Analysis program, information processing device, and analysis method

Abstract

A patent information analysis program according to the present invention causes a computer to execute a step for acquiring technical information including a technical concept, a step for acquiring patent information to be compared to the technical information, a step for specifying the technical information and patent information to be included in a prescribed instruction statement for evaluating the relatedness of the two pieces of information from a specific perspective, and a step for outputting an evaluation result indicating, in terms of the perspective, the relatedness between the technical information and the patent information on the basis of results obtained from a large language model by inputting, into the large language model, the instruction statement in which the technical information and patent information are specified.

Description

Analysis program, information processing device, and analysis method

[0001] The present disclosure relates to an analysis program, an information processing device, and an analysis method.

[0002] In recent years, various large-scale language models have been developed. Large-scale language models are language models trained using a huge amount of text data, and are trained to be able to process a variety of natural languages.

[0003] Non-Patent Document 1 discloses a method for improving the output accuracy of ChatGPT, an example of a large-scale language model. Specifically, Non-Patent Document 1 discloses that the accuracy of the output is improved by inputting the instruction "Let's think step by step" into ChatGPT.

[0004] "The Spell to Make ChatGPT Smarter," Nikkei Newspaper, [online], March 24, 2023, [Retrieved September 6, 2023], Internet <URL: https: / / www.nikkei.com / article / DGXZQOUC22BVO0S3A320C2000000 / >

[0005] Large-scale language models can be applied to a variety of services, but there are still few examples of them being implemented as specific services. In this regard, there is a desire to use large-scale language models in patent information analysis to provide new value.

[0006] In one example of the present disclosure, there is provided a patent information analysis program, which causes a computer to execute the steps of acquiring technical information including a technical idea, acquiring patent information to be compared with the technical information, specifying the technical information and the patent information in response to a predetermined instruction statement for evaluating the relevance of two pieces of information from a specific perspective, and outputting an evaluation result indicating the relevance between the technical information and the patent information for the perspective based on a result obtained from a large-scale language model by inputting the instruction statement specifying the technical information and the patent information into the large-scale language model.

[0007] In one example of the present disclosure, the predetermined instruction sentence is defined to evaluate the relevance of two pieces of information from a plurality of perspectives, and in the outputting step, an evaluation result indicating the relevance between the technical information and the patent information is output for each of the plurality of perspectives.

[0008] In one example of the present disclosure, the multiple perspectives include at least one of a technical field perspective, a problem perspective, an action function perspective, a content suggestion perspective, a parameter perspective, a numerical limitation perspective, and a technical terminology perspective.

[0009] In one example of the present disclosure, the analysis program further causes the computer to execute a step of outputting a comprehensive evaluation result that summarizes the other evaluation results of the aspects.

[0010] In one example of the present disclosure, each of the plurality of viewpoints is associated with a weight, and the comprehensive evaluation result is calculated by assigning the weight to the other evaluation results of the viewpoints.

[0011] In one example of the present disclosure, the predetermined instruction sentence includes an instruction to cause the large-scale language model to output a degree of relevance between the two pieces of information and an instruction to cause the large-scale language model to output a reason for evaluating the degree of relevance, and the evaluation result includes the degree of relevance between the technical information and the patent information and the reason for evaluating the technical information and the patent information.

[0012] In one example of the present disclosure, the analysis program further causes the computer to execute a step of receiving an input regarding a concept level when comparing the patent information with the technical information, and the specifying step further includes reflecting the input regarding the concept level in the instruction statement.

[0013] In one example of the present disclosure, the analysis program further causes the computer to execute the steps of: accepting input regarding a conceptual level when comparing the patent information with the technical information while the evaluation result is displayed on a display; generating an instruction statement for reevaluation that reflects the input regarding the conceptual level; and inputting the instruction statement for reevaluation into the large-scale language model to output a result obtained from the large-scale language model.

[0014] In one example of the present disclosure, the analysis program further causes the computer to execute a step of calculating a similarity between the technical information and the patent information based on a predetermined algorithm, and the evaluation result is output for each of the viewpoints based on the result obtained from the large-scale language model and the similarity.

[0015] In one example of the present disclosure, the multiple perspectives include a first perspective and a second perspective different from the first perspective. The large-scale language model includes a first language model and a second language model different from the first language model. The instruction sentence for evaluating the relevance between the technical information and the patent information from the first perspective is input to the first language model. The instruction sentence for evaluating the relevance between the technical information and the patent information from the second perspective is input to the second language model.

[0016] In one example of the present disclosure, the technical information includes drawings, the patent information includes drawings, and the viewpoints include viewpoints of the contents of the drawings.

[0017] In one example of the present disclosure, the instruction sentence includes a first instruction to evaluate the relevance of the two pieces of information from a specific perspective, and a second instruction to further evaluate the relevance of the two pieces of information from the specific perspective using the evaluation result of the relevance of the two pieces of information based on the first instruction.

[0018] In another example of the present disclosure, an information processing device capable of analyzing patent information is provided. The information processing device includes a control unit for operating the information processing device. The control unit executes the following processes: acquiring technical information including a technical idea; acquiring patent information to be compared with the technical information; specifying the technical information and the patent information in response to a predetermined instruction statement for evaluating the relevance of two pieces of information from a viewpoint of relevance; and outputting an evaluation result indicating the relevance between the technical information and the patent information for the viewpoint based on a result obtained from a large-scale language model by inputting the instruction statement specifying the technical information and the patent information into the large-scale language model.

[0019] In another example of the present disclosure, a computer-executable method for analyzing patent information is provided, the method comprising the steps of: acquiring technical information including a technical idea; acquiring patent information to be compared with the technical information; specifying the technical information and the patent information in a predetermined instruction statement for evaluating the relevance of two pieces of information from a plurality of perspectives; and inputting the instruction statement specifying the technical information and the patent information into a large-scale language model, based on a result obtained from the large-scale language model, outputting an evaluation result indicating the relevance between the technical information and the patent information for the plurality of perspectives.

[0020] The above and other objects, features, aspects and advantages of the present invention will become apparent from the following detailed description of the invention taken in conjunction with the accompanying drawings.

