system

Abstract

The system according to the embodiment aims to improve the efficiency of work related to patent applications. [Solution] A system according to an embodiment includes a reception unit, a search unit, a creation unit, and a support unit. The reception unit receives technical information from a user. The search unit searches for prior art based on the technical information received by the reception unit. The creation unit creates patent application documents based on the prior art information searched by the search unit. The support unit checks the patent application documents created by the creation unit and makes suggested revisions.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the work involved in patent applications was highly dependent on the individual, resulting in a significant amount of work time.

[0005] The system according to the embodiment aims to improve the efficiency of work related to patent applications. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, a creation unit, and a support unit. The reception unit receives technical information from a user. The search unit searches for prior art based on the technical information received by the reception unit. The creation unit creates patent application documents based on the prior art information searched by the search unit. The support unit checks the patent application documents created by the creation unit and makes suggested revisions. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of work related to patent applications. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A patent application support system according to an embodiment of the present invention accepts technical information from users, uses a generation AI to search for prior art, prepares patent application documents, and proposes revisions. The patent application support system accepts technical information from users, and the generation AI automatically searches for related prior art and provides the search results. For example, if a user wishes to apply for a patent for a new product, the generation AI searches for and lists existing patents and technical literature related to that product. Next, the patent application support system automatically prepares patent application documents based on the user's input of information necessary for the patent application. For example, when a user inputs details of the invention and drawings, the generation AI generates patent application documents based on that information. Furthermore, the generation AI checks the contents of the documents and suggests necessary revisions and additional information. This patent application support system is expected to improve the efficiency of patent application procedures and reduce work hours. In particular, automating the prior art search and document preparation processes reduces the need for individualized tasks and speeds up patent acquisition. Furthermore, the support provided by the generation AI is expected to improve the quality of patent application documents and increase the success rate of patent acquisition. As a result, the patent application support system can improve the efficiency of patent application work and reduce the amount of work required. For example, by automating the processes of prior art research and document preparation, work that relies on individual skills is reduced, and the speed of patent acquisition is improved. In addition, the support provided by generative AI is expected to improve the quality of patent application documents and increase the success rate of patent acquisition.

[0029] A patent application support system according to an embodiment includes a reception unit, a search unit, a creation unit, and a support unit. The reception unit receives technical information from a user. Examples of technical information include, but are not limited to, technical documents, drawings, and data sheets. The reception unit receives, for example, technical information entered by a user in digital format. The reception unit can also scan and convert paper-based technical information into digital data. The search unit uses a generation AI to search for prior art based on the technical information received by the reception unit. Examples of prior art include, but are not limited to, patent documents, academic papers, and products on the market. The search unit uses, for example, a generation AI to search and list related technical literature. The search unit can also search for prior art using a search algorithm or database. The creation unit uses the generation AI to create patent application documents based on the prior art information searched by the search unit. Examples of patent application documents include, but are not limited to, specifications, claims, and drawings. The creation unit generates patent application documents based on, for example, invention details and drawings entered by a user. The creation unit can also create patent application documents using document generation templates. The support unit uses a generation AI to check the patent application documents created by the creation unit and propose necessary corrections and additional information. Checks include, but are not limited to, grammar checks, technical content checks, and format checks. The support unit, for example, checks the contents of the generated patent application documents and proposes necessary corrections and additional information. This enables the patent application support system according to the embodiment to efficiently accept, search, create, and support users' technical information.

[0030] The research department can use the generative AI to search for and list relevant technical literature. Examples of generative AI include, but are not limited to, models such as GPT-4 (registered trademark) and Gemini. The research department can also use the generative AI to search for and list relevant technical literature. The research department can also use the generative AI to analyze the contents of technical literature and prioritize displaying highly relevant literature. Furthermore, the research department can use the generative AI to generate summaries of technical literature and provide them to the user. This improves the accuracy of searches for relevant technical literature by using the generative AI. Some or all of the above-mentioned processing in the research department can be performed, for example, using AI, or can be performed without using AI.

