoresistive random access memory (MRAM), and a flash memory. The storage unit 22 may include at least one of NOSRAM (registered trademark) and DOSRAM (registered trademark). The storage unit 22 may include a recording media drive. Examples of the recording media drive include a hard disk drive (HDD) and a solid state drive (SSD).

[0212] NOSRAM is an abbreviation for "Nonvolatile Oxide Semiconductor Random Access Memory (RAM)." NOSRAM is a type of memory in which memory cells are two-transistor (2T) or three-transistor (3T) gain cells, and the transistors are transistors (also called OS transistors) that use metal oxides in the channel formation region. OS transistors have extremely low leakage current, i.e., the current that flows between the source and drain in the off state. NOSRAM can be used as a nonvolatile memory by retaining a charge corresponding to data in the memory cell using its extremely low leakage current. NOSRAM is particularly suitable for arithmetic processing that requires repeated large amounts of data read operations because it can read stored data without destroying it (nondestructive read). NOSRAM can increase its data capacity by stacking layers, so it can be used as a large-scale cache memory, main memory, or storage memory, thereby improving the performance of semiconductor devices.

[0213] DOSRAM is an abbreviation for "Dynamic Oxide Semiconductor RAM" and refers to RAM with 1T (transistor) 1C (capacitance) type memory cells. DOSRAM is a DRAM formed using OS transistors, and is a memory that temporarily stores information sent from outside. DOSRAM is a memory that takes advantage of the small off-current of OS transistors.

[0214] In this specification and the like, the term "metal oxide" refers to an oxide of a metal in a broad sense. Metal oxides are classified into oxide insulators, oxide conductors (including transparent oxide conductors), oxide semiconductors (also referred to as oxide semiconductors or simply as OSs), and the like. For example, when a metal oxide is used in a semiconductor layer of a transistor, the metal oxide may be referred to as an oxide semiconductor.

[0215] The metal oxide contained in the channel formation region preferably contains indium (In). When the metal oxide contained in the channel formation region contains indium, the carrier mobility (electron mobility) of the OS transistor is increased. For example, indium oxide (InOx) or indium gallium zinc oxide (In-Ga-Zn oxide, also referred to as "IGZO") can be used for the channel formation region. The metal oxide contained in the channel formation region is preferably an oxide semiconductor containing element M. The element M is preferably at least one of aluminum (Al), gallium (Ga), and tin (Sn). Other elements applicable to element M include boron (B), silicon (Si), titanium (Ti), iron (Fe), nickel (Ni), germanium (Ge), yttrium (Y), zirconium (Zr), molybdenum (Mo), lanthanum (La), cerium (Ce), neodymium (Nd), hafnium (Hf), tantalum (Ta), and tungsten (W). However, the element M may be a combination of two or more of the above elements. The element M is, for example, an element having a high bond energy with oxygen. For example, the element M is an element having a higher bond energy with oxygen than indium. Furthermore, the metal oxide contained in the channel formation region is preferably a metal oxide containing zinc (Zn). Metal oxides containing zinc may be more likely to crystallize.

[0216] The metal oxide contained in the channel formation region is not limited to a metal oxide containing indium, but may be, for example, a metal oxide containing zinc but not indium, such as zinc tin oxide or gallium tin oxide, a metal oxide containing gallium, or a metal oxide containing tin.

[0217] Processing Unit 23 The processing unit 23 has a function of performing processes such as calculation, analysis, and inference using data supplied from one or both of the input unit 21 and the storage unit 22. The processing unit 23 can supply the generated data (e.g., calculation results, analysis results, and inference results) to one or both of the storage unit 22 and the output unit 24.

[0218] The processing unit 23 has a function of acquiring data from the storage unit 22. The processing unit 23 can also have a function of recording or registering data in the storage unit 22.

[0219] The processing unit 23 may include, for example, an arithmetic circuit. The processing unit 23 may include, for example, a central processing unit (CPU). The processing unit 23 may also include a graphics processing unit (GPU). The processing unit 23 may also include an NPU (neural processing unit / neural network processing unit).