[0021] 1 is a diagram showing an example of the device configuration of an information processing system. FIG. 2 is a diagram for explaining a relevance assessment function. FIG. 3 is a diagram for explaining a relevance assessment function according to a modified example. FIG. 4 is a schematic diagram showing an example of the hardware configuration of an information processing device. FIG. 5 is a schematic diagram showing an example of the hardware configuration of a user terminal. FIG. 6 is a diagram showing an example of data flow between an information processing device, a user terminal, and a server. FIG. 7 is a diagram showing an example of an input screen for search conditions. FIG. 8 is a diagram showing an example of an input screen for peer review conditions. FIG. 9 is a diagram showing an example of a directive. FIG. 10 is a diagram showing answer information which is an example of an answer generated by a large-scale language model. FIG. 11 is a diagram showing an example of an evaluation result screen. FIG. 12 is a diagram showing a flowchart related to relevance assessment between technical information and patent information. FIG. 13 is a diagram showing an example of weights associated by viewpoint. FIG. 14 is a diagram showing an outline of relevance assessment processing according to another example. FIG. 15 is a diagram showing an outline of relevance assessment processing according to another example. FIG. 16 is a diagram showing another example of a directive.

[0022] Hereinafter, each embodiment according to the present invention will be described with reference to the drawings. In the following description, the same parts and components are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated. Note that each embodiment and each modified example described below may be selectively combined as appropriate.

[0023] <A. Information Processing System 10> First, the device configuration of the information processing system 10 will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of the device configuration of the information processing system 10.

[0024] 1, the information processing system 10 includes an information processing device 100, a user terminal 200, and a server 300. The information processing device 100, the user terminal 200, and the server 300 are configured to be able to communicate with each other via a network NW (for example, the Internet).

[0025] The information processing device 100 is a notebook or desktop personal computer (PC), a tablet terminal, a smartphone, or any other computer equipped with a communication function. The number of information processing devices 100 constituting the information processing system 10 may be one, or may be two or more. The information processing device 100 is operated, for example, by a company "A."

[0026] The user terminal 200 is, for example, a notebook or desktop PC, a tablet terminal, a smartphone, or any other computer with a communication function. The number of user terminals 200 constituting the information processing system 10 may be one, or may be two or more. The user terminal 200 is owned, for example, by user "A," who is a general user.

[0027] The server 300 is a laptop or desktop personal computer (PC), a tablet terminal, a smartphone, or any other computer with a communication function. The number of servers 300 constituting the information processing system 10 may be one, or two or more. The server 300 is operated by, for example, company "B."

[0028] The server 300 stores a large-scale language model 324. The large-scale language model 324 is a language model trained on a huge amount of text data, more than several billion pieces of text, and is trained to be able to process various natural languages. The large-scale language model 324 is also called an LLM (Large Language Model). The large-scale language model 324 is trained so that when a directive is input, it generates an output corresponding to the directive.

[0029] Examples of the large-scale language model 324 include the GPT series such as GPT-3 (Generative Pretrained Transformer) and GPT-4, PaLM (Scaling Language Modeling with Pathways), LLaMA (Large Language Model Meta AI), and the well-known LLM. In addition to the GPT series, various large-scale language models may be used, such as a transformer-based large-scale language model such as BERT (Bidirectional Encoder Representations from Transformers), a CNN (Convolutional Neural Network), an RNN (Recurrent Neural Network), and an LSTM (Long Short Term Memory).

[0030] For example, company "B" publishes an API (Application Programming Interface) for using the functions of the large-scale language model 324. This allows designers and general users of company "A" to use the functions of the large-scale language model 324 through the API.

[0031] The various processes described in this specification may be implemented in the information processing device 100, the user terminal 200, the server 300, or other computers.

[0032] Furthermore, although the above description has been given of an example in which the information processing system 10 includes the server 300, the information processing system 10 does not have to include the server 300. In this case, the information processing system 10 is configured with one or more information processing devices 100 and one or more user terminals 200.

[0033] <B. Processing Overview> The information processing device 100 provides various analysis functions related to patent information to the user "A." As an example, the information processing device 100 provides the user "A" with a function for evaluating the relevance between technical information and patent information.

[0034] "Patent information" refers to data related to patent documents. Patent information includes, for example, patent documents such as published patent gazettes, patent publications, published patent gazettes, republished patents, and utility model publications. As an example, patent information includes bibliographic information, a specification, claims, drawings, and an abstract. Bibliographic information includes, for example, an application number, a publication number, a patent registration number, an application date, a publication date, a registration date, an applicant, a patent owner, a title of the invention, an agent, and an application country. Patent information may also be non-patent documents such as technical journals.

[0035] "Technical information" refers to data that includes a technical idea. A technical idea is a technical means for solving a technical problem. Technical information is, for example, a description of a technical means in the form of a string of characters. Technical information includes, for example, invention information that describes matters that specify an invention.

[0036] An outline of the function of evaluating the relevance between the technical information 123 and the patent information 125 will be described below with reference to Fig. 2. Fig. 2 is a diagram for explaining the relevance evaluation function.

[0037] The information processing device 100 acquires technical information 123 from the user terminal 200. The technical information 123 may be a character string input by user "A" of the user terminal 200, or may be data extracted from patent information in response to an operation by user "A."

[0038] The information processing device 100 also acquires patent information 125 to be compared with the technical information 123. The patent information 125 may be acquired from any source. As one example, the patent information 125 is acquired from a patent database 124 (see FIG. 4 ) described below that is stored in a storage device within the information processing device 100. As another example, the patent information 125 is acquired from a patent database in a server managed by the Patent Office. As yet another example, the patent information 125 is acquired from a patent database managed by another server.

[0039] The relevance evaluation between the technical information 123 and the patent information 125 is realized by utilizing the large-scale language model 324. More specifically, first, the information processing device 100 specifies the technical information 123 and the patent information 125 in a predetermined directive 128 for evaluating the relevance between the two pieces of information from a specific viewpoint.

[0040] The directive 128 is registered in advance in the information processing device 100 as, for example, a template. The directive 128 also includes an argument portion 129A and an argument portion 129B. The technical information 123 is specified in the argument portion 129A. On the other hand, the patent information 125 is specified in the argument portion 129B.

[0041] Furthermore, the directive 128 specifies from what viewpoint the relevance between the technical information 123 and the patent information 125 is to be evaluated. In the example of FIG. 2, it specifies that the two pieces of information are to be evaluated from viewpoint "α". The viewpoint "α" may be specified in advance or may be specified as an argument. When the viewpoint "α" is specified as an argument, the viewpoint is arbitrarily specified by the user "A" of the user terminal 200.