[0031] The creation unit can generate patent application documents based on invention details and drawings entered by a user. The invention details include, but are not limited to, technical descriptions, examples, and drawings. The creation unit can generate patent application documents based on, for example, invention details and drawings entered by a user. The creation unit can also use a generation AI to analyze the invention details and automatically generate patent application documents. Furthermore, the creation unit can also create patent application documents using document generation templates. This automates the generation of patent application documents based on information entered by a user. Some or all of the above-described processing in the creation unit can be performed, for example, using AI, or can be performed without using AI.

[0032] The support unit can check the content of the generated patent application documents and suggest necessary corrections or additional information. Examples of checks include, but are not limited to, grammar checks, technical content checks, and format checks. The support unit can also check the content of the generated patent application documents and suggest necessary corrections or additional information. The support unit can also use a generation AI to analyze the content of the patent application documents and suggest corrections. Furthermore, the support unit can check the content of the patent application documents based on information entered by the user and suggest necessary corrections or additional information. This improves the quality of patent application documents and increases the success rate of patent acquisition. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI.

[0033] The search department may search for prior art using a search algorithm or a database. Examples of search algorithms include, but are not limited to, keyword searches, full-text searches, and similar literature searches. Examples of databases include, but are not limited to, patent databases and academic paper databases. The search department may search for prior art using, for example, a search algorithm. The search department may also use a database to search for related technical literature. Furthermore, the search department may combine a search algorithm with a database to improve the accuracy of prior art searches. Thus, the use of a search algorithm or a database improves the accuracy of prior art searches. Some or all of the above-described processing in the search department may be performed using, for example, AI, or may be performed without using AI.

[0034] The creation unit can create patent application documents using document generation templates. Templates include, but are not limited to, patent application document formats and example descriptions. The creation unit can create patent application documents using, for example, document generation templates. The creation unit can also customize templates to create patent application documents suitable for a specific technical field. Furthermore, the creation unit can use templates to streamline the patent application document creation process. As a result, the use of templates increases the efficiency of patent application document creation. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The reception unit can analyze the user's past technical information submission history and select the optimal reception method. The submission history includes, for example, past submission dates and times, submission contents, and submission frequency, but is not limited to these examples. The reception unit, for example, analyzes the user's past technical information submission history and selects the optimal reception method. For example, the reception unit may preferentially suggest reception methods that the user has frequently used in the past. The reception unit can also suggest the optimal reception time slot based on the user's past submission history. Furthermore, the reception unit can select the optimal reception means (online, offline, etc.) based on the user's past submission history. In this way, the optimal reception method can be selected by analyzing the past submission history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0036] When receiving technical information, the reception unit may filter the technical information based on the user's current project or field of interest. Projects include, but are not limited to, research projects and development projects. Fields of interest include, but are not limited to, technical fields and research themes. The reception unit, for example, receives only technical information related to the user's ongoing project. The reception unit may also preferentially receive related technical information based on the user's field of interest. Furthermore, the reception unit may filter and receive appropriate technical information according to the progress of the user's project. This allows related technical information to be preferentially received based on the user's project or field of interest. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI.

[0037] When receiving technical information, the reception unit can select the optimal reception means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. Reception means include, but are not limited to, online forms, email, and telephone. For example, if the user desires voice input, the reception unit receives the technical information using a voice recognition system. Furthermore, if the user desires text input, the reception unit can provide a text input form. Furthermore, if the user desires image input, the reception unit can receive the technical information using an image recognition system. This allows the optimal reception means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0038] When receiving technical information, the reception unit can prioritize highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. Examples of highly relevant information include, but are not limited to, geographically nearby technical information and region-specific technical information. For example, if the user is in a specific region, the reception unit can prioritize receiving technical information related to that region. Furthermore, if the user is traveling, the reception unit can also prioritize receiving relevant technical information based on the user's current location. Furthermore, if the user is in a specific country, the reception unit can prioritize receiving technical information related to the country's patent system. This allows highly relevant technical information to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0039] When receiving the technical information, the reception unit can analyze the user's social media activity and receive related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of followers, and the engagement rate. For example, the reception unit automatically receives the technical information shared by the user on social media. The reception unit can also analyze the user's social media activity and receive related technical information. Furthermore, the reception unit can receive related technical information by referring to the activity of the user's friends on social media. In this way, related technical information can be received based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving technical information. Feedback includes, but is not limited to, user ratings, comments, and survey results, for example. The reception unit can, for example, propose an optimal reception method based on feedback provided by the user in the past. The reception unit can also improve the reception procedure based on the user's past feedback. Furthermore, the reception unit can customize the reception interface by reflecting the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0041] When searching for prior art, the search department can improve the accuracy of the search by taking into account the interrelationships between technical information. Examples of interrelationships between technical information include, but are not limited to, the relevance of technical fields and citation relationships. The search department, for example, examines search results by taking into account the citation relationships between related technical documents. The search department can also prioritize displaying highly relevant documents based on cross-referencing of technical information. Furthermore, the search department can analyze the interrelationships between technical information and improve the accuracy of the search results. Thus, by taking into account the interrelationships between technical information, the accuracy of the search is improved. Some or all of the above-described processing in the search department may be performed, for example, using AI, or may be performed without using AI.