[0220] The processing unit 23 may have a microprocessor such as a DSP (Digital Signal Processor). The microprocessor may be realized by a PLD (Programmable Logic Device) such as an FPGA (Field Programmable Gate Array) or an FPAA (Field Programmable Analog Array). The processing unit 23 may also have a quantum processor. The processing unit 23 can perform various data processing and program control by interpreting and executing instructions from various programs using the processor. Programs that can be executed by the processor are stored in at least one of a memory area of ​​the processor and the storage unit 22.

[0221] The processing unit 23 may include a main memory. The main memory may include at least one of a volatile memory such as a RAM and a non-volatile memory such as a ROM (Read Only Memory). The main memory may also include at least one of the above-mentioned NOSRAM and DOSRAM.

[0222] The RAM may be, for example, a DRAM or an SRAM, and a virtual memory space is allocated to it and used as a working space for the processing unit 23. The operating system, application programs, program modules, program data, lookup tables, and the like stored in the storage unit 22 are loaded into the RAM for execution. The data, programs, and program modules loaded into the RAM are each directly accessed and operated by the processing unit 23.

[0223] ROM can store BIOS (Basic Input / Output System) and firmware, which do not require rewriting. Examples of ROM include mask ROM, OTPROM (One-Time Programmable Read Only Memory), and EPROM (Erasable Programmable Read Only Memory). Examples of EPROM include UV-EPROM (Ultra-Violet Erasable Programmable Read Only Memory), which allows stored data to be erased by exposure to ultraviolet light, EEPROM (Electrically Erasable Programmable Read Only Memory), and flash memory.

[0224] The processing unit 23 can include one or both of an OS transistor and a transistor having silicon in a channel formation region (Si transistor).

[0225] The processing unit 23 preferably includes an OS transistor. Because the off-state current of an OS transistor is extremely small, using the OS transistor as a switch for retaining charge (data) flowing into a capacitive element functioning as a memory element can ensure a long data retention period. By utilizing this characteristic in at least one of the register and cache memory of the processing unit, the processing unit can be operated only when necessary, and can be turned off at other times by saving the information from the previous processing in the memory element. In other words, normally-off computing becomes possible, enabling the information processing system to consume less power.

[0226] It is preferable that the information processing device uses AI for at least some of its processing.

[0227] It is particularly preferable that the information processing device uses an artificial neural network (ANN, hereinafter also simply referred to as a neural network). A neural network is realized by a circuit (hardware) or a program (software).

[0228] In this specification, a neural network refers to a general model that mimics the neural circuit network of a living organism, determines the connection strength between neurons through learning, and has problem-solving capabilities. A neural network has an input layer, an intermediate layer (hidden layer), and an output layer.

[0229] In this specification and the like, when discussing neural networks, determining the connection strengths (also called weighting coefficients) between neurons from existing information may be referred to as "learning."

[0230] In this specification and the like, the act of constructing a neural network using connection strengths obtained by learning and deriving a new conclusion from it may be referred to as "inference."

[0231] Output section 24 The output unit 24 can output at least one of the calculation result, analysis result, and inference result in the processing unit 23 to the outside of the information processing device. For example, the output unit 24 can transmit data via a network 51. Specifically, a device such as a personal computer equipped with a communication port or a communication function can be used. Furthermore, a device equipped with a communication function may be used for the input unit 21 and the output unit 24.

[0232] "Transmission Line 25" The transmission path 25 has a function of transmitting data. Data can be transmitted and received between the input unit 21, the storage unit 22, the processing unit 23, and the output unit 24 via the transmission path 25. Specifically, an external bus, a LAN, or the Internet can be used as the transmission path 25.

[0233] Note that this embodiment mode can be appropriately combined with other embodiment modes described in this specification.

[0234] (Embodiment 2) In this embodiment, an information processing method of one embodiment of the present invention will be described with reference to FIGS.

[0235] FIG. 14 is a flowchart illustrating an information processing method according to one embodiment of the present invention.

[0236] FIG. 15 is a flowchart illustrating an information processing method according to one embodiment of the present invention.

[0237] FIG. 16 is a flowchart illustrating an information processing method according to one embodiment of the present invention.

[0238] FIG. 17 is a sequence diagram illustrating an information processing method according to one embodiment of the present invention.

[0239] FIG. 18 is a sequence diagram illustrating an information processing method according to one embodiment of the present invention.

[0240] FIG. 19 is a sequence diagram illustrating an information processing method according to one embodiment of the present invention.

