Analysis program, information processing device, and analysis method

Abstract

We provide technology that leverages large-scale language models in SDI to deliver new value. [Solution] The patent document analysis program causes the computer to perform the following processes: receiving input of pre-configured settings for the analysis of patent documents; obtaining the first newly arrived patent document from a patent database; inputting a first instruction sentence for analyzing the content of the first newly arrived patent document according to the pre-configured settings into a large-scale language model, and generating analysis results for the first newly arrived patent document based on the results obtained from the large-scale language model; and outputting the analysis results.

Description

Technical Field

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

Background Art

[0002] In recent years, various large language models have been developed. A large language model is a language model that has learned an enormous amount of text data and is trained to be able to process various natural languages.

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

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Although large language models can be applied to various services, there are still few examples of being realized as specific services. In this regard, it is desired to utilize large language models for SDI (Selective Dissemination of Information) to provide new value.

Means for Solving the Problems

[0006] One example of this disclosure provides a patent document analysis program. The analysis program causes a computer to perform the following processes: receiving input of pre-configured settings for the analysis of patent documents; obtaining a first newly arrived patent document from a patent database; inputting a first instruction sentence for analyzing the content of the first newly arrived patent document according to the pre-configured settings into a large-scale language model, and generating analysis results for the first newly arrived patent document based on the results obtained from the large-scale language model; and outputting the analysis results.

[0007] In one example of this disclosure, the above pre-configuration includes technical information representing a technical idea. The above first instruction includes instructions for evaluating the relationship between the above technical information and the above first new patent document. The above analysis results include analysis results showing the relationship between the above technical information and the above first new patent document.

[0008] In one example of this disclosure, the output process includes the process of delivering the analysis results to the user's terminal.

[0009] In one example of this disclosure, the analysis program causes the computer to execute a process that accepts input of filtering conditions to further narrow down the output target. In the output process, the analysis results are output for the first newly acquired patent document that satisfies the filtering conditions.

[0010] In one example of this disclosure, the analysis program causes the computer to perform the following processes: to acquire a second new patent document that was added to the patent database after the first new patent document; to generate contextual information that includes some or all of the analysis results regarding the first new patent document; to input a second instruction for analyzing the content of the second new patent document into the large-scale language model and generate analysis results regarding the second new patent document based on the results obtained from the large-scale language model; and to output the analysis results regarding the second new patent document. The second instruction includes the contextual information.

[0011] In one example of this disclosure, the analysis program further causes the computer to execute a process to receive user evaluations regarding the analysis results for the first newly acquired patent documents. The process for generating the context information includes a process for extracting first patent documents from among the first newly acquired patent documents acquired in the acquisition process for which the user evaluations satisfy pre-set conditions, and a process for adding the analysis results relating to the extracted first patent documents to the context information.

[0012] In one example of this disclosure, the first instruction includes an instruction to individually analyze each of the first newly acquired patent documents according to the above-mentioned pre-configuration, and an instruction to comprehensively analyze each of the first newly acquired patent documents according to the above-mentioned pre-configuration.

[0013] In one example of this disclosure, the above pre-configuration causes the system to execute a process that accepts input of filtering conditions for narrowing down the analysis target. The above analysis program causes the computer to execute a process that extracts first patent documents that satisfy the above filtering conditions from among the first newly acquired patent documents acquired in the above acquisition process. The above first instruction specifies the extracted first patent documents as the analysis target.

[0014] In one example of this disclosure, the analysis program causes the computer to perform the following processes: inputting a third instruction to the large-scale language model to extract new information that is not present in the first new patent document but newly appears in the second new patent document; and outputting the new information obtained from the large-scale language model by inputting the third instruction.

[0015] Another example of the present disclosure provides an information processing device capable of analyzing patent documents. The information processing device includes a control unit. The control unit performs the following processes: receiving input of a pre-configuration for analyzing patent documents; obtaining a first new patent document from a patent database; inputting a first instruction sentence for analyzing the content of the first new patent document in accordance with the pre-configuration into a large-scale language model, and generating an analysis result for the first new patent document based on the results obtained from the large-scale language model; and outputting the analysis result.

[0016] Another example of the present disclosure provides a method for analyzing patent documents. The analysis method comprises the steps of: receiving input of a pre-configuration for the analysis of patent documents; obtaining a first new patent document from a patent database; inputting a first instruction sentence for analyzing the content of the first new patent document in accordance with the pre-configuration into a large-scale language model, and generating an analysis result for the first new patent document based on the results obtained from the large-scale language model; and outputting the analysis result.

[0017] The above and other objects, features, aspects and advantages of the present invention will become apparent from the following detailed description relating to the invention, which will be understood in conjunction with the accompanying drawings. [Brief explanation of the drawing]

[0018] [Figure 1] This figure shows an example of the device configuration of an information processing system. [Figure 2] This diagram schematically illustrates the SDI function according to the embodiment. [Figure 3] This diagram schematically illustrates the SDI function related to relevance evaluation. [Figure 4] This is a schematic diagram showing an example of the hardware configuration of an information processing device. [Figure 5] This is a schematic diagram showing an example of the hardware configuration of a user terminal. [Figure 6] This diagram shows an example of the data flow when performing pre-configuration. [Figure 7] This is a diagram showing an example of a setting screen. [Figure 8] This is a diagram showing an example of a data flow when performing analysis processing on newly arrived patent documents. [Figure 9] This is a diagram showing an example of an instruction sentence. [Figure 10] This is a diagram showing an example of response information generated by a large language model. [Figure 11] This is a diagram showing an example of a distributed email. [Figure 12] This is a diagram showing an example of an analysis result screen. [Figure 13] This is a diagram schematically showing analysis processing according to a modification example. [Figure 14] This is a diagram showing a setting screen according to a modification example. [Figure 15] This is a diagram showing an example of an instruction sentence reflecting context information. [Figure 16] This is a diagram showing an analysis result screen according to a modification example. [Figure 17] This is a diagram schematically showing a process for extracting new information. [Figure 18] This is a diagram showing an instruction sentence according to a modification example. [Figure 19] This is a diagram showing an example of an email including a comprehensive evaluation result. [Figure 20] This is a diagram for explaining a relevance evaluation function according to a modification example. [Figure 21] This is a diagram showing an example of an analysis result according to a modification example.

Modes for Carrying Out the Invention

[0019] 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 descriptions thereof will not be repeated. Note that each embodiment and each modification example described below may be selectively combined as appropriate.

[0020] <A. Information Processing System 10> First, referring to FIG. 1, the device configuration of the information processing system 10 will be described. FIG. 1 is a diagram showing an example of the device configuration of the information processing system 10.