[0042] When searching for prior art, the search department can take into account attribute information of the submitter of the technical document. The submitter's attribute information includes, but is not limited to, for example, occupation, field of expertise, affiliated institution, etc. The search department can search for relevant technical documents, for example, based on the submitter's field of expertise. The search department can also filter technical documents based on the submitter's affiliated institution. Furthermore, the search department can search for relevant technical documents taking into account the submitter's past research history. This makes it possible to search for highly relevant technical documents by taking into account the submitter's attribute information. Some or all of the above-mentioned processing in the search department can be performed, for example, using AI, or can be performed without using AI.

[0043] When searching for prior art, the research department can weight the search results based on the frequency of submission of technical documents. Examples of the frequency of submission include, but are not limited to, the number of submissions per month or the number of submissions per year. Examples of weighting include, but are not limited to, weighting based on submission frequency or importance. For example, the research department can prioritize technical documents submitted more frequently in the search results. The research department can also display less frequently submitted technical documents in the search results as a complement. Furthermore, the research department can adjust the weighting of search results based on submission frequency. Thus, by weighting search results based on submission frequency, important technical documents can be prioritized. Some or all of the above-described processing in the research department can be performed, for example, using AI or without AI.

[0044] When searching for prior art, the search department can take into account the geographical distribution of technical documents. Geographical distribution includes, but is not limited to, the distribution of technical documents by country or region. For example, the search department may prioritize searching for technical documents related to a specific region. The search department can also filter out highly relevant technical documents based on the geographical distribution. Furthermore, the search department can adjust the display order of search results taking the geographical distribution into account. This allows for prioritized searching of highly relevant technical documents by taking the geographical distribution into account. Some or all of the above-described processing in the search department may be performed, for example, using AI, or may be performed without using AI.

[0045] When searching for prior art, the search department can improve the accuracy of the search by referring to related documents. Related documents include, but are not limited to, cited documents and reference documents. The search department, for example, examines the search results based on the citation relationships of the related documents. The search department can also improve the accuracy of the search results by referring to related documents. Furthermore, the search department can prioritize displaying highly relevant technical documents based on cross-referencing of the related documents. By referring to related documents, the accuracy of the search is thereby improved. Some or all of the above-described processing in the search department may be performed, for example, using AI, or may be performed without using AI.

[0046] When searching for prior art, the research department can take into account the market value of technical documents. Market value includes, but is not limited to, sales revenue, market share, etc. The research department, for example, can prioritize displaying technical documents with high market value in the search results. The research department can also display technical documents with low market value in the search results in a complementary manner. Furthermore, the research department can adjust the weighting of search results based on market value. This allows important technical documents to be searched preferentially by taking market value into consideration. Some or all of the above-mentioned processing in the research department can be performed, for example, using AI, or can be performed without using AI.

[0047] When preparing patent application documents, the preparation unit can adjust the level of detail of the documents based on the importance of the technical information. Examples of the importance of the technical information include, but are not limited to, technical innovation and market value. Examples of the level of detail include, but are not limited to, the depth of description and comprehensiveness of information. For example, the preparation unit can prepare documents that include detailed explanations for technical information of high importance. The preparation unit can also prepare documents that include concise explanations for technical information of low importance. Furthermore, the preparation unit can adjust the level of detail of the documents based on the importance of the technical information. This allows the level of detail of the documents to be adjusted based on the importance of the technical information. Some or all of the above-described processing in the preparation unit may be performed, for example, using AI or without AI.