[0241] <Example of information processing method 1> The information processing method according to one embodiment of the present invention includes a phase PH1 (see FIG. 14).

[0242] <Example of Phase PH1> Phase PH1 comprises steps S1 to S8.

[0243] Step S1 In step S1 of phase PH1, component 110 accepts claims PC, a notice of reasons for refusal NRFR relating to the claims PC, and a draft argument DArg1 relating to the notice of reasons for refusal NRFR, and transmits them to component 120. For example, a user of the information processing system inputs claims PC, a notice of reasons for refusal NRFR, and a draft argument DArg1. Note that step S1 corresponds to the arrows extending from (1) and (2) in FIG. 17.

[0244] Step S2 In step S2 of phase PH1, component 120 receives claims PC, a notice of reasons for refusal NRFR, and a draft argument DArg1, and shares them within component 120. Note that component 120 includes subcomponents 120A and 120B.

[0245] Step S3 In step S3 of phase PH1, subcomponent 120A extracts an examination record list ExmL1 from database DB in response to query Que1.

[0246] Note that query Que1 requires that the technical field be the same as the claims PC and that the examiner is the same as the examiner responsible for the Office Action NRFR. Step S3 corresponds to the arrow extending from (3) in Figure 17.

[0247] The database DB also includes one or more examination records RExm. The examination record RExm includes a field for storing the opinion record RArg, a field for storing the notice record RNtc, a field for storing information IDExm that identifies the examiner in charge, and a field for storing information TchF that identifies the technical field. The notice record RNtc also includes a decision on the opinion record RArg.

[0248] Step S4 In step S4 of phase PH1, subcomponent 120B creates instruction statement Pt1 and sends it to component 130.

[0249] The instruction Pt1 includes an instruction g1(), an examination record list ExmL1, a notice of reasons for refusal NRFR, and a draft opinion DArg1. The instruction g1() also includes a procedure for generating a predicted Fcst by referring to the examination record list ExmL1. Step S4 corresponds to the arrows extending from (4) and (5) in FIG. 17.

[0250] Step S5 In step S5 of phase PH1, the component 130 receives the directive Pt1 and generates a prediction Fcst using the large-scale language model LLM.

[0251] Step S6 In step S6 of phase PH1, the component 130 transmits the predicted Fcst to the component 120. Note that step S6 corresponds to the arrow extending from (6) in FIG.

[0252] Step S7 In step S7 of phase PH1, the component 120 accepts the predicted Fcst and transmits the predicted Fcst to the component 110. Note that step S7 corresponds to the arrow extending from (7) in FIG.

[0253] Step S8 In step S8 of phase PH1, the component 110 accepts the predicted Fcst and provides it to, for example, a user of the information processing system. Note that step S8 corresponds to the arrow extending from (8) in FIG.

[0254] This allows the examiner who issued the Office Action NRFR to predict the decision on the draft opinion DArg1. Furthermore, the decision can be predicted by referring to the examination record list ExmL1 of the examiner who issued the Office Action NRFR. Furthermore, the decision can be predicted by referring to the opinion record RArg and notice record RNtc stored in the examination record list ExmL1. Furthermore, for example, a user of the information processing system can reconsider the draft opinion DArg1 by referring to the predicted Fcst. Furthermore, for example, a user of the information processing system can review the response policy RP by referring to the predicted Fcst. As a result, a novel information processing method that is highly convenient, useful, and reliable can be provided.

[0255] <Example of information processing method 2> An information processing method according to one embodiment of the present invention is an information processing method having a phase PH1 and a phase PH2 (see FIG. 15).

[0256] <Example of Phase PH2> Phase PH1 is followed by phase PH2, which comprises steps S1 to S8.

[0257] Step S1 In step S1 of phase PH2, component 110 accepts a response policy RP, claims PC, and a notice of reasons for refusal NRFR and transmits them to component 120. For example, a user of the information processing system inputs the response policy RP. Note that step S1 corresponds to the arrows extending from (1) and (2) in FIG. 18.

[0258] Step S2 In step S2 of phase PH2, component 120 accepts the response policy RP, claims PC, and notice of reasons for refusal NRFR and shares them within component 120.

[0259] Step S3 In step S3 of phase PH2, subcomponent 120A extracts examination record list ExmL2 from database DB in response to query Que2.