[0021] As shown in FIG. 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 communicable with each other through a network NW (for example, the Internet).

[0022] The information processing device 100 is a notebook or desktop PC (Personal Computer), a tablet terminal, a smartphone, or other computer equipped with a communication function. The number of information processing devices 100 constituting the information processing system 10 may be one or two or more. The information processing device 100 is, for example, operated by company "A".

[0023] The user terminal 200 is, for example, a notebook or desktop PC, a tablet terminal, a smartphone, or other computer equipped with a communication function. The number of user terminals 200 constituting the information processing system 10 may be one or two or more. The user terminal 200 is, for example, owned by user "A" who is a general user.

[0024] The server 300 is a notebook or desktop PC (Personal Computer), a tablet terminal, a smartphone, or other computer equipped with a communication function. The number of servers 300 constituting the information processing system 10 may be one or two or more. The server 30 is, for example, operated by company "B".

[0025] Server 300 stores the Large Language Model 324. The Large Language Model 324 is a language model that has been trained on a massive amount of text data, exceeding several billion entries, and is trained to process various natural languages. The Large Language Model 324 is also known as an LLM (Large Language Model). The Large Language Model 324 is trained to receive an instruction as input and generate an output corresponding to that instruction.

[0026] Examples of large-scale language models 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 known LLMs. In addition to the GPT series, various other large-scale language models may be used, such as Transformer-based large-scale language models like BERT (Bidirectional Encoder Representations from Transformers), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and LSTM (Long Short-Term Memory).

[0027] Company "B" has, for example, published an API (Application Programming Interface) for utilizing the functions of the large-scale language model 324. This allows designers and general users of company "A" to utilize the functions of the large-scale language model 324 through this API.

[0028] Furthermore, the various processes described herein may be implemented in the information processing device 100, the user terminal 200, the server 300, or other computers.

[0029] In the above description, an example in which the information processing system 10 includes the server 300 has been described. However, the information processing system 10 may not include the server 300. In this case, the information processing system 10 is composed of one or more information processing devices 100 and one or more user terminals 200.

[0030] <B. Processing Overview> The information processing device 100 provides an SDI function using a large language model. The SDI function is a function for monitoring a patent database and analyzing newly published patent documents. The analysis results are distributed regularly. The SDI function is used to grasp technological trends or to monitor the trends of applications of competing companies.

[0031] Note that the "patent document" is a document published by the patent office of Japan or a foreign country. The language of the patent document may be Japanese or a foreign language such as English. Examples of patent documents include published patent gazettes, patent gazettes, published patent gazettes, re-published patents, and utility model gazettes. As an example, a patent document includes bibliographic information, a specification, claims, drawings, and an abstract. Examples of bibliographic information include an application number, a publication number, a patent registration number, an application date, a publication date, a registration date, an applicant, a patentee, an invention name, an agent, and an application country.

[0032] Hereinafter, referring to FIG. 2, an overview of the SDI function provided by the information processing device 100 according to the embodiment will be described. FIG. 2 is a diagram schematically showing the SDI function.

[0033] The information processing device 100 receives in advance a preset 123 regarding the analysis of newly published patent documents via the above-described user terminal 200. The preset 123 defines, for example, analysis conditions for newly published patent documents.

[0034] Furthermore, the information processing device 100 periodically monitors the patent database 124. The patent database 124 may be data stored in the information processing device 100, or it may be data stored on a server managed by the Japan Patent Office. Typically, the patent database 124 is stored in the information processing device 100. In this case, the information processing device 100 periodically monitors the Japan Patent Office's patent database and adds newly added patent documents to the patent database 124 within the information processing device 100.

[0035] The information processing device 100 retrieves the new patent documents 125 to be analyzed from the patent database 124 based on the arrival of a preset analysis time. The criteria for determining whether a patent document is new or not can be determined in various ways. For example, the information processing device 100 considers any patent document not included in the previous analysis results as a new patent document 125. As another example, the information processing device 100 considers any patent document whose publication date belongs to the current analysis cycle as a new patent document 125.

[0036] Next, the information processing device 100 generates an instruction statement 128 (first instruction statement) for analyzing the content of the newly received patent document 125 (first newly received patent document) according to the pre-configuration 123. At this time, the instruction statement 128 reflects the pre-configuration 123 and the newly received patent document 125 to be analyzed. Subsequently, the information processing device 100 inputs the generated instruction statement 128 into 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.

[0037] The information processing device 100 outputs analysis results 130 of the newly received patent document 125 based on the answers obtained from the large-scale language model 324. The destination of the output of the analysis results 130 is arbitrary. For example, the destination is the user terminal 200. The analysis results 130 output to the user terminal 200 are displayed on the user terminal 200's display, for example.

[0038] Also, the output mode of the analysis result 130 is arbitrary. As an example, the analysis result 130 is distributed to the user terminal 200 in the form of an email. As another example, the analysis result 130 is displayed on the user terminal 200 as a screen.

[0039] As described above, the information processing apparatus 100 provides an SDI function using a large language model. Thereby, the user can monitor the newly arrived patent documents 125 without setting a search formula.

[0040] Note that the user can specify the analysis method of the newly arrived patent documents 125 in the preset 123. As an example, the user can analyze the newly arrived patent documents 125 from the viewpoint of whether they are related to a predetermined technical idea.

[0041] <C. Usage example> By using the SDI function provided by the information processing apparatus 100, the user can analyze the newly arrived patent documents 125 in various analysis modes.

[0042] As an example of the analysis mode, the information processing apparatus 100 analyzes whether the newly published newly arrived patent documents 125 are related to a preset technical idea. In this case, the user sets a technical idea as a comparison criterion as the preset 123. When a newly arrived patent document 125 related to the set technical idea is published, the information processing apparatus 100 transmits the newly arrived patent document 125 to the user terminal 200. Thereby, the user can notice that a newly arrived patent document 125 related to the preset technical idea has been published.

[0043] As another example of the analysis mode, the information processing apparatus 100 analyzes the content of the newly published newly arrived patent documents 125 and generates a summary. In this case, the user sets an instruction to generate a summary of the newly arrived patent documents 125 as the preset 123. Thereby, the information processing apparatus 100 generates a summary of the newly published newly arrived patent documents 125 and periodically transmits the generated summary to the user terminal 200. As a result, the user can simplify the confirmation work of the newly arrived patent documents 125.

[0044] As yet another example of the analysis method, the information processing device 100 analyzes the classification to which the newly published patent document 125 belongs from among predefined classifications and assigns that classification to the newly published patent document 125. Typically, this classification is a user-defined classification and is different from the patent classification. In this case, the user sets an instruction to analyze the classification of the newly published patent document 125 as a pre-setting 123. As a result, the information processing device 100 assigns a classification to the newly published patent document 125. The user can then analyze the newly published patent document 125 based on the assigned classification. As a result, the user can reduce the time required to verify the newly published patent document 125.