[0048] When preparing patent application documents, the creation unit can apply different creation algorithms depending on the category of technical information. Examples of technical information categories include, but are not limited to, technical fields and applications. Examples of creation algorithms include, but are not limited to, natural language generation algorithms and template-based generation. For example, the creation unit applies a creation algorithm specifically for chemistry to technical information in the chemical field. Furthermore, the creation unit can also apply a creation algorithm specifically for machinery to technical information in the mechanical field. Furthermore, the creation unit can also apply a creation algorithm specifically for electronics to technical information in the electronic field. This allows the application of the optimal creation algorithm depending on the category of technical information. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI.

[0049] When preparing patent application documents, the preparation unit can improve the accuracy of the preparation by referring to the user's past application results. Past application results include, but are not limited to, for example, the success or failure of patent acquisition and examination results. The preparation unit, for example, analyzes the user's past application results and refers to the format of successful application documents. The preparation unit can also improve the accuracy of the preparation by extracting areas for improvement from the user's past application results. Furthermore, the preparation unit can suggest an optimal document preparation method based on the user's past application results. This improves the accuracy of the preparation by referring to the past application results. Some or all of the above-mentioned processing in the preparation unit may be performed, for example, using AI or without AI.

[0050] When preparing patent application documents, the preparation unit can determine the priority of documents based on the submission date of technical information. Submission dates include, but are not limited to, for example, the submission date and the frequency of submission. Priority includes, but is not limited to, for example, importance and urgency. For example, the preparation unit can prioritize the preparation of documents for technical information that is submitted early. The preparation unit can also postpone the preparation of documents for technical information that is submitted later. Furthermore, the preparation unit can adjust the priority of document preparation based on the submission date. Thus, by determining the priority of documents based on the submission date, documents can be prepared efficiently. Some or all of the above-described processing in the preparation unit may be performed, for example, using AI, or may be performed without using AI.

[0051] When preparing patent application documents, the preparation unit can adjust the order of documents based on the relevance of technical information. Examples of the relevance of technical information include, but are not limited to, the relevance of technical fields and citation relationships. Examples of the order of documents include, but are not limited to, the order of relevance or importance. For example, the preparation unit prioritizes the preparation of documents for highly relevant technical information. The preparation unit can also postpone the preparation of documents for less relevant technical information. Furthermore, the preparation unit can adjust the order of document preparation based on the relevance of technical information. This allows documents to be prepared efficiently by adjusting the order of documents based on the relevance of technical information. Some or all of the above-described processing in the preparation unit may be performed, for example, using AI or without AI.

[0052] When preparing patent application documents, the creation unit can adjust the use of technical terminology in the documents according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. Examples of technical terminology include, but are not limited to, technical terms and industry jargon. For example, if the user has technical expertise, the creation unit can create documents that use a lot of technical terminology. Alternatively, if the user does not have technical expertise, the creation unit can create documents using simple language. Furthermore, the creation unit can adjust the use of technical terminology in the documents based on the user's level of expertise. This allows for the creation of documents that are easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the creation unit may be performed, for example, using AI or without AI.

[0053] When proposing a correction, the support unit can provide an optimal suggestion by referring to the user's past feedback. Examples of feedback include, but are not limited to, user ratings, comments, and survey results. The support unit, for example, makes an optimal suggestion based on feedback provided by the user in the past. The support unit can also extract improvements from the user's past feedback and make a suggestion. Furthermore, the support unit can customize the content of the suggestion by reflecting the user's feedback. This allows the optimal suggestion to be provided by referring to the past feedback. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI.

[0054] When proposing a correction, the support unit can customize the content of the proposal based on the user's current technical information. Examples of technical information include, but are not limited to, technical documents, drawings, and data sheets. The support unit, for example, makes optimal correction proposals based on the user's current technical information. The support unit can also analyze the user's current technical information and make relevant correction proposals. Furthermore, the support unit can customize the content of the correction proposals according to the user's progress in the technical information. Customizing the content of the proposal based on the current technical information allows for more appropriate correction proposals. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI.