[0260] Query Que2 requires that the technical field is the same as the claims PC, that the examiner is the same as the examiner who handled the Office Action NRFR, and that the written opinion record RArg is similar to the response policy RP. Step S3 corresponds to the arrow extending from (3) in Figure 18.

[0261] Step S4 In step S4 of phase PH2, subcomponent 120B creates instruction statement Pt2 and sends it to component 130.

[0262] The instruction Pt2 includes an instruction g2() and an examination record list ExmL2. The instruction g2() includes a procedure for generating a draft opinion DArg2 by referring to the examination record list ExmL2. Step S4 corresponds to the arrows extending from (4) and (5) in FIG. 18.

[0263] Step S5 In step S5 of phase PH2, the component 130 receives the directive Pt2 and generates a draft opinion DArg2 using the large-scale language model LLM.

[0264] Step S6 In step S6 of phase PH2, the component 130 transmits the draft opinion DArg2 to the component 120. Note that step S6 corresponds to the arrow extending from (6) in FIG.

[0265] Step S7 In step S7 of phase PH2, the component 120 accepts the draft opinion DArg2 and transmits the draft opinion DArg2 to the component 110. Note that step S7 corresponds to the arrow extending from (7) in FIG.

[0266] Step S8 In step S8 of phase PH2, the component 110 accepts the draft opinion DArg2 and provides it to, for example, a user of the information processing system. Note that step S8 corresponds to the arrow extending from (8) in FIG.

[0267] This makes it possible to generate a draft opinion DArg2 based on the response policy RP. Furthermore, for example, it is possible to generate a draft opinion DArg2 that overturns the decision on the draft opinion DArg1. Furthermore, for example, it is possible to generate a draft opinion DArg2 that is easily accepted by the examiner who wrote the Notice of Reasons for Rejection NRFR. As a result, it is possible to provide a novel information processing method that is highly convenient, useful, and reliable.

[0268] <Example 3 of information processing method> An information processing method according to one embodiment of the present invention is an information processing method having a phase PH1, a phase PH2, and a phase PH3 (see FIG. 16).

[0269] <Example of Phase PH3> Phase PH2 is followed by phase PH3, which comprises steps S1 to S16.

[0270] Step S1 In step S1 of phase PH3, component 110 accepts claims PC, a specification PSpc relating to the claims PC, a notice of reasons for refusal NRFR relating to the claims PC, and a reference Ref relating to the notice of reasons for refusal NRFR, and transmits them to component 120. For example, a user of the information processing system inputs claims PC, a specification PSpc, a notice of reasons for refusal NRFR, and a reference Ref. Note that step S1 corresponds to the arrows extending from (1) and (2) in FIG. 19.

[0271] Step S2 In step S2 of phase PH3, component 120 receives the claims PC, the specification PSpc, the office action NRFR, and the cited references Ref, and shares them within component 120.

[0272] Step S3 In step S3 of phase PH3, subcomponent 120B creates instruction statement Pt3 and sends it to component 130.

[0273] The instruction Pt3 includes an instruction g3(), the specification PSpc, the claims PC, the notice of refusal NRFR, and a reference Ref, and the instruction g3() includes a procedure for analyzing the notice of refusal NRFR using the specification PSpc, the claims PC, and the reference Ref to generate an analysis result AR. Step S3 corresponds to the arrows extending from (3) and (4) in Figure 19.

[0274] Step S4 In step S4 of phase PH3, the component 130 receives the directive Pt3 and generates an analysis result AR using the large-scale language model LLM.

[0275] Step S5 In step S5 of phase PH3, the component 130 transmits the analysis result AR to the component 120. Note that step S5 corresponds to the arrow extending from (5) in FIG.

[0276] Step S6 In step S6 of phase PH3, the component 120 receives the analysis result AR and shares it within the component 120.

[0277] Step S7 In step S7 of phase PH3, subcomponent 120B creates instruction statement Pt4 and sends it to component 130.

[0278] Note that instruction Pt4 includes instruction g4() and the analysis result AR, and instruction g4() includes a procedure for generating a correspondence table Tbl from the analysis result AR. Note that the correspondence table Tbl includes claim CL and the reason for refusal RFR related to claim CL. Also, claim CL is included in claims PC, and the reason for refusal RFR is included in the notice of reason for refusal NRFR. Also, step S7 corresponds to the arrows extending from (6) and (7) in Figure 19.