[0045] The following describes the SDI function related to relevance evaluation, but the SDI function provided by the information processing device 100 is not limited to relevance evaluation.

[0046] Figure 3 is a schematic diagram illustrating the SDI function related to relevance evaluation. As shown in Figure 3, the user sets the technical information 123A, which will serve as the comparison standard, as a pre-configuration 123.

[0047] "Technical information" refers to data that includes technical ideas. Technical ideas are technical means for solving technical problems. Technical information, for example, is a description of technical means using text. Technical information also includes, for example, inventive information that describes the specific features of an invention.

[0048] Technical information 123A may be a string entered by user "A" on user terminal 200, or it may be data extracted from patent documents in response to user "A"'s operations. Technical information 123A may also be set by specifying an application number, publication number, etc.

[0049] Based on the arrival of a preset analysis time, the information processing device 100 acquires the newly arrived patent document 125 to be analyzed from the patent database 124. Then, the information processing device 100 performs a relevance evaluation between the preset technical information 123A and the newly arrived patent document 125.

[0050] The relevance evaluation between the technical information 123A and the newly arrived patent document 125 is realized by using the large language model 324. More specifically, first, the information processing device 100 designates the technical information 123A and the newly arrived patent document 125 for a predetermined instruction statement 128 defined to evaluate the relevance of two pieces of information. The instruction statement 128 designated with the technical information 123A and the newly arrived patent document 125 is input into the large language model 324. When receiving the input of the instruction statement 128, the large language model 324 generates an answer corresponding to the instruction statement 128. The generated answer is output to the information processing device 100.

[0051] Based on the answer obtained from the large language model 324, the information processing device 100 outputs an analysis result 130 indicating the relevance between the technical information 123A and the newly arrived patent document 125. The analysis result 130 includes an evaluation result indicating the relevance between the technical information 123A and the newly arrived patent document 125. The relevance may be represented by a numerical value indicating the degree of relevance or by a descriptive text. In the example of FIG. 3, the degree of relevance is represented by a binary value. Note that the degree of relevance may also be represented by three or more values.

[0052] <D. Hardware Configuration>

[0053] As described above, the information processing device 100 uses the large language model 324 for the relevance evaluation between the technical information 123A and the newly arrived patent document 125. Thereby, the user does not need to set a search formula and can monitor the newly arrived patent document 125. Next, referring to FIGS. 4 and 5, the hardware configurations of the information processing device 100 and the user terminal 200 shown in FIG. 1 above will be described in order.

[0054] The hardware configuration of the server 300 shown in Figure 1 is the same as that of the information processing device 100, so its explanation will not be repeated.

[0055] (D1. Information processing device 100) First, with reference to Figure 4, the hardware configuration of the information processing device 100 shown in Figure 1 will be explained. Figure 4 is a schematic diagram showing an example of the hardware configuration of the information processing device 100.

[0056] The information processing device 100 includes a control device 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 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.

[0057] The control device 101 is comprised of, for example, at least one integrated circuit. The integrated circuit may consist of, for example, at least one CPU (Central Processing Unit), at least one GPU (Graphics Processing Unit), at least one ASIC (Application Specific Integrated Circuit), at least one FPGA (Field Programmable Gate Array), or a combination thereof.

[0058] The control device 101 controls the operation of the information processing device 100 by executing various programs such as the analysis program 122 and the operating system. Based on the receipt of execution commands for various programs, the control device 101 reads the program from the auxiliary storage device 120 or ROM 102 into the RAM 103. The RAM 103 functions as working memory and temporarily stores various data necessary for the execution of various programs.

[0059] The communication interface 104 is connected to a LAN (Local Area Network), an antenna, and other devices. The information processing device 100 exchanges data with external devices via the communication interface 104. These external devices include, for example, a user terminal 200, a server 300, and other communication devices.

[0060] A display 106 is connected to the display interface 105. The display interface 105 sends image signals to the display 106 for displaying images, in accordance with commands 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 it may be configured separately from the information processing device 100.

[0061] An input device 108 is connected to the input interface 107. The input device 108 is, for example, a mouse, keyboard, touch panel, or other device capable of receiving user input. The input device 108 may be configured integrally with the information processing device 100, or it may be configured separately from the information processing device 100.

[0062] The auxiliary storage device 120 is, for example, a hard disk, flash memory, SSD (Solid State Drive), or other storage medium. The auxiliary storage device 120 stores the analysis program 122, the aforementioned pre-configuration 123, the aforementioned patent database 124, and the aforementioned instruction statements 128, etc. The storage location of the analysis program 122, the pre-configuration 123, the patent database 124, and the instruction statements 128 is not limited to the auxiliary storage device 120, but may also be stored in the storage area of ​​the control device 101 (for example, cache memory, etc.), ROM 102, RAM 103, external devices, etc.

[0063] Furthermore, the analysis program 122 may be provided not as a standalone program, but incorporated as part of any other program. In this case, the various processes defined in the analysis program 122 are realized in cooperation with any other program, such as the analysis program 222 described later. Even a program that does not include such modules does not deviate from the intent of the analysis program 122 according to this embodiment. Moreover, 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 a form similar to a so-called cloud service, where at least one server executes a portion of the processing of the analysis program 122.

[0064] (D2. User terminal 200) Next, with reference to Figure 5, the hardware configuration of the user terminal 200 shown in Figure 1 will be described. Figure 5 is a schematic diagram showing an example of the hardware configuration of the user terminal 200.

[0065] The user terminal 200 includes a control unit 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.

[0066] The control device 201 is comprised of, for example, at least one integrated circuit. The integrated circuit may consist of, for example, at least one CPU, at least one GPU, at least one ASIC, at least one FPGA, or a combination thereof.

[0067] The control device 201 controls the operation of the user terminal 200 by executing various programs such as the analysis program 222 and the operating system. Based on the receipt of execution commands for various programs, the control device 201 reads the program from the auxiliary storage device 220 or ROM 202 into the RAM 203. The RAM 203 functions as working memory and temporarily stores various data necessary for the execution of the program.

[0068] The communication interface 204 is connected to a LAN, an antenna, and the like. The user terminal 200 exchanges data with external devices via the communication interface 204. These external devices include, for example, an information processing device 100, a server 300, and other communication devices. The user terminal 200 may be configured to download an analysis program 222 from the information processing device 100.