[0055] When proposing a correction, the support unit can improve the suggestion method by reflecting user feedback. Feedback includes, but is not limited to, user ratings, comments, and survey results, for example. The support unit improves the suggestion method for correction, for example, based on the feedback provided by the user. The support unit can also extract improvements to the suggestion method from the user feedback. Furthermore, the support unit can improve the interface for suggesting correction by reflecting user feedback. In this way, the suggestion method can be improved by reflecting the feedback. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI.

[0056] When proposing revisions, the support unit can provide optimal suggestions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user is in a specific region, the support unit can provide revision suggestions related to that region. Furthermore, if the user is traveling, the support unit can also provide optimal revision suggestions based on the user's current location. Furthermore, if the user is in a specific country, the support unit can also provide revision suggestions related to the patent system of that country. This allows optimal revision suggestions to be provided by taking into account the geographical location information. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0057] When making a correction suggestion, the support unit can analyze the user's social media activity and adjust the suggestion content. Social media activity includes, but is not limited to, for example, the content of posts, the number of followers, and the engagement rate. The support unit can make correction suggestions based on, for example, technical information shared by the user on social media. The support unit can also analyze the user's social media activity and make relevant correction suggestions. Furthermore, the support unit can make relevant correction suggestions based on the activity of the user's friends on social media. In this way, relevant correction suggestions can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without using AI.

[0058] When making a correction suggestion, the support unit can customize the suggestion method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, user ratings, comments, and survey results. The support unit makes optimal correction suggestions, for example, based on feedback provided by the user in the past. The support unit can also customize the suggestion method based on the user's past feedback. Furthermore, the support unit can customize the interface for suggesting corrections by reflecting the user's feedback. In this way, the suggestion method can be customized by reflecting the past feedback. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without using AI.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When accepting technical information from a user, the acceptance unit can refer to the user's past patent application history and suggest the optimal acceptance method. For example, the acceptance unit can prioritize the proposal of patent application methods that the user has used successfully in the past. The acceptance unit can also suggest the optimal submission format and submission procedure based on the user's past application history. Furthermore, the acceptance unit can also suggest an acceptance method suitable for a specific technical field based on the user's past application history. In this way, the acceptance unit can provide the optimal acceptance method by referring to the user's past application history.

[0061] When creating patent application documents, the creation unit can adjust the content of the documents according to the user's level of expertise. For example, if the user is an expert, it can create detailed documents using a lot of technical terminology. If the user is a beginner, it can create easy-to-understand documents using simple language. Furthermore, if the user is an intermediate user, it can create documents using appropriate technical terminology. In this way, by adjusting the content of the documents according to the user's level of expertise, it is possible to create documents that are easier to understand.

[0062] When searching for prior art, the research department can scrutinize the search results by taking into account the citation relationships of technical documents. For example, the search results can be scrutinized based on the citation relationships of related technical documents. The research department can also prioritize the display of highly relevant documents based on cross-referencing of technical information. Furthermore, the research department can analyze the interrelationships of technical information to improve the accuracy of search results. This improves the accuracy of searches by taking into account the interrelationships of technical information.

[0063] When preparing patent application documents, the preparation department can determine the priority of documents based on the submission date of technical information. For example, documents can be prepared with priority for technical information that is submitted early. Also, documents can be prepared later for technical information that is submitted later. Furthermore, the preparation department can adjust the priority of document preparation based on the submission date. Thus, by determining the priority of documents based on the submission date, documents can be prepared efficiently.

[0064] When receiving technical information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize receiving technical information related to that region. In addition, if the user is traveling, the reception unit can also receive relevant technical information based on the user's current location. Furthermore, if the user is in a specific country, the reception unit can also prioritize receiving technical information related to the patent system of that country. This allows highly relevant technical information to be received preferentially based on the user's geographical location information.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception department accepts technical information from users. This technical information includes technical documents, drawings, data sheets, etc. The reception department not only accepts technical information entered by users in digital format, but can also scan technical information submitted on paper and convert it into digital data. Step 2: The search department uses the generation AI to search for prior art based on the technical information received by the reception department. Prior art includes patent documents, academic papers, products on the market, etc. The search department uses the generation AI to search for and list relevant technical literature. It can also use search algorithms and databases to search for prior art. Step 3: The Creation Department uses the generation AI to create patent application documents based on the prior art information retrieved by the Search Department. Patent application documents include specifications, claims, drawings, etc. The Creation Department can generate patent application documents based on invention details and drawings entered by the user, or it can create patent application documents using document generation templates. Step 4: The support department uses the generation AI to check the patent application documents created by the creation department and propose any necessary corrections or additional information. Checks include grammar checks, technical content checks, format checks, etc. The support department checks the content of the generated patent application documents and proposes any necessary corrections or additional information.