[0042] The instruction statement 128 specifying the technical information 123 and the patent information 125 is input to the large-scale language model 324. Upon receiving the instruction statement 128, the large-scale language model 324 generates a response corresponding to the instruction statement 128. The generated response is output to the information processing device 100.

[0043] The information processing device 100 outputs an evaluation result 130 indicating the relevance between the technical information 123 and the patent information 125 for the perspective "α" based on the response obtained from the large-scale language model 324. The relevance may be expressed as a numerical value indicating the degree of relevance, or may be expressed as an explanatory sentence. In the example of FIG. 2, the relevance is expressed as a numerical value.

[0044] The evaluation result 130 from the information processing device 100 may be output to any destination. As an example, the output destination is the user terminal 200. The evaluation result 130 output to the user terminal 200 is displayed on the display of the user terminal 200, for example.

[0045] As described above, the information processing device 100 provides new value to the user by using the large-scale language model 324 to evaluate the relevance between the technical information 123 and the patent information 125 .

[0046] The relevance assessment function is effective in the following cases, for example. Until now, patent research has mainly been conducted by humans screening or peer-reviewing thousands to tens of thousands of patent documents. In this case, if a user uses the relevance assessment function shown in FIG. 2, they can easily determine the degree to which the technical information 123 being researched is related to the patent information 125. This allows the user to significantly reduce the time required for screening or peer-reviewing.

[0047] In the example of Figure 2, the instruction statement 128 is defined to evaluate the relevance from a single perspective "α", but the instruction statement 128 may also be defined to evaluate the relevance from multiple perspectives.

[0048] 3 is a diagram illustrating a relevance assessment function according to a modified example. The instruction 128 shown in FIG. 3 is defined to assess the relevance between two pieces of information from multiple perspectives, "α" and "β." Upon receiving the instruction 128, the large-scale language model 324 generates a response corresponding to the instruction 128.

[0049] The information processing device 100 outputs an evaluation result 130 indicating the relevance between the technical information 123 and the patent information 125 based on the results obtained from the large-scale language model 324. The evaluation result 130 indicates the relevance between the technical information 123 and the patent information 125 for each of the perspectives "α" and "β". The evaluation result 130 is transmitted from the information processing device 100 to the user terminal 200 and displayed on the display of the user terminal 200.

[0050] This allows the user to determine whether the technical information 123 is related to the patent information 125 based on the relevance from multiple perspectives. As a result, the user can more easily determine to what extent the technical information 123 being investigated is related to the patent information 125.

[0051] <C. Types of Viewpoints> The viewpoints "α" and "β" defined in the directive 128 will be described with continued reference to FIGS.

[0052] Any viewpoint may be defined in the directive 128. Preferably, the viewpoint may be a matter that the user considers important when evaluating the relevance between the technical information 123 and the patent information 125. As an example, the judgment index for "motivation for applying the secondary cited invention to the primary cited invention" described in "Part III, Chapter 2, Section 2, Inventive Step" of the Examination Guidelines is defined as viewpoints "α" and "β".

[0053] More specifically, the viewpoints "α" and "β" include at least one of the viewpoint of commonality of technical fields, the viewpoint of commonality of problems, the viewpoint of commonality of action and function, the viewpoint of commonality of content suggestions, the viewpoint of commonality of parameters, the viewpoint of commonality of numerical limitations, and the viewpoint of commonality of technical terms. As a result, the commonality between the technical information 123 and the patent information 125 is evaluated using an evaluation axis that meets the criteria for determining inventive step.

[0054] Note that the viewpoint defined in the directive 128 is not limited to the above example. As another example, the viewpoint defined in the directive 128 may be a viewpoint suitable for use search. The viewpoint may be a viewpoint of commonality of the application or product to which the invention is applied, or a viewpoint of commonality of the industrial field to which the invention is applied.

[0055] As yet another example, the viewpoint specified in the directive 128 may be a viewpoint of commonality of the drawings, a specific example of which will be described later.

[0056] As yet another example, the viewpoint specified in the instruction 128 may be a viewpoint for evaluating whether or not there are any obstacles to combining one of the inventions related to the technical information 123 and the inventions related to the patent information 125 with the other.

[0057] As yet another example, it may be possible to evaluate whether or not the difference between the invention related to the technical information 123 and the invention related to the patent information 125 is a design change.

[0058] <D. Hardware Configuration> Next, the hardware configurations of the information processing device 100 and the user terminal 200 shown in FIG. 1 will be described in order with reference to FIGS. 4 and 5. FIG.

[0059] The hardware configuration of server 300 shown in FIG. 1 is similar to that of information processing device 100, and therefore description thereof will not be repeated.

[0060] (D1. Information Processing Device 100) First, the hardware configuration of the information processing device 100 shown in Fig. 1 will be described with reference to Fig. 4. Fig. 4 is a schematic diagram showing an example of the hardware configuration of the information processing device 100.

[0061] The information processing device 100 includes a control device 101, a read only memory (ROM) 102, a random access memory (RAM) 103, a communication interface 104, a display interface 105, an input interface 107, and an auxiliary storage device 120. These components are connected to a bus 110.

[0062] The control device 101 is configured, for example, by at least one integrated circuit. The integrated circuit may be configured, for example, by at least one central processing unit (CPU), at least one graphics processing unit (GPU), at least one application specific integrated circuit (ASIC), at least one field programmable gate array (FPGA), or a combination thereof.

[0063] The control device 101 controls the operation of the information processing device 100 by executing various programs such as an analysis program 122 and an operating system. Upon receiving an execution command for one of the various programs, the control device 101 reads the program from the auxiliary storage device 120 or the ROM 102 into the RAM 103. The RAM 103 functions as a working memory and temporarily stores various data required for executing the various programs.

[0064] A LAN (Local Area Network), an antenna, etc. are connected to the communication interface 104. The information processing device 100 exchanges data with external devices via the communication interface 104. The external devices include, for example, a user terminal 200, a server 300, and other communication devices.

[0065] A display 106 is connected to the display interface 105. The display interface 105 sends an image signal for displaying an image to the display 106 in accordance with a command from the control device 101 or the like. The display 106 is, for example, a liquid crystal display, an organic EL (Electro Luminescence) display, or other display device. The display 106 may be configured integrally with the information processing device 100 or may be configured separately from the information processing device 100.

[0066] An input device 108 is connected to the input interface 107. The input device 108 is, for example, a mouse, a keyboard, a touch panel, or any other device capable of receiving user operations. The input device 108 may be configured integrally with the information processing device 100 or may be configured separately from the information processing device 100.