[0069] A display 206 is connected to the display interface 205. The display interface 205 sends image signals to the display 206 for displaying images, in accordance with commands 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 it may be configured separately from the user terminal 200.

[0070] An input device 208 is connected to the input interface 207. The input device 208 may be, for example, a mouse, keyboard, touch panel, or other device capable of receiving user input. The input device 208 may be configured integrally with the user terminal 200 or separately from the user terminal 200.

[0071] The auxiliary storage device 220 is, for example, a hard disk, a flash memory, an SSD (Solid State Drive), and other storage media. The auxiliary storage device 220 stores an analysis program 222 and the like. The storage location of the analysis program 222 is not limited to the auxiliary storage device 220 and may be stored in the storage area of the control device 201 (for example, cache memory, etc.), ROM 202, RAM 203, an external device (for example, a server), etc.

[0072] Note that the analysis program 222 may be provided not as a single program but incorporated into a 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 such a part of the module does not deviate from the gist of the analysis program 222 according to the present embodiment. Further, part or all of the functions provided by the analysis program 222 may be realized by dedicated hardware. Further, the user terminal 200 may be configured in a form such as a so-called cloud service in which at least one server executes a part of the processing of the analysis program 222.

[0073] <E. Data Flow Related to Preset> Next, referring to FIGS. 6 and 7, the preset performed before using the SDI function will be described. FIG. 6 is a diagram showing an example of the data flow when performing the preset.

[0074] In step S110, based on receiving a preset setting operation, the user terminal 200 displays a setting screen 400A shown in FIG. 7. FIG. 7 is a diagram showing an example of the setting screen 400A.

[0075] The setting screen 400A is configured to receive a preset for using the SDI function. Various information input to the setting screen 400A is stored in the information processing device 100 as the above-described preset 123.

[0076] The settings screen 400A includes, for example, setting fields 410, 412, 414, 416, 418, and 420, a cancel button 430, and a save button 432.

[0077] The settings field 410 is configured to accept settings for specifying the newly received patent documents to be analyzed. For example, the settings field 410 includes radio buttons for specifying published patents as the target of analysis and radio buttons for specifying registered patents as the target of analysis. The user can select only one of the radio buttons or both radio buttons.

[0078] The settings field 412 is configured to accept settings for the analysis frequency of newly received patent documents. For example, the user can set the analysis frequency to either weekly or monthly in the settings field 412. However, the analysis frequency is not limited to weekly or monthly and may be set arbitrarily. For example, the analysis frequency may be specified by a period such as a quarter, six months, or year, or by a day of the week or date. As another example, the analysis frequency may be set immediately when the database is updated.

[0079] The settings field 414 is configured to accept various settings related to the analysis of newly submitted patent documents. For example, the settings field 414 is configured to accept input for a setting name, an instruction, the type of large-scale language model to use, the analysis location of the newly submitted patent documents, the maximum number of newly submitted patent documents to add, and the filtering conditions for the analysis results 130.

[0080] The settings field 414 includes, for example, a setting field 416 for instruction texts and a setting field 418 for filtering conditions.

[0081] The settings field 416 is configured to accept input of instruction sentences to be entered into the large-scale language model 324. In the settings field 416, the user can enter the aforementioned technical information 123A, which will serve as a comparison standard. In the example in Figure 7, the technical concept, "An invention relating to the evaluation of the relevance of patent documents using a large-scale language model," is entered as text in the settings field 416.

[0082] Furthermore, the user can specify the output format of the analysis results 130 in the settings section 416. In the example in Figure 7, the user has specified that the degree of relevance between technical information 123A and new patent document 125 be output as "0" or "1". A degree of relevance of "0" indicates no relevance. A degree of relevance of "1" indicates a relevance. In addition, the user has specified in the settings section 416 that the reason for the evaluation be output.

[0083] Furthermore, various criteria can be set for determining the degree of relevance through the instructions. For example, the instructions may be set so that a relevance is determined if a specified technical term is included, and a non-relevance is determined if that term is not included. As another example, the instructions may be set so that a relevance is determined if an element is included within a specified numerical range, and a non-relevance is determined if it is outside that range or if no numerical value is provided.

[0084] The setting field 418 is configured to accept input of filtering conditions. These filtering conditions are used to narrow down the output of analysis results 130. When filtering conditions are set in the setting field 418, the information processing device 100 will output only analysis results 130 that satisfy those filtering conditions.

[0085] In the setting column 418, various filtering conditions can be set. As an example, the user can set filtering conditions so that only the analysis results 130 containing specific terms are output. As another example, the user can set filtering conditions so that only the analysis results 130 whose evaluation results are specific results are output. The user can arbitrarily increase or decrease the filtering conditions in the setting column 418.

[0086] The setting column 420 is configured to receive settings related to the method of receiving the analysis results 130. As an example, the user can set in the setting column 420 whether to receive the analysis results 130 by email.

[0087] When the cancel button 430 is pressed, the user terminal 200 discards the information input on the setting screen 400A and closes the setting screen 400A.

[0088] On the other hand, when the save button 432 is pressed, the user terminal 200 transmits the information input on the setting screen 400A to the information processing apparatus 100 as the preset 123.

[0089] Referring to FIG. 6 again, in step S112, the information processing apparatus 100 saves the information received from the user terminal 200 as the preset 123. The preset 123 is stored, for example, in the auxiliary storage device 120 of the information processing apparatus 100.

[0090] <F. Data flow related to analysis processing> Next, referring to FIGS. 8 to 12, the operation of the information processing system 10 during the analysis of the incoming patent document 125 will be described. FIG. 8 is a diagram showing an example of the data flow when the analysis process of the incoming patent document 125 is executed.

[0091] (F1. Step S120) In step S120, it is assumed that the analysis timing has arrived according to the analysis frequency set in the setting field 412 (see Figure 7) described above. Based on this, the information processing device 100 retrieves the newly arrived patent document 125 from the patent database 124 described above.

[0092] Preferably, the information processing device 100 extracts only newly received patent documents 125 that satisfy the filtering conditions set in the setting screen 400A (see Figure 7) as targets for analysis. As an example, the information processing device 100 extracts newly received patent documents 125 from at least one of the published and registered publications as targets for analysis, according to the filtering conditions set in the setting field 410 (see Figure 7) mentioned above.

[0093] The filtering criteria for narrowing down the analysis target are not limited to those mentioned above. Other examples of such filtering criteria include patent classifications such as IPC, keywords, and publication dates.

[0094] In Japan, approximately several thousand publicly available patent applications are published each week, but the information processing device 100 can narrow down the target of analysis using pre-set filtering conditions. As a result, the cost of using the large-scale language model 324 can be reduced.