[0279] Step S8 In step S8 of phase PH3, the component 130 receives the directive Pt4 and generates a correspondence table Tbl using the large-scale language model LLM.

[0280] Step S9 In step S9 of phase PH3, the component 130 transmits the correspondence table Tbl to the component 120. Note that step S9 corresponds to the arrow extending from (8) in FIG.

[0281] Step S10 In step S10 of phase PH3, the component 120 receives the correspondence table Tbl and shares it within the component 120.

[0282] Step S11 In step S11 of phase PH3, subcomponent 120B creates instruction statement Pt5 and sends it to component 130.

[0283] The instruction Pt5 includes an instruction g5(), an analysis result AR, and a correspondence table Tbl. The instruction g5() includes a procedure for generating a response policy list RPL. Each rejection reason RFR in the response policy list RPL lists multiple response policies RP. Each response policy RP also indicates its advantages and disadvantages. Step S11 corresponds to the arrows extending from (9) and (10) in FIG. 19.

[0284] Step S12 In step S12 of phase PH3, the component 130 receives the directive Pt5 and generates a response policy list RPL using the large-scale language model LLM.

[0285] Step S13 In step S13 of phase PH3, component 130 transmits the response policy list RPL to component 120. Note that step S13 corresponds to the arrow extending from (11) in FIG.

[0286] Step S14 In step S14 of phase PH1, the component 120 accepts the response policy list RPL and transmits the response policy list RPL to the component 110. Note that step S14 corresponds to the arrow extending from (12) in FIG.

[0287] Step S15 In step S15 of phase PH1, the component 110 accepts the response policy list RPL and provides it to, for example, a user of the information processing system. Note that step S15 corresponds to the arrow extending from (13) in FIG.

[0288] Step S14 In step S16 of phase PH1, the component 110 waits for input of a response policy RP. For example, a user of the information processing system inputs the response policy RP.

[0289] This allows the analysis of the Notice of Rejection NRFR using the Claims PC, the Specification PSpc, and the Citations Ref to generate the analysis result AR. Furthermore, a correspondence table Tbl, which matches the Claims CL with the Reasons for Rejection RFR, can be generated from the analysis result AR. Furthermore, a response policy list RPL, which lists multiple response policies RP, can be generated and provided from the analysis result AR and the correspondence table Tbl. Furthermore, advantages and disadvantages of each response policy RP can be indicated. Furthermore, for example, a user of an information processing system can select a response policy RP by referring to the response policy list RPL. As a result, a novel information processing method that is highly convenient, useful, and reliable can be provided.

[0290] Note that this embodiment mode can be appropriately combined with other embodiment modes described in this specification. [Explanation of symbols]

[0291] Adv Advantages Adv_1a Advantages Adv_1b Advantages Adv_2a Advantages Adv_2b Advantages AR analysis results DAdv Disadvantages DAdv_1a Disadvantages DAdv_1b Disadvantages DAdv_2a Disadvantages DAdv_2b Disadvantages DB Database Fcst prediction Id Identification information IDExm Information IDExm_1 Examiner LLM Large-Scale Language Model NRFR Notice of Rejection PSpc statement RArg Record of Opinion RArg_1 Opinion Record RArg_2 Opinion Record RArg_m Opinion Record Ref citation RExm Review Record RFR Reason for Rejection RFR_1 Reason for Refusal RFR_2 Reason for Refusal RNtc Notification Record RNtc_1 Notification Record RNtc_2 Notification Record RNtc_m notification record RP Response Policy RP_1a Response Policy RP_1b Response Policy RP_2a Response Policy RP_2b Response Policy RPL Response Policy List SE Search Engine Tbl Correspondence Table TchF Information TchF_1 Technical Field 20 Information processing equipment 21 Input section 22 Memory section 23 Processing section 24 Output section 25 Transmission Line 51 Network 99 User 110 Components 120 components 120A Subcomponents 120B Subcomponent 130 Components