[0067] The auxiliary storage device 120 is, for example, a hard disk, a flash memory, an SSD (Solid State Drive), or other storage medium. The auxiliary storage device 120 stores the analysis program 122, the patent database 124, the instruction statement 128, and the like. The patent database 124 includes a plurality of pieces of patent information 125. The analysis program 122, the patent database 124, and the instruction statement 128 may not be stored in the auxiliary storage device 120, but may also be stored in a memory area (e.g., cache memory, etc.) of the control device 101, the ROM 102, the RAM 103, an external device, or the like.

[0068] The analysis program 122 may be provided not as a standalone program but as part of an arbitrary program. In this case, various processes defined in the analysis program 122 are realized in cooperation with an arbitrary program such as the analysis program 222 described below. Even a program that does not include some of these modules does not deviate from the spirit of the analysis program 122 according to this embodiment. Furthermore, some or all of the functions provided by the analysis program 122 may be realized by dedicated hardware. Furthermore, the information processing device 100 may be configured in the form of a so-called cloud service in which at least one server executes part of the processing of the analysis program 122.

[0069] (D2. User Terminal 200) Next, the hardware configuration of the user terminal 200 shown in Fig. 1 will be described with reference to Fig. 5. Fig. 5 is a schematic diagram showing an example of the hardware configuration of the user terminal 200.

[0070] The user terminal 200 includes a control device 201, a ROM 202, a RAM 203, a communication interface 204, a display interface 205, an input interface 207, and an auxiliary storage device 220. These components are connected to a bus 210.

[0071] The control device 201 is configured, for example, by at least one integrated circuit. The integrated circuit may be configured, for example, by at least one CPU, at least one GPU, at least one ASIC, at least one FPGA, or a combination thereof.

[0072] The control device 201 controls the operation of the user terminal 200 by executing various programs such as an analysis program 222 and an operating system. Upon receiving an execution command for one of the programs, the control device 201 reads the program from the auxiliary storage device 220 or the ROM 202 into the RAM 203. The RAM 203 functions as a working memory and temporarily stores various data required for executing the program.

[0073] A LAN, an antenna, and the like are connected to the communication interface 204. The user terminal 200 exchanges data with external devices via the communication interface 204. The external devices include, for example, the information processing device 100, the server 300, and other communication devices. The user terminal 200 may be configured to be able to download the analysis program 222 from the information processing device 100.

[0074] A display 206 is connected to the display interface 205. The display interface 205 sends an image signal for displaying an image to the display 206 in accordance with a command from the control device 201 or the like. The display 206 is, for example, a liquid crystal display, an organic EL display, or other display device. The display 206 may be configured integrally with the user terminal 200 or may be configured separately from the user terminal 200.

[0075] An input device 208 is connected to the input interface 207. The input device 208 is, for example, a mouse, a keyboard, a touch panel, or any other device capable of receiving user operations. The input device 208 may be configured integrally with the user terminal 200 or may be configured separately from the user terminal 200.

[0076] The auxiliary storage device 220 is, for example, a hard disk, a flash memory, an SSD (Solid State Drive), or other storage medium. The auxiliary storage device 220 stores the analysis program 222 and the like. The analysis program 222 may be stored in a storage area of ​​the control device 201 (for example, a cache memory), the ROM 202, the RAM 203, an external device (for example, a server), or the like, without being limited to the auxiliary storage device 220.

[0077] The analysis program 222 may be provided not as a standalone program but as part of an arbitrary program. In this case, various processes defined in the analysis program 222 are realized in cooperation with an arbitrary program such as the above-described analysis program 122. Even a program that does not include some of these modules does not deviate from the spirit of the analysis program 222 according to this embodiment. Furthermore, some or all of the functions provided by the analysis program 222 may be realized by dedicated hardware. Furthermore, the user terminal 200 may be configured in the form of a so-called cloud service in which at least one server executes part of the processing of the analysis program 222.

[0078] <E. Data Flow> Next, the operation of the information processing system 10 relating to the evaluation of the relevance between the technical information 123 and the patent information 125 will be described with reference to Figures 6 to 11. Figure 6 is a diagram showing an example of data flow between the information processing device 100, the user terminal 200, and the server 300.

[0079] As described above, the information processing device 100 provides a function for evaluating the relevance between the technical information 123 and the patent information 125. In the following, the explanation will be given on the assumption that the relevance evaluation function is applied to the review of the patent information 125.

[0080] (E1. Step S110) In step S110, the user terminal 200 displays the input screen 400A shown in Fig. 7. Fig. 7 is a diagram showing an example of the input screen 400A.

[0081] The input screen 400A accepts search conditions for searching the patent information 125 from the patent database 124 (see FIG. 4 ). By inputting the search conditions, the user can specify one or more pieces of patent information 125 to be compared with the technical information 123. The input screen 400A includes, for example, a selection field 410 and an input field 412.

[0082] The selection field 410 accepts a selection input of a search type. For example, search types that can be specified in the selection field 410 include "full-text search," "field search," "command search," "semantic search," and "number search."

[0083] The input field 412 accepts input of various search conditions. Examples of search conditions that can be input into the input field 412 include the patent document number, the type of the number (e.g., application number, publication number, etc.), and the country in which the application was published.

[0084] (E2. Step S112) Next, in step S112, the user terminal 200 displays an input screen 400A shown in Fig. 8. Fig. 8 is a diagram showing an input screen 400B as an example.

[0085] The input screen 400B receives the setting of the peer review conditions. As an example, the input screen 400B includes an input field 420, a selection field 422, an input field 424, a selection field 426, and a start button 428.

[0086] The input field 420 accepts settings for review targets. As an example, the input field 420 accepts input of the maximum number of patent information 125 to be reviewed and the review portion of the patent information 125. Examples of review portions that can be specified include the abstract, the title of the invention, the top claim in the claims (i.e., claim 1), the technical field, the background art, the problem that the invention aims to solve, the effects of the invention, the means for solving the problem, and the specification.

[0087] The selection field 422 accepts the selection of a review type. Selectable review types include, for example, relevance evaluation, user instructions such as summarization and information extraction, and user tag / evaluation.

[0088] The input field 424 accepts input of the above-mentioned technical information 123. The input field 424 accepts input of, for example, a sentence indicating a technical idea. As one example, a user inputs content invented by the user or another company into the input field 424. As another example, a user inputs the specifications of their company's products into the input field 424.