[0095] (F2. Step S122) In step S122, the information processing device 100 generates an instruction statement 128 for analyzing the contents of newly received patent documents according to the pre-configuration 123. The instruction statement 128 reflects the instructions entered in the setting field 416 (see Figure 7) and designates the newly received patent documents extracted in step S120 as the target of analysis. As a result, the information processing device 100 generates an instruction statement 128 for evaluating the relationship between the technical ideas entered in the setting field 416 and the newly received patent documents extracted in step S120.

[0096] Figure 9 shows an example of the instruction statement 128 generated in step S122. As shown in Figure 9, the instruction statement 128 includes an instruction 132 for performing a relevance evaluation based on the technical idea entered in the setting field 416 described above, and an instruction 134 for specifying the new patent document 125 to be analyzed.

[0097] The information processing device 100 sends the instruction statement 128 generated in step S122 to the server 300.

[0098] (E3. Step S130) Next, in step S130, the server 300 inputs the instruction 128 to the large-scale language model 324 based on the fact that it has received the instruction 128 from the information processing device 100. As a result, the large-scale language model 324 generates a response corresponding to the instruction 128.

[0099] Figure 10 shows an example of response information 326 generated by the large-scale language model 324. In the example in Figure 10, the response information 326 is shown in tabular format, but the format of the response information 326 is arbitrary. The output format of the large-scale language model 324 is predetermined, for example, in instruction 128, and the large-scale language model 324 outputs the response information 326 according to the output format specified in instruction 128.

[0100] The response information 326 includes the analysis results 327 of the relationship between technical information 123A and new patent document 125, separately for new patent document 125. The analysis results 327 include the degree of relationship between technical information 123A and new patent document 125, and the reasons for evaluating that degree of relationship.

[0101] Analysis result 327 can be uniquely identified by the identifier of new patent document 125. This identifier is defined, for example, by the application number of new patent document 125, the publication number of new patent document 125, the registered publication number of new patent document 125, or the applicant information of new patent document 125.

[0102] The information processing device 100 transmits the response information 326 generated in step S130 to the information processing device 100.

[0103] (E4. Steps S132, S140) Next, in step S132, the information processing device 100 generates an email showing the analysis results 130 of the new patent document 125 based on the response information 326 received from the server 300.

[0104] In step S140, the information processing device 100 delivers the email generated in step S132 to the user terminal 200. The user terminal 200 notifies that the email has been delivered. In this way, the information processing device 100 delivers the analysis results of newly published patent documents to the user terminal 200. By checking email 400B, the user can determine whether or not newly published patent documents related to the technical information 123A that they have set have been published.

[0105] Figure 11 shows an example of email 400B delivered in step S140. Email 400B includes information relating to newly received patent documents, the analysis results relating to said newly received patent documents, and the reasons for the analysis.

[0106] In the example in Figure 11, the analysis results show the degree of relevance between technical information 123A and new patent document 125 as either "0" or "1". A degree of relevance of "0" indicates that there is no relationship between technical information 123A and new patent document 125. A degree of relevance of "1" indicates that there is a relationship between technical information 123A and new patent document 125. By displaying an analysis result of "0" for relevance, users can clearly understand that although the search keywords or patent classifications yielded results, there is no relevance.

[0107] Furthermore, email 400B includes message 440 to which a URL (Uniform Resource Locator) is linked. When the user clicks message 440, the user terminal 200 sends a request to the information processing device 100 to display a screen showing the details of the analysis results 130.

[0108] The means of delivering the analysis results 130 are not limited to email; they may be delivered by other means. For example, the analysis results 130 may be delivered using SMS (Short Message Service). Another example is that the analysis results 130 may be delivered using a chat tool. Yet another example is that the analysis results 130 may be delivered via the analysis results screen 400C (see Figure 12) described later.

[0109] (E5. Steps S142, S150) Next, in step S142, the information processing device 100 generates an analysis results screen based on the response information 326 (see Figure 10) received from the server 300. This analysis results screen is written in a language such as HTML (HyperText Markup Language).

[0110] Next, in step S150, the user terminal 200 displays the analysis results screen generated by the information processing device 100. Figure 12 shows an example of the analysis results screen 400C. The analysis results screen 400C is displayed, for example, on the display 206 of the user terminal 200.

[0111] The analysis results screen 400C includes a display area 450 that displays information related to the pre-configuration 123, and a display area 452 that displays the analysis results 130.

[0112] Display field 450 displays the information set in the settings screen 400A (see Figure 7) described above. As an example, display field 450 displays "ID" (Identification), "Analysis Type", "Model", "Status", "Number of Items Acquired", "Acquisition Time", "Analysis Target", and "User Instructions".

[0113] "ID" is an identifier used to uniquely identify the analysis results. "Analysis Type" displays the type of analysis, such as relevance evaluation. "Model" indicates the type of large-scale language model 324 used during relevance evaluation. "Number of Items Retrieved" indicates the number of newly received patent documents 125 that were analyzed. "Status" indicates whether the relevance evaluation process was completed successfully or not. "Retrieval Time" indicates the time taken from the start to the end of the relevance evaluation process. "Analysis Target" indicates the section of the newly received patent document 125 that was analyzed. "User Instructions" displays the instructions entered in the setting field 416 (see Figure 7) mentioned above.

[0114] Display field 452 of analysis result 130 includes display fields 454 and 456.

[0115] Display field 454 displays information related to the newly received patent document 125 that was subject to relevance evaluation. Display field 454 displays, for example, the "Title of Invention," "Abstract," "Problem," "Solution," "Selected Figure," "Application Number," "Publication Number," and "Registration Number."

[0116] Display area 456 displays the analysis results generated from the response information 326 (see Figure 10) of the large-scale language model 324. As an example, the analysis results include the degree of relevance between technical information 123A and new patent document 125, and the reason for the evaluation that determined the degree of relevance. In the example in Figure 12, the degree of relevance is shown as "0" or "1". A degree of relevance of "0" indicates that there is no relationship between technical information 123A and new patent document 125. A degree of relevance of "1" indicates that there is a relationship between technical information 123A and new patent document 125. By displaying an analysis result of a degree of relevance of "0", the user can clearly understand that although the search keywords or patent classifications were hit, there is no relevance.

[0117] Note that the analysis result 130 may be displayed for all of the newly analyzed patent documents 125, or only those that meet the filtering criteria may be displayed. As an example, the filtering criteria for narrowing down the output targets are set in advance on the setting screen 400A (see FIG. 7). According to the filtering criteria, for example, only the newly arrived patent documents 125 that are relevant to the technical information 123A are displayed on the analysis result screen 400C. As a result, only the analysis results 130 that the user is interested in are displayed. As a result, the user can efficiently analyze the newly arrived patent documents 125.