[0067] (Example 2) A patent application support system according to an embodiment of the present invention accepts technical information from users, uses a generation AI to search for prior art, prepares patent application documents, and proposes revisions. The patent application support system accepts technical information from users, and the generation AI automatically searches for related prior art and provides the search results. For example, if a user wishes to apply for a patent for a new product, the generation AI searches for and lists existing patents and technical literature related to that product. Next, the patent application support system automatically prepares patent application documents based on the user's input of information necessary for the patent application. For example, when a user inputs details of the invention and drawings, the generation AI generates patent application documents based on that information. Furthermore, the generation AI checks the contents of the documents and suggests necessary revisions and additional information. This patent application support system is expected to improve the efficiency of patent application procedures and reduce work hours. In particular, automating the prior art search and document preparation processes reduces the need for individualized tasks and speeds up patent acquisition. Furthermore, the support provided by the generation AI is expected to improve the quality of patent application documents and increase the success rate of patent acquisition. As a result, the patent application support system can improve the efficiency of patent application work and reduce the amount of work required. For example, by automating the processes of prior art research and document preparation, work that relies on individual skills is reduced, and the speed of patent acquisition is improved. In addition, the support provided by generative AI is expected to improve the quality of patent application documents and increase the success rate of patent acquisition.

[0068] A patent application support system according to an embodiment includes a reception unit, a search unit, a creation unit, and a support unit. The reception unit receives technical information from a user. Examples of technical information include, but are not limited to, technical documents, drawings, and data sheets. The reception unit receives, for example, technical information entered by a user in digital format. The reception unit can also scan and convert paper-based technical information into digital data. The search unit uses a generation AI to search for prior art based on the technical information received by the reception unit. Examples of prior art include, but are not limited to, patent documents, academic papers, and products on the market. The search unit uses, for example, a generation AI to search and list related technical literature. The search unit can also search for prior art using a search algorithm or database. The creation unit uses the generation AI to create patent application documents based on the prior art information searched by the search unit. Examples of patent application documents include, but are not limited to, specifications, claims, and drawings. The creation unit generates patent application documents based on, for example, invention details and drawings entered by a user. The creation unit can also create patent application documents using document generation templates. The support unit uses a generation AI to check the patent application documents created by the creation unit and propose necessary corrections and additional information. Checks include, but are not limited to, grammar checks, technical content checks, and format checks. The support unit, for example, checks the contents of the generated patent application documents and proposes necessary corrections and additional information. This enables the patent application support system according to the embodiment to efficiently accept, search, create, and support users' technical information.

[0069] The research unit can use the generative AI to search for and list relevant technical literature. Examples of generative AI include, but are not limited to, models such as GPT-4 and Gemini. The research unit can also use the generative AI to search for and list relevant technical literature. The research unit can also use the generative AI to analyze the contents of technical literature and prioritize displaying highly relevant literature. Furthermore, the research unit can also use the generative AI to generate summaries of technical literature and provide them to the user. This improves the accuracy of searching for relevant technical literature by using the generative AI. Some or all of the above-mentioned processing in the research unit can be performed, for example, using AI or without using AI.

[0070] The creation unit can generate patent application documents based on invention details and drawings entered by a user. The invention details include, but are not limited to, technical descriptions, examples, and drawings. The creation unit can generate patent application documents based on, for example, invention details and drawings entered by a user. The creation unit can also use a generation AI to analyze the invention details and automatically generate patent application documents. Furthermore, the creation unit can also create patent application documents using document generation templates. This automates the generation of patent application documents based on information entered by a user. Some or all of the above-described processing in the creation unit can be performed, for example, using AI, or can be performed without using AI.