[0089] The selection field 426 accepts the selection of the large-scale language model 324 to be used. The large-scale language model 324 selected in the selection field 426 is used for evaluating the relevance between the technical information 123 and the patent information 125.

[0090] When the user presses the start review button 428, the user terminal 200 transmits the search conditions entered on the input screen 400A shown in Figure 7 and the review conditions entered on the input screen 400B shown in Figure 8 to the information processing device 100.

[0091] (E3. Step S120) Next, in step S120, the information processing device 100 searches for patent information 125 that matches the search conditions entered on the input screen 400A (see FIG. 7 ) from among the patent information 125 registered in the patent database 124. The information processing device 100 regards the patent information 125 that matches the search conditions as a population to be compared with the technical information 123.

[0092] (E4. Step S122) Next, in step S122, the information processing apparatus 100 generates the above-mentioned directive statement 128 (see FIGS. 2 and 3) to be input to the large-scale language model 324.

[0093] 9 is a diagram showing an example of a directive 128. The directive 128 includes a plurality of argument sections 129A and a plurality of argument sections 129B. The information processing device 100 specifies technical information 123 for each of the plurality of argument sections 129A, and specifies patent information 125 for each of the plurality of argument sections 129B. The directive 128 is generated for each of the patent information 125 searched in step S120.

[0094] 9, the instruction 128 preferably includes an instruction 131A for causing the large-scale language model 324 to output the relevance between the two pieces of information, and an instruction for causing the large-scale language model 324 to output the reason for the evaluation of the relevance, thereby increasing the number of indicators for the user to determine the relevance.

[0095] In addition, the instruction statement 128 includes an instruction 131C to output the relevance of the two pieces of information to the large-scale language model 324 by synthesizing the evaluation results from each perspective, and an instruction 131D to output the reason for the comprehensive evaluation to the large-scale language model 324.

[0096] The information processing device 100 transmits the instruction statement 128 generated in step S122 to the server 300.

[0097] (E5. Step S130) Next, in step S130, the server 300, upon receiving the instruction statement 128 from the information processing device 100, inputs the instruction statement 128 into the large-scale language model 324. As a result, the large-scale language model 324 generates an answer corresponding to the instruction statement 128.

[0098] 10 is a diagram showing answer information 326, which is an example of an answer generated by the large-scale language model 324. In the example of Fig. 10, the answer information 326 is shown in a table format, but the format of the answer information 326 is arbitrary. The output format of the large-scale language model 324 is, for example, specified in advance in the instruction statement 128, and the large-scale language model 324 outputs the answer information 326 in accordance with the output format specified in the instruction statement 128.

[0099] The response information 326 includes evaluation results 327 of the relevance between the technical information 123 and the patent information 125 for each piece of patent information 125. The evaluation results 327 include a relevance score indicating the degree of relevance between the technical information 123 and the patent information 125, and an evaluation reason for the degree of relevance. The relevance score is included for each evaluation perspective specified in the instruction statement 128. Similarly, the evaluation reason is included for each evaluation perspective specified in the instruction statement 128. Furthermore, the evaluation results 327 include an overall evaluation score for each evaluation perspective, and the overall evaluation reason.

[0100] The evaluation result 327 can be uniquely identified by the identifier of the patent information 125. The identifier is specified, for example, by the application number of the patent information 125, the publication number of the patent information 125, the registered publication number of the patent information 125, the applicant information of the patent information 125, or the like.

[0101] The information processing device 100 transmits the response information 326 generated in step S130 to the information processing device 100.

[0102] (E6. Steps S140, S150) Next, in step S140, information processing device 100 generates an evaluation result screen based on response information 326 received from server 300. The evaluation result screen is written in a language such as HTML (HyperText Markup Language), for example.

[0103] Next, in step S150, the user terminal 200 displays the evaluation result screen generated by the information processing device 100. Fig. 11 is a diagram showing an example of the evaluation result screen 400C. The evaluation result screen 400C is displayed on the display 206 of the user terminal 200, for example.

[0104] The evaluation result screen 400C includes a display area 430 that displays information related to the technical information 123, and a display area 440 that displays the evaluation results of the relevance between the technical information 123 and the patent information 125. The display area 440 includes a display area 442 and evaluation result display areas 444 and 446. The display areas 440 are arranged in accordance with the number of pieces of patent information 125 compared with the technical information 123.

[0105] The display area 430 displays information entered on the above-mentioned input screens 400A and 400B (see FIGS. 7 and 8), etc. As an example, the display area 430 displays an "ID" (Identification), a "Review Type", a "Model", a "Status", a "Handling Time", a "Review Subject", and a "Research Perspective".

[0106] "ID" is an identifier for uniquely identifying the peer review evaluation results. "Peer review type" corresponds to the item set in the selection field 422 (see FIG. 8) described above. "Model" indicates the type of large-scale language model 324 used during the relevance evaluation, and corresponds to the information set in the selection field 426 (see FIG. 8) described above. "Status" indicates whether the relevance evaluation process was completed successfully. "Handling time" indicates the time required from the start to the end of the relevance evaluation process. "Peer review target" corresponds to the information entered in the input field 420 (see FIG. 8) described above. "Research perspective" indicates the content of the technical information 123, and corresponds to the item entered in the input field 420 (see FIG. 8) described above.

[0107] The display area 442 displays information related to the patent information 125 that was the subject of the relevance evaluation. For example, the display area 442 displays the "Title of the Invention," "Application Number," "Publication Number," "Status," and "Abstract."

[0108] In "Summary," for example, an outline of the contents of the patent information 125 is displayed. As one example, the outline is generated from the results output from the large-scale language model 324 when the large-scale language model 324 is instructed to summarize the contents of the patent information 125. As another example, the outline is generated from the abstract included in the patent information 125.

[0109] Display area 444 displays the evaluation results generated from response information 326 (see FIG. 10 ) of large-scale language model 324 in the form of a graph. In the example of FIG. 11 , the evaluation results are displayed as a radar chart showing evaluation scores from each perspective. Preferably, the type of graph to be displayed in display area 444 can be arbitrarily set by the user.

[0110] Display area 446 displays in tabular form the evaluation results generated from answer information 326 (see FIG. 10 ) of large-scale language model 324. In the example of FIG. 11 , display area 446 displays the relevance scores for each viewpoint included in answer information 326, the evaluation reasons for each viewpoint included in answer information 326, the overall evaluation score included in answer information 326, and the overall evaluation reason included in answer information 326.