[0118] Preferably, the analysis result screen 400C may be configured to display the original of the patent document selected by the user. As a result, the user can easily check the original of the patent document. Further, when the original of the patent document is displayed, the analysis results related to the patent document may be displayed side by side.

[0119] <G. Others> Next, referring to FIGS. 13 to 16, another example of the above embodiment will be described.

[0120] In the above-described embodiment, when analyzing the newly arrived patent documents 125, the past analysis tendency is not utilized. However, the information processing apparatus 100 may analyze the newly arrived patent documents 125 by using the past analysis tendency. The past analysis tendency is also called context information. The information processing apparatus 100 according to this example can analyze the newly arrived patent documents 125 based on the same criteria as the past analysis results by using the past analysis results 130 as context information. As a result, the user can obtain analysis results with unified judgment criteria. As a result, the analysis accuracy of the newly arrived patent documents 125 is improved.

[0121] Hereinafter, referring to FIG. 13, the analysis process of the newly arrived patent documents using the context information will be described. FIG. 13 is a diagram schematically showing the analysis process.

[0122] In step S1, assume that the predetermined analysis time has arrived. Based on this, the information processing device 100 performs the Nth (where N is a natural number) patent analysis.

[0123] More specifically, the information processing device 100 retrieves the new patent document 125A to be analyzed from the patent database 124. Then, the information processing device 100 specifies the technical information 123A and the new patent document 125A in a predetermined instruction statement 128A for evaluating the relationship between the two pieces of information. Subsequently, the instruction statement 128A specifying the technical information 123A and the new patent document 125A is input to the large-scale language model 324. Upon receiving the instruction statement 128A, the large-scale language model 324 generates a response corresponding to the instruction statement 128A. The generated response is output to the information processing device 100.

[0124] Next, the information processing device 100 outputs an analysis result 130A showing the relationship between the technical information 123A and the newly received patent document 125A, based on the response obtained from the large-scale language model 324. The analysis result 130A includes an evaluation result showing the relationship between the technical information 123A and the newly received patent document 125A. This relationship may be expressed as a numerical value indicating the degree of relevance, or as an explanatory text.

[0125] In step S2, assume that the next analysis time has arrived. Based on this, the information processing device 100 performs the N+1th (where N is a natural number) patent analysis.

[0126] More specifically, the information processing device 100 retrieves the newly arrived patent document 125B (second newly arrived patent document), which was added after the newly arrived patent document 125A, from the patent database 124. Then, the information processing device 100 specifies the technical information 123A and the newly arrived patent document 125B to a predetermined instruction statement 128B (second instruction statement) for evaluating the relationship between the two pieces of information. At this time, the information processing device 100 includes contextual information, including some or all of the past analysis results 130A, in the instruction statement 128B. The instruction statement 128B includes instructions for evaluating the relationship between the technical information 123A and the newly arrived patent document 125B based on the past analysis results 130A.

[0127] Subsequently, the information processing device 100 inputs the instruction sentence 128B into the large-scale language model 324. Upon receiving the instruction sentence 128B, the large-scale language model 324 generates a response corresponding to the instruction sentence 128B. The generated response is output to the information processing device 100.

[0128] Subsequently, the information processing device 100 outputs an analysis result 130B showing the relationship between the technical information 123A and the newly received patent document 125B, based on the response obtained from the large-scale language model 324. The analysis result 130B includes an evaluation result showing the relationship between the technical information 123A and the newly received patent document 125B. This relationship may be expressed as a numerical value indicating the degree of relevance, or as an explanatory text.

[0129] Whether or not to use contextual information in the analysis is predetermined in the settings screen 400D shown in Figure 14. Figure 14 shows the settings screen 400D according to a modified example.

[0130] The settings screen 400D shown in Figure 14 differs from the settings screen 400A shown in Figure 7 above in that it has a setting field 415 related to context information. Other points are as described above, so those explanations will not be repeated.

[0131] The settings field 415 has a checkbox that accepts whether or not to use past analysis results 130A as contextual information. If this checkbox is selected, the information processing device 100 uses the past analysis results 130A to analyze the new patent document 125B.

[0132] Figure 15 shows an example of instruction 128B that reflects contextual information. As shown in Figure 15, instruction 128B includes contextual information 460, 462 and instruction 464.

[0133] Context information 460 indicates the instruction sentences input to the large-scale language model 324 when outputting past analysis results 130A. Context information 462 shows a list of past analysis results 130A. Instruction 464 also specifies instructions to analyze the relationship between the newly received patent document 125B and technical information 123A using context information 460 and 462.

[0134] Furthermore, if the amount of past analysis results 130A to be included in the context information increases, the cost of using the large-scale language model 324 will increase. Therefore, preferably, the setting field 415 is configured to accept settings for narrowing down the past analysis results 130A to be included in the context information.

[0135] As an example, the setting may include a setting to prioritize the inclusion of newer analysis results 130A in the context information. As another example, the setting may include a setting to specify the data size of the analysis results 130A to be included in the context information. As yet another example, the setting may include a setting to randomly select the analysis results 130A to be included in the context information based on user evaluations of past analysis results 130A. The context information may also include the reasons for the user evaluations.

[0136] An example of extracting the analysis result 130A to be included in the context information according to the user evaluation will be further described. In this case, the information processing apparatus 100 preliminarily accepts a user evaluation regarding the analysis result 130A for the latest patent document 125A, and extracts the analysis result 130A to be included in the context information based on the user evaluation.

[0137] The user evaluation for the analysis result 130A is, for example, preliminarily accepted on the analysis result screen 400E shown in FIG. 16. FIG. 16 is a diagram showing the analysis result screen 400E according to a modified example.

[0138] The analysis result screen 400E shown in FIG. 16 is different from the analysis result screen 400C shown in FIG. 12 described above in that it has an input field 458 for accepting user evaluation. Since the other points are as described above, the descriptions thereof will not be repeated.

[0139] The input field 458 accepts a user evaluation for the past analysis result 130A. The user evaluation includes, for example, "○" indicating that the analysis result by the large language model 324 is correct, and "×" indicating that the analysis result by the large language model 324 is incorrect. The user evaluation input to the input field 458 is stored in the information processing apparatus 100 after being associated with each of the latest patent documents 125A.

[0140] When analyzing the current latest patent document 125B, the information processing apparatus 100 extracts the latest patent document 125A that satisfies the condition for which the user evaluation is preset from the past latest patent documents 125A. As an example, the information processing apparatus 100 extracts the latest patent document 125A with a user evaluation of "○". Then, the information processing apparatus 100 includes the analysis result 130A related to the extracted latest patent document 125A in the context information. As a result, only the analysis result 130A that matches the user evaluation is included in the context information. As a result, the analysis accuracy for the subsequent latest patent document 125B is improved.