Claims

1. a first component; and a second component; and a third component, the first component has a function of receiving claims, a notice of reasons for refusal related to the claims, and a first draft argument related to the notice of reasons for refusal and transmitting them to the third component, and a function of receiving and providing a forecast; the second component has a function of receiving a first instruction sentence and transmitting the prediction to the third component, and a function of performing processing using a large-scale language model; the large-scale language model is operable to generate the prediction in accordance with the first instruction sentence; the third component has a function of receiving the claims, the notice of reasons for rejection, and the first draft argument and sharing them within the third component, and a function of receiving the forecast and sending it to the first component; the third component comprises a first subcomponent and a second subcomponent; the first subcomponent has a function of performing processing using a database and a search engine; the database comprises one or more audit records; The examination record includes a field for storing an opinion record, a field for storing a notice record, a field for storing first information for identifying the examiner in charge, and a field for storing second information for identifying the technical field, The notice record includes a judgment on the opinion record; The search engine has a function of extracting a first review record list from the database in response to a first query; The first query requires that the technical field be the same as that of the claims and that the examiner is the same as the examiner responsible for the office action; the second subcomponent has a function of creating the first instruction statement and sending it to the second component; the first instruction includes a first instruction, the first prosecution record listing, and the first draft opinion; the first instructions include a procedure for generating the prediction by referring to the first review record list; The information processing system, wherein the prediction includes a judgment on the first draft opinion.

2. the first component has a function of accepting a response policy and sending it to the third component, and a function of accepting a second draft opinion and providing it; the second component has a function of receiving a second instruction statement and transmitting the second draft opinion to the third component; the large-scale language model has a function of generating the second draft opinion in accordance with the second instruction sentence; the third component has a function of receiving the response policy and sharing it within the third component, and a function of receiving the second draft opinion and sending it to the first component; the search engine has a function of extracting a second review record list from the database in response to a second query; The second query requires that the technical field be the same as the scope of the claims, that the examiner be the same as the examiner assigned to the Office Action, and that the record of opinion be similar to the response policy; the second subcomponent has a function of creating the second instruction statement and sending it to the second component; the second instruction sentence includes a second instruction and the second audit record list; The information processing system according to claim 1 , wherein the second instructions include a procedure for generating the second draft opinion by referring to the second review record list.

3. the first component has a function of receiving the specification relating to the claims and the citation relating to the notice of reasons for refusal and transmitting them to the third component, and a function of receiving and providing a response policy list; the second component has a function of receiving a third instruction statement and transmitting an analysis result to the third component, a function of receiving a fourth instruction statement and transmitting a correspondence table to the third component, and a function of receiving a fifth instruction statement and transmitting the response policy list to the third component; the large-scale language model comprises a function of generating the analysis result in accordance with the third directive, a function of generating the correspondence table in accordance with the fourth directive, and a function of generating the response policy list in accordance with the fifth directive; the third component has a function of receiving the specification and the cited references and sharing them within the third component, and a function of receiving the response policy list and sending it to the first component; the second subcomponent has a function of creating the third instruction statement, the fourth instruction statement, and the fifth instruction statement and sending them to the second component; the third instruction includes a third instruction, the description, the claims, the notice of rejection, and the reference; the third instructions include a procedure for analyzing the notice of rejection using the description, the claims, and the references to generate the analysis result; the fourth instruction statement includes a fourth instruction and the analysis result; the fourth instruction includes a procedure for generating the correspondence table from the analysis result, The correspondence table includes claims and reasons for refusal related to the claims, The claims are within the scope of the claims, The reasons for refusal are included in the notice of reasons for refusal, the fifth instruction statement includes a fifth instruction, the analysis result, and the correspondence table; the fifth instructions include a procedure for generating the response policy list; Each of the reasons for refusal in the response policy list lists a plurality of response policies; The information processing system of claim 2 , wherein each of the response policies has advantages and disadvantages indicated.