[0111] In this way, the information processing device 100 outputs a comprehensive evaluation result that summarizes the evaluation results for each viewpoint, thereby enabling the user to easily recognize the degree of association between the technical information 123 and the patent information 125.

[0112] Preferably, a "total score" is further displayed in the display area 446 as the overall evaluation result. The "total score" corresponds to the sum of the evaluation result scores for each viewpoint. Note that the "average score" of the evaluation result scores for each viewpoint may be displayed instead of the "total score."

[0113] The evaluation results displayed on the evaluation result screen 400C may be configured to be selectable for each evaluation perspective. For example, when evaluating relevance focusing on the "perspective of commonality of issues," the user deselects perspectives other than the "perspective of commonality of issues." As a result, only the evaluation results related to the "perspective of commonality of issues" are displayed on the evaluation result screen 400C. This configuration allows the user to evaluate the relevance between the technical information 123 and the patent information 125 according to their own purpose. The selection of the evaluation perspective may be reflected in the instruction statement 128 before generating the answer information 326, or may be reflected only in the display content of the evaluation result screen 400C.

[0114] The evaluation criteria displayed on the evaluation result screen 400C may be configured to be rearrangeable, which allows the user to display evaluation items with higher priority according to their own purpose at the top.

[0115] Furthermore, the multiple display areas 440 displayed on the evaluation result screen 400C are configured to be rearrangeable according to selected sorting conditions, such as ascending / descending order of evaluation score, ascending / descending order of overall evaluation score, and ascending / descending order of total score.

[0116] Furthermore, the content displayed on the evaluation result screen 400C is not limited to the example of FIG. 11 . The evaluation result screen 400C may display even more information (hereinafter also referred to as "additional information"). Examples of additional information include a technical summary of the patent information 125, a problem summary of the patent information 125, characteristic keywords contained in the patent information 125, and a classification assigned to the patent information 125. This additional information is generated by the large-scale language model 324.

[0117] <I. Flowchart> Next, an example of a control flow of the information processing device 100 will be described with reference to Fig. 12. Fig. 12 is a diagram showing a flowchart relating to the evaluation of the relevance between the technical information 123 and the patent information 125.

[0118] 12 is realized by control device 101 of information processing device 100 executing the above-described analysis program 122. In another aspect, some or all of the processing may be performed by circuit elements or other hardware.

[0119] In step S210, the control device 101 determines whether the relatedness assessment start button 428 (see FIG. 8) has been pressed. If the control device 101 determines that the relatedness assessment start button 428 has been pressed (YES in step S210), the control device 101 switches control to step S212. If not (NO in step S210), the control device 101 executes the process of step S210 again.

[0120] In step S212, the control device 101 searches for patent information 125 registered in the patent database 124 that matches the search conditions entered on the input screen 400A (see FIG. 7).

[0121] In step S214, the control device 101 generates the above-mentioned instruction statement 128 to be input to the large-scale language model 324. The process of generating the instruction statement 128 is as described above, and therefore the description thereof will not be repeated. The control device 101 transmits the generated instruction statement 128 to the server 300.

[0122] The server 300 inputs the received instruction sentence 128 into the large-scale language model 324, and generates answer information 326 (see FIG. 10) corresponding to the instruction sentence 128. The answer information 326 is transmitted to the information processing device 100.

[0123] In step S216, control device 101 generates evaluation result screen 400C (see FIG. 11 ) described above based on response information 326 received from server 300. Evaluation result screen 400C is as described above, and therefore description thereof will not be repeated.

[0124] In step S218, the control device 101 outputs the evaluation result screen 400C generated in step S216 to the user terminal 200. As a result, the user terminal 200 displays the evaluation result screen 400C on the display 206.

[0125] <J1. Others> Next, other examples of the above-described embodiment will be described.

[0126] As described above, the overall evaluation results such as the "overall evaluation score" and the "total score" shown in Fig. 11 are generated based on the results of evaluating the relevance between the technical information 123 and the patent information 125 from each perspective. At this time, the evaluation results relating to each perspective may be weighted.

[0127] FIG. 13 is a diagram showing an example of weights associated with each viewpoint. In the example of FIG. 13, a weight "w1" is associated with viewpoint "α". A weight "w2" is associated with viewpoint "β". A weight "w3" is associated with viewpoint "γ". A weight "w4" is associated with viewpoint "δ". The weights "w1" to "w4" may be set in advance or may be set arbitrarily by the user.

[0128] 13 also shows a relevance score "s1" as an evaluation result from viewpoint "α". A relevance score "s2" is shown as an evaluation result from viewpoint "β". A relevance score "s3" is shown as an evaluation result from viewpoint "γ". A relevance score "s4" is shown as an evaluation result from viewpoint "δ".

[0129] The information processing device 100 calculates an overall evaluation result by taking into account weights "w1" to "w4" for the different evaluation results "s1" to "s4" of the viewpoints. As an example, the information processing device 100 calculates a weighted average by multiplying the evaluation results "s1" to "s4" by the weights "w1" to "w4", respectively. The information processing device 100 outputs the calculated weighted average as the overall evaluation result.

[0130] <J2. Others> Next, still another example of the above embodiment will be described.

[0131] In some cases, the technical information 123 and the patent information 125 may be compared using broader concepts than expected. To prepare for such cases, the information processing device 100 is configured to be able to re-evaluate the relevance between the technical information 123 and the patent information 125. Specifically, when the evaluation result screen 400C (see FIG. 11 ) is displayed on the display 206 of the user terminal 200, the information processing device 100 accepts an input regarding the conceptual level at which the patent information 125 is compared with the technical information 123.

[0132] In one aspect, "input on the conceptual level" is realized in an input field (not shown) in the evaluation result screen 400C. As an example, assume that the evaluation result of the technical information 123 and the patent information 125 displays the result "Both patents are related to an information processing system and its method." In this case, the user inputs, for example, the following phrase as "input on the conceptual level": "The concept of 'information processing system and its method' is too broad, so please compare it with a more specific concept such as 'an information processing system that appropriately evaluates strategies.'" By inputting such an instruction, "input on the conceptual level" is realized.

[0133] In another aspect, the "input regarding the concept level" is realized in a selection field (not shown) in the evaluation result screen 400C. The options in the selection field indicate the concept level at the time of evaluation. The options include, for example, the concept "broad," the concept "normal," and the concept "narrow."