[0141] <H. Others> Next, with reference to Figure 17, yet another example of the above embodiment will be described.

[0142] In the above example, the information processing device 100 used past analysis results 130A as contextual information to analyze the newly received patent document 125B. In contrast, the information processing device 100 according to this example uses a large-scale language model 324 to extract new information that was not present in the past patent document 125A but newly appeared in the current patent document 125B.

[0143] Examples of such new information include new technical terms, new challenges, new applicants, and patent classifications. Patent classifications include IPC (International Patent Classification), FI (File Index), and F-terms (File Forming Term).

[0144] The extracted new information can be used for various purposes. For example, the extracted new information can be used as search criteria for newly published patent documents. This new information may be automatically reflected in the search criteria or manually. By using the new information in the search criteria, the setting of search criteria is simplified. Furthermore, by reviewing the extracted new information, users can identify issues that have not appeared in previous analysis results by comparing them. In addition, because users can review the extracted new information, they can identify rapidly increasing technological fields and patent classifications.

[0145] A sudden influx of new information may be highlighted more than other new information. The method of highlighting is optional. For example, a sudden influx of new information can be highlighted by displaying it in a specific color. Another example is highlighting a sudden influx of new information by adding a line break. Yet another example is highlighting a sudden influx of new information by enclosing it in quotation marks. Yet another example is highlighting a sudden influx of new information by underlining it. Yet another example is highlighting a sudden influx of new information by making it bold. Yet another example is highlighting a sudden influx of new information by adding a callout.

[0146] Figure 17 is a schematic diagram illustrating the process for extracting new information. As described above, the newly acquired patent document 125A is the data used when outputting the Nth analysis result 130A. The newly acquired patent document 125B is the data used when outputting the N+1th analysis result 130B.

[0147] The information processing device 100 generates an instruction statement 128C (third instruction statement) for extracting new information that has newly appeared in the new patent document 125B, based on the previously received new patent document 125A. The instruction statement 128C includes, for example, an instruction for extracting new technical words related to the technical field of the large-scale language model. The information processing device 100 outputs new information 131 obtained from the large-scale language model 324 by inputting the instruction statement 128C into the large-scale language model 324.

[0148] The destination for outputting the new information 131 is arbitrary. For example, the destination is the user terminal 200. The new information 131 output to the user terminal 200 will be displayed on the user terminal 200's screen, for example. The output method of the new information 131 is also arbitrary. For example, the new information 131 will be delivered to the user terminal 200 in email format. As another example, the new information 131 will be displayed on the user terminal 200 in screen format.

[0149] The user can select any information from the displayed new information 131. The information processing apparatus 100 uses the information selected by the user during the next analysis of the latest patent documents 125.

[0150] <I. Others> Next, referring to FIGS. 18 and 19, still other examples of the above embodiment will be described.

[0151] In the above-described embodiment, the information processing apparatus 100 analyzed each of the latest patent documents 125 individually. In contrast, the information processing apparatus 100 may analyze each of the latest patent documents 125 comprehensively.

[0152] FIG. 18 is a diagram showing an instruction text 128D according to a modified example. The instruction text 128D shown in FIG. 18 is different from the instruction text 128 described in FIG. 9 above in that it further includes an instruction 133.

[0153] More specifically, the instruction text 128D includes not only an instruction 132 for individually analyzing each of the latest patent documents 125 according to the preset 123, but also an instruction 133 for comprehensively analyzing each of the latest patent documents 125 according to the preset 123. The information processing apparatus 100 inputs the instruction text 128D into the large language model 324 to obtain individual analysis results of the latest patent documents 125 and comprehensive analysis results of the latest patent documents 125.

[0154] The comprehensive analysis result is distributed, for example, as the email 400B shown in FIG. 19. FIG. 19 is a diagram showing an example of the email 400B including the comprehensive evaluation result. The user can grasp new applicants, technological trends, etc. by checking the email 400B.

[0155] Preferably, the comprehensive analysis result is displayed above the individual analysis results of the latest patent documents 125. Thereby, the user can first check the overall evaluation of the current analysis results and can easily select whether the current analysis results are important.

[0156] In the above description, an example where the comprehensive analysis result is included in the mail 400B has been described. However, the comprehensive analysis result may be displayed on the above-described analysis result screen 400C (see FIG. 12).

[0157] <J. Others> Next, referring to FIGS. 20 and 21, still other examples of the above embodiment will be described.

[0158] In the above-described embodiment, the information processing apparatus 100 evaluated the relevance between the technical information 123A and the newly arrived patent document 125. At this time, the information processing apparatus 100 may evaluate the relevance between the technical information 123A and the newly arrived patent document 125 from a specific perspective.

[0159] FIG. 20 is a diagram for explaining the relevance evaluation function according to this example. As shown in FIG. 20, the information processing apparatus 100 designates the above-described technical information 123A and the above-described newly arrived patent document 125 for a predetermined instruction statement 128T for evaluating the relevance between two pieces of information from a specific perspective. The instruction statement 128T is, for example, registered in advance in the information processing apparatus 100 as a template.

[0160] More specifically, the instruction statement 128T includes an argument part 129A and an argument part 129B. The technical information 123A is designated in the argument part 129A. On the other hand, the newly arrived patent document 125 is designated in the argument part 129B.

[0161] Also, the instruction statement 128T stipulates from what perspective the relevance between the technical information 123A and the newly arrived patent document 125 is to be evaluated. In the example of FIG. 20, the instruction statement 128T is stipulated to evaluate the two pieces of information for each of a plurality of evaluation perspectives "α", "β", ···. Each evaluation perspective may be stipulated in advance or may be stipulated as an argument. When each evaluation perspective is stipulated as an argument, the evaluation perspective is designated in advance on the above-described setting screen 400A. Note that the number of evaluation perspectives stipulated in the instruction statement 128T may be one or more.

[0162] The evaluation criteria include at least one of the following: commonality in the technical field, commonality in the problem, commonality in the function, commonality in the content suggestion, commonality in the parameters, commonality in the numerical limitations, and commonality in the technical terminology. This allows the commonality between technical information 123A and newly received patent document 125 to be evaluated using an evaluation axis that aligns with the criteria for determining inventive step.

[0163] As another example, the evaluation criteria may be criteria suitable for application exploration. These evaluation criteria may be criteria for commonality of applicable applications or products, or criteria for commonality of applicable industrial fields. Yet another example is that the evaluation criteria may be criteria for commonality of drawings.