4. 1. An information processing method having a first phase, comprising: The first phase comprises first to eighth steps, In a first step of the first phase, a first component receives claims, a notice of reasons for refusal relating to the claims, and a first draft argument relating to the notice of reasons for refusal, and transmits them to a second component; In a second step of the first phase, the second component receives the claims, the notice of rejection, and the first draft argument and shares them within the second component; the second component comprises a first subcomponent and a second subcomponent; In a third step of the first phase, the first subcomponent extracts a first review record list from a database in response to a first query; The first query requires that the technical field be the same as that of the claims and that the examiner is the same as the examiner responsible for the office action; the database comprises one or more audit records; The examination record includes a field for storing an opinion record, a field for storing a notice record, a field for storing first information for identifying the examiner in charge, and a field for storing second information for identifying the technical field, The notice record includes a judgment on the opinion record; In a fourth step of the first phase, the second subcomponent creates a first instruction statement and sends it to a third component; the first instruction includes a first instruction, the first prosecution record listing, and the first draft opinion; the first instructions include a procedure for generating a prediction by referring to the first review record list; In a fifth step of the first phase, the third component receives the first instruction sentence and generates the prediction using a large-scale language model; In a sixth step of the first phase, the third component transmits the prediction to the second component; In a seventh step of the first phase, the second component accepts and transmits the prediction to the first component; In an eighth step of the first phase, the first component accepts and provides the prediction.

5. An information processing method having a second phase, comprising: the first phase follows the second phase; the second phase comprises first to eighth steps, In the first step of the second phase, the first component accepts a response policy, the claims, and the office action and transmits them to the second component; In the second step of the second phase, the second component receives the response policy, the claims, and the office action and shares them within the second component; In the third step of the second phase, the first subcomponent extracts a second review record list from the database in response to a second query; The second query requires that the technical field be the same as the scope of the claims, that the examiner be the same as the examiner assigned to the Office Action, and that the record of opinion be similar to the response policy; In the fourth step of the second phase, the second subcomponent creates and sends a second instruction to the third component; the second instruction sentence includes a second instruction and the second audit record list; the second instructions include a procedure for generating a second draft opinion with reference to the second examination record list; In the fifth step of the second phase, the third component receives the second directive and generates the second draft opinion using the large-scale language model; In the sixth step of the second phase, the third component transmits the second draft opinion to the second component; In the seventh step of the second phase, the second component accepts the second draft opinion and sends it to the first component; The information processing method according to claim 4 , wherein in the eighth step of the second phase, the first component accepts and provides the second draft opinion.

6. An information processing method having a third phase, comprising: the second phase follows the third phase; the third phase comprises steps 1 to 16; In the first step of the third phase, the first component receives the claims, the description relating to the claims, the notice of reasons for refusal relating to the claims, and the citations relating to the notice of reasons for refusal, and transmits them to the second component; In the second step of the third phase, the second component receives the claims, the description, the office action, and the references and shares them within the second component; In the third step of the third phase, the second subcomponent creates and sends a third instruction to the third component; the third instruction includes a third instruction, the description, the claims, the notice of rejection, and the reference; the third instructions include a procedure for analyzing the notice of rejection using the description, the claims, and the references to generate an analysis result; In the fourth step of the third phase, the third component receives the third instruction sentence and generates the analysis result using the large-scale language model; In the fifth step of the third phase, the third component transmits the analysis result to the second component; In the sixth step of the third phase, the second component receives the analysis result and shares it within the second component; In the seventh step of the third phase, the second subcomponent creates and sends a fourth instruction to the third component; the fourth instruction statement includes a fourth instruction and the analysis result; the fourth instruction includes a procedure for generating a correspondence table from the analysis result, The correspondence table includes claims and reasons for refusal related to the claims, The claims are within the scope of the claims, The reasons for refusal are included in the notice of reasons for refusal, In the eighth step of the third phase, the third component receives the fourth instruction sentence and generates the correspondence table using the large-scale language model; In the ninth step of the third phase, the third component transmits the correspondence table to the second component; In the tenth step of the third phase, the second component receives the correspondence table and shares it within the second component; In the eleventh step of the third phase, the second subcomponent creates a fifth instruction statement and sends it to the third component; the fifth instruction statement includes a fifth instruction, the analysis result, and the correspondence table; the fifth instructions include a procedure for generating a response policy list; Each of the reasons for refusal in the response policy list lists a plurality of response policies; Each of the response strategies has its advantages and disadvantages; In the twelfth step of the third phase, the third component receives the fifth directive and generates the response policy list using the large-scale language model; In the thirteenth step of the third phase, the third component sends the response policy list to the second component; In the fourteenth step of the third phase, the second component accepts the response policy list and sends it to the first component; In the fifteenth step of the third phase, the first component accepts and provides the response policy list; 6. The information processing method according to claim 5, wherein in the sixteenth step of the third phase, the first component waits for input of the response policy.

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