[0134] The concept "broad" is associated with a directive for reevaluating relevance based on a first concept. The concept "medium" is associated with a directive for reevaluating relevance based on a second concept, which is narrower than the first concept. The concept "narrow" is associated with a directive for reevaluating relevance based on a third concept, which is narrower than the second concept.

[0135] Thereafter, the information processing device 100 generates an instruction statement for reevaluation that reflects the "input related to the concept level." The reflection method is arbitrary. As an example, the information processing device 100 includes the "input related to the concept level" in the instruction statement for reevaluation.

[0136] Thereafter, the information processing device 100 inputs the generated directive sentence for re-evaluation into the large-scale language model 324. Then, the information processing device 100 outputs the results obtained from the large-scale language model 324. This allows the user to perform relevance evaluation while gradually narrowing the concept level.

[0137] The timing for accepting "inputs related to the concept level" is not limited to after the execution of the relevance evaluation, but may be before the execution of the relevance evaluation.

[0138] As an example, the "input regarding the concept level" may be received on the above-mentioned input screens 400A and 400B (see FIGS. 7 and 8) for setting conditions related to the relevance evaluation. The "input regarding the concept level" is reflected in the above-mentioned instruction statement 128 (see FIG. 9, etc.). This allows the user to specify the concept level and perform the relevance evaluation.

[0139] <J3. Others> Next, still another example of the above embodiment will be described.

[0140] 11, the evaluation scores and evaluation reasons output from the large-scale language model 324 are output as they are on the evaluation result screen 400C. However, the output of the large-scale language model 324 does not necessarily have to be output as it is on the evaluation result screen 400C.

[0141] As an example, the information processing device 100 calculates the similarity between the technical information 123 and the patent information 125 based on a predetermined algorithm. Any technology may be used as the similarity calculation algorithm. As an example, the information processing device 100 extracts feature vectors based on text, images, patent classification, or the like from each of the technical information 123 and the patent information 125. The information processing device 100 then calculates the similarity between the feature vector extracted from the technical information 123 and the feature vector extracted from the patent information 125. The similarity may be, for example, cosine similarity. To extract the feature vector, for example, indices based on word frequency, such as TF, TFIDF, BM25, or other indices, indices obtained by models that generate word vectors, such as Word2Vec or FastText, or indices obtained by a sentence encoder model, such as BERT, may be used.

[0142] The information processing device 100 then combines the evaluation results for each viewpoint obtained from the large-scale language model 324 with the calculated similarity and outputs the combined result as an evaluation result. The method for combining the evaluation results for each viewpoint and the similarity is arbitrary. As an example, the information processing device 100 combines the evaluation results for each viewpoint obtained from the large-scale language model 324 by adding the calculated similarity.

[0143] <J4. Others> Next, still another example of the above embodiment will be described.

[0144] In the above example, the information processing device 100 uses one large-scale language model 324 when evaluating the relevance between the technical information 123 and the patent information 125 from each perspective. However, the information processing device 100 may use a different large-scale language model 324 for each perspective to evaluate the relevance between the technical information 123 and the patent information 125.

[0145] 14 is a diagram showing an outline of the relevance assessment process in this example, in which different types of large-scale language models 324A and 324B are shown.

[0146] An instruction statement 128A for evaluating the relevance between the technical information 123 and the patent information 125 from the viewpoint "α" is input to the large-scale language model 324A. On the other hand, an instruction statement 128B for evaluating the relevance between the technical information 123 and the patent information 125 from the viewpoint "β" is input to the large-scale language model 324B. In this way, in this example, the large-scale language models 324A and 324B to be used are determined for each evaluation viewpoint.

[0147] Typically, the large-scale language model 324A is tuned to suit relevance evaluation from the viewpoint "α." Similarly, the large-scale language model 324B is tuned to suit relevance evaluation from the viewpoint "β." For this tuning, for example, fine-tuning of the Chat GPT API or fine-tuning of an open source model such as Llama2 or Japanese Stable LM is used.

[0148] <J5. Others> Next, still another example of the above embodiment will be described.

[0149] In the above example, the information processing device 100 evaluates the relevance between the text content of the technical information 123 and the text content of the patent information 125. In contrast, in the present example, the information processing device 100 evaluates the relevance between the content of the drawings included in the technical information 123 and the drawings included in the patent information 125.

[0150] FIG. 15 is a diagram illustrating the relevance evaluation process in this example. As shown in FIG. 15, technical information 123 includes one or more drawings 123F. Similarly, patent information 125 includes one or more drawings 125F. Furthermore, directive 128 specifies that the relevance of the two pieces of information should be evaluated from the perspective of the content of the drawings. Drawings 123F and 125F are, for example, image data. There are no particular limitations on the format of the images.

[0151] The information processing device 100 specifies the technical information 123 for the argument part 129A of the directive 128, and specifies the patent information 125 for the argument part 129B of the directive 128. The directive 128, in which the technical information 123 and the patent information 125 are specified, is transmitted from the information processing device 100 to the server 300, and input to the large-scale language model 324 in the server 300.

[0152] When the large-scale language model 324 receives the instruction 128 as input, it generates a response corresponding to the instruction 128. The large-scale language model 324 is trained to be able to interpret the content of an image. As a result, the large-scale language model 324 interprets the content of the drawing 123F included in the technical information 123, and also interprets the content of the drawing 125F included in the patent information 125. Examples of large-scale language models that can interpret the content of an image include CLIP (Contrastive Language-Image Pretraining), BLIP (Bootstrapping Language Image Pre-training for unified vision-language understanding and generation), and ViLBERT (Vision-and-Language BERT).

[0153] The information processing device 100 outputs an evaluation result 130 indicating the relevance between the technical information 123 and the patent information 125 from the viewpoint of the content of the drawings, based on each interpretation result obtained from the large-scale language model 324. The relevance may be expressed as a numerical value indicating the degree of relevance, or may be expressed as an explanatory text. In the example of FIG. 15, the relevance is expressed as a numerical value.

[0154] When the above-mentioned relevance is output as an explanatory text, the explanatory text is expressed in wording such as "Drawing 123F included in technical information 123 has a structure called XXX, and compared to drawing 125F included in patent information 125, the part called XXX does not match."

[0155] <J6. Others> Next, still another example of the above embodiment will be described.

[0156] In this example, the information processing device 100 evaluates the relevance between the technical information 123 and the patent information 125 in stages from the same viewpoint, thereby improving the accuracy of the relevance evaluation.