[0164] Instruction 128, which specifies technical information 123A and new patent document 125, is input to the large-scale language model 324. Upon receiving instruction 128, the large-scale language model 324 generates a response corresponding to instruction 128. The generated response is output to the information processing device 100.

[0165] The information processing device 100 outputs an analysis result 130C that shows the relationship between the technical information 123A and the newly received patent document 125 for each evaluation criterion, based on the response obtained from the large-scale language model 324. This relationship may be expressed as a numerical value indicating the degree of relevance, or as an explanatory text.

[0166] Figure 21 shows an example of the analysis result 130C in this example. In the example in Figure 21, the analysis result 130C is shown in tabular format, but the format of the analysis result 130C is arbitrary. The output format of the large-scale language model 324 is predetermined, for example, in instruction 128, and the large-scale language model 324 outputs the analysis result 130C according to the output format specified in instruction 128.

[0167] Analysis result 130C includes the degree of relevance between technical information 123A and newly received patent document 125, and the reasons for evaluating that degree of relevance. The degree of relevance is included for each evaluation criterion indicated in instruction 128. Similarly, the reasons for evaluation are included for each evaluation criterion indicated in instruction 128. Furthermore, analysis result 130C includes the overall evaluation score for each evaluation criterion and the reasons for that overall evaluation.

[0168] The output destination of the analysis results 130C from the information processing device 100 is arbitrary. For example, the output destination is the user terminal 200. The analysis results 130C output to the user terminal 200 are displayed on the user terminal 200's display, for example.

[0169] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of the present invention is indicated by the claims rather than by the foregoing description, and all modifications within the meaning and scope equivalent to the claims are intended to be included. [Explanation of symbols]

[0170] 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 Pre-configuration, 123A Technical information, 124 Patent database, 125 New patent documents, 125A New patent documents, 125B New patent documents, 128 Instructions, 128A Instructions, 128B Instructions, 128C Instructions, 128D Instructions, 128T Instructions, 129A Argument section, 129B Argument section, 130 Analysis results, 130A Analysis results, 130B Analysis results, 130C Analysis results, 131 New information, 132 Instructions, 133 Instructions, 134 Instructions, 200 User terminal, 201 Control unit, 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 language model, 326 Response information, 327 Analysis results, 400A Settings screen, 400B Email, 400C Analysis results screen, 400D Settings screen, 400E Analysis results screen, 410 Settings field, 412 Settings field, 414 Settings field, 415 Settings field, 416 Settings field, 418 Settings field, 420 Settings field, 430 Cancel button, 432 Save button, 440 Message, 450 Display field, 452 Display field, 454 Display field, 456 Display field, 458 Input field, 460 Context information, 462 Context information, 464 Instructions.

Claims

1. A patent analysis program, The aforementioned analysis program is programmed into a computer. A process for receiving input of pre-configured settings regarding the analysis of patent documents, The process of obtaining the first newly arrived patent document from the patent database, A process to generate analysis results regarding the first newly filed patent document based on the results obtained from a large-scale language model by inputting a first instruction sentence for analyzing the content of the first newly filed patent document in accordance with the aforementioned pre-settings into a large-scale language model, An analysis program that performs the process of outputting the aforementioned analysis results.

2. The aforementioned pre-configuration includes technical information representing the technical concept, The first instruction includes instructions for evaluating the relationship between the technical information and the first newly filed patent document, The analysis program according to claim 1, wherein the analysis results include analysis results showing the relationship between the technical information and the first newly filed patent document.

3. The analysis program according to claim 1 or 2, wherein the output process includes a process for delivering the analysis results to a user terminal.

4. The analysis program further causes the computer to perform a process to accept input of filtering conditions for narrowing down the output target, The analysis program according to claim 1 or 2, wherein the output process outputs the analysis results for the first newly acquired patent document that satisfies the filtering conditions.

5. The analysis program further provides the computer with: A process for obtaining a second newly added patent document that was added to the patent database after the first newly added patent document, A process for generating context information that includes part or all of the analysis results relating to the first newly acquired patent document, A process to generate analysis results regarding the second newly filed patent document based on the results obtained from the large-scale language model by inputting a second instruction sentence for analyzing the content of the second newly filed patent document into the large-scale language model, The process of outputting the analysis results relating to the second newly acquired patent document is executed. The analysis program according to claim 4, wherein the second instruction includes the context information.

6. The analysis program further provides the computer with: The process is executed to receive user evaluations regarding the analysis results for the first newly acquired patent document. The process for generating the aforementioned context information is: A process to extract from the first newly acquired patent documents obtained in the aforementioned acquisition process the first patent documents whose user evaluation satisfies the conditions set in advance, The analysis program according to claim 5, further comprising the process of adding the analysis results related to the extracted first patent document to the context information.

7. The first instruction above is, Instructions for individually analyzing each of the first newly acquired patent documents according to the aforementioned pre-configurations, The analysis program according to claim 1 or 2, further comprising instructions for causing each of the first newly acquired patent documents to be comprehensively analyzed according to the aforementioned pre-configurations.

8. The aforementioned pre-configuration executes a process that accepts input of filtering conditions to narrow down the target of analysis. The analysis program further provides the computer with: From among the first newly acquired patent documents obtained in the aforementioned acquisition process, a process is executed to extract the first patent documents that satisfy the filtering conditions. The analysis program according to claim 1 or 2, wherein the first instruction specifies the extracted first patent document as the object of analysis.

9. The analysis program further provides the computer with: The process involves inputting a third instruction sentence into the large-scale language model to extract new information that is not present in the first new patent document but newly appears in the second new patent document, The analysis program according to claim 5, which performs a process of outputting the new information obtained from the large-scale language model by inputting the third instruction sentence.

10. An information processing device capable of analyzing patent documents, Equipped with a control unit, The control unit, A process for receiving input of pre-configured settings regarding the analysis of patent documents, The process of obtaining the first newly arrived patent document from the patent database, A process to generate analysis results regarding the first newly filed patent document based on the results obtained from a large-scale language model by inputting a first instruction sentence for analyzing the content of the first newly filed patent document in accordance with the aforementioned pre-settings into a large-scale language model, An information processing device that performs the process of outputting the aforementioned analysis results.

11. A method for analyzing patent documents, A step to accept input of pre-configured settings regarding the analysis of patent documents, Steps include obtaining the first newly arrived patent document from the patent database, The steps include: inputting a first instruction sentence for analyzing the content of the first newly filed patent document in accordance with the aforementioned pre-settings into a large-scale language model, and generating analysis results regarding the first newly filed patent document based on the results obtained from the large-scale language model; An analysis method comprising the step of outputting the aforementioned analysis results.

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