[0157] 16 is a diagram showing instruction statement 128C in this example. Instruction statement 128C includes instruction 132A (first instruction) and instruction 132B (second instruction). Instruction 132A is an instruction to evaluate the relevance of two pieces of information from a specific perspective. Instruction 132B is an instruction to further evaluate the relevance of the two pieces of information from the same perspective, using the evaluation result of the relevance of the two pieces of information based on instruction 132A.

[0158] The information processing device 100 specifies the technical information 123 for the argument part 129A of the directive 128C, and specifies the patent information 125 for the argument part 129B of the directive 128C. The directive 128C specifying the technical information 123 and the patent information 125 is input to the large-scale language model 324. Upon receiving the input of the directive 128C, the large-scale language model 324 evaluates the relevance between the technical information 123 and the patent information 125 in multiple stages according to the directive 128C.

[0159] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the above description, and is intended to include all modifications within the meaning and scope of the claims.

[0160] 10 Information processing system, 100 Information processing device, 101 Control device, 102 ROM, 103 RAM, 104 Communication interface, 105 Display interface, 106 Display, 107 Input interface, 108 Input device, 110 Bus, 120 Auxiliary storage device, 122 Analysis program, 123 Technical information, 123F Drawing, 124 Patent database, 125 Patent information, 125F Drawing, 128 Instruction, 128A Instruction, 128B Instruction, 128C Instruction, 129A Argument part, 129B Argument part, 130 Evaluation result, 132A Instruction, 132B Instruction, 200 User terminal, 201 Control device, 202 ROM, 203 RAM, 204 Communication interface, 205 Display interface, 206 Display, 207 Input interface, 208 Input device, 210 bus, 220 auxiliary storage device, 222 analysis program, 300 server, 324 large-scale language model, 324A large-scale language model, 324B large-scale language model, 326 response information, 400A input screen, 400B input screen, 400C evaluation result screen, 410 selection field, 412 input field, 420 input field, 422 selection field, 424 input field, 426 selection field, 428 start button, 430 display area, 440 display area, 442 display area, 444 display area, 446 display area, NW network.

Claims

1. A patent information analysis program which causes a computer to execute the following steps: acquiring technical information including a technical idea; acquiring patent information to be compared with the technical information; specifying the technical information and the patent information in response to a predetermined instruction sentence for evaluating the relevance of two pieces of information from a specific viewpoint; and outputting an evaluation result indicating the relevance between the technical information and the patent information for the viewpoint based on a result obtained from a large-scale language model by inputting the instruction sentence in which the technical information and the patent information are specified into the large-scale language model.

2. The analysis program of claim 1, wherein the specified instruction sentence is defined to evaluate the relevance of the two pieces of information from multiple perspectives, and in the outputting step, evaluation results indicating the relevance between the technical information and the patent information are output for each of the multiple perspectives.

3. The analysis program of claim 2, wherein the multiple perspectives include at least one of a technical field perspective, a problem perspective, an action / function perspective, a content suggestion perspective, a parameter perspective, a numerical limitation perspective, and a technical term perspective.

4. The analysis program according to claim 2 or 3, further causing the computer to execute a step of outputting a comprehensive evaluation result that summarizes the separate evaluation results of the viewpoints.

5. The analysis program according to claim 4, wherein each of the plurality of perspectives is associated with a weight, and the overall evaluation result is calculated by applying the weight to the other evaluation results of the perspectives.

6. An analysis program according to any one of claims 1 to 3, wherein the specified instruction sentence includes an instruction to output the relevance of the two pieces of information to the large-scale language model, and an instruction to output an evaluation reason for the relevance to the large-scale language model, and the evaluation result includes the relevance relating to the technical information and the patent information, and the evaluation reason relating to the technical information and the patent information.

7. The analysis program according to any one of claims 1 to 3, further comprising the step of: causing the computer to execute a step of receiving an input regarding a conceptual level when comparing the patent information with the technical information; and the step of specifying further includes reflecting the input regarding the conceptual level in the instruction statement.

8. The analysis program according to any one of claims 1 to 3, further comprising the steps of: receiving an input regarding a conceptual level at which the patent information is compared with the technical information while the evaluation result is being displayed on a display; generating an instruction sentence for re-evaluation that reflects the input regarding the conceptual level; and inputting the instruction sentence for re-evaluation into the large-scale language model to output the results obtained from the large-scale language model.

9. The analysis program according to any one of claims 1 to 3, further comprising causing the computer to execute a step of calculating the similarity between the technical information and the patent information based on a predetermined algorithm, and the evaluation results are output for each of the perspectives based on the results obtained from the large-scale language model and the similarity.

10. An analysis program as described in claim 2 or 3, wherein the multiple perspectives include a first perspective and a second perspective different from the first perspective, the large-scale language model includes a first language model and a second language model different from the first language model, the instruction sentence for evaluating the relevance between the technical information and the patent information from the first perspective is input to the first language model, and the instruction sentence for evaluating the relevance between the technical information and the patent information from the second perspective is input to the second language model.

11. An analysis program according to any one of claims 1 to 3, wherein the technical information includes drawings, the patent information includes drawings, and the viewpoints include viewpoints of the contents of the drawings.

12. An analysis program as claimed in any one of claims 1 to 3, wherein the instruction sentence includes: a first instruction for causing the relevance of the two pieces of information to be evaluated from a specific perspective; and a second instruction for causing the relevance of the two pieces of information to be further evaluated from the specific perspective using a result of the evaluation of the relevance of the two pieces of information based on the first instruction.

13. An information processing device capable of analyzing patent information, comprising a control unit for operating the information processing device, the control unit executing the following processes: a process of acquiring technical information including a technical idea; a process of acquiring patent information to be compared with the technical information; a process of designating the technical information and the patent information in response to a predetermined instruction sentence for evaluating the relevance of two pieces of information from a viewpoint; and a process of outputting an evaluation result indicating the relevance between the technical information and the patent information for the viewpoint based on the result obtained from a large-scale language model by inputting the instruction sentence in which the technical information and the patent information are designated into the large-scale language model.

14. A method for analyzing patent information executed by a computer, comprising: a step of acquiring technical information including a technical idea; a step of acquiring patent information to be compared with the technical information; a step of designating the technical information and the patent information in response to a predetermined instruction sentence for evaluating the relevance of two pieces of information from multiple perspectives; and a step of inputting the instruction sentence in which the technical information and the patent information are designated into a large-scale language model, and outputting an evaluation result indicating the relevance between the technical information and the patent information for the perspectives based on the results obtained from the large-scale language model.

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