[0071] The support unit can check the content of the generated patent application documents and suggest necessary corrections or additional information. Examples of checks include, but are not limited to, grammar checks, technical content checks, and format checks. The support unit can also check the content of the generated patent application documents and suggest necessary corrections or additional information. The support unit can also use a generation AI to analyze the content of the patent application documents and suggest corrections. Furthermore, the support unit can check the content of the patent application documents based on information entered by the user and suggest necessary corrections or additional information. This improves the quality of patent application documents and increases the success rate of patent acquisition. Some or all of the above-described processing in the support unit can be performed, for example, using AI or without AI.

[0072] The search department may search for prior art using a search algorithm or a database. Examples of search algorithms include, but are not limited to, keyword searches, full-text searches, and similar literature searches. Examples of databases include, but are not limited to, patent databases and academic paper databases. The search department may search for prior art using, for example, a search algorithm. The search department may also use a database to search for related technical literature. Furthermore, the search department may combine a search algorithm with a database to improve the accuracy of prior art searches. Thus, the use of a search algorithm or a database improves the accuracy of prior art searches. Some or all of the above-described processing in the search department may be performed using, for example, AI, or may be performed without using AI.

[0073] The creation unit can create patent application documents using document generation templates. Templates include, but are not limited to, patent application document formats and example descriptions. The creation unit can create patent application documents using, for example, document generation templates. The creation unit can also customize templates to create patent application documents suitable for a specific technical field. Furthermore, the creation unit can use templates to streamline the patent application document creation process. As a result, the use of templates increases the efficiency of patent application document creation. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The reception unit can estimate the user's emotion and adjust the timing of receiving technical information based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. The reception unit can estimate the user's emotion and adjust the timing of receiving technical information based on the estimated user emotion. For example, if the user is stressed, the reception unit can receive technical information during a time when the user is able to relax. Also, if the user is concentrating, the reception unit can receive technical information immediately. Furthermore, if the user is tired, the reception unit can receive technical information after a break. By adjusting the timing of receiving technical information according to the user's emotion, the technical information can be received at a more appropriate time. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI.

[0075] The reception unit can analyze the user's past technical information submission history and select the optimal reception method. The submission history includes, for example, past submission dates and times, submission contents, and submission frequency, but is not limited to these examples. The reception unit, for example, analyzes the user's past technical information submission history and selects the optimal reception method. For example, the reception unit may preferentially suggest reception methods that the user has frequently used in the past. The reception unit can also suggest the optimal reception time slot based on the user's past submission history. Furthermore, the reception unit can select the optimal reception means (online, offline, etc.) based on the user's past submission history. In this way, the optimal reception method can be selected by analyzing the past submission history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0076] When receiving technical information, the reception unit may filter the technical information based on the user's current project or field of interest. Projects include, but are not limited to, research projects and development projects. Fields of interest include, but are not limited to, technical fields and research themes. The reception unit, for example, receives only technical information related to the user's ongoing project. The reception unit may also preferentially receive related technical information based on the user's field of interest. Furthermore, the reception unit may filter and receive appropriate technical information according to the progress of the user's project. This allows related technical information to be preferentially received based on the user's project or field of interest. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI.

[0077] When receiving technical information, the reception unit can select the optimal reception means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. Reception means include, but are not limited to, online forms, email, and telephone. For example, if the user desires voice input, the reception unit receives the technical information using a voice recognition system. Furthermore, if the user desires text input, the reception unit can provide a text input form. Furthermore, if the user desires image input, the reception unit can receive the technical information using an image recognition system. This allows the optimal reception means to be selected depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0078] The reception unit can estimate the user's emotions and determine the priority of the technical information to be received based on the estimated user's emotions. Examples of emotions include, but are not limited to, joy, sadness, and anger. Examples of priorities include, but are not limited to, importance and urgency. The reception unit can estimate the user's emotions and determine the priority of the technical information to be received based on the estimated user's emotions. For example, if the user is nervous, technical information of high importance can be received with priority. Also, if the user is relaxed, technical information can be received with normal priority. Furthermore, if the user is in a hurry, technical information that requires prompt processing can be received with priority. Thus, by determining the priority of the technical information according to the user's emotions, important technical information can be received with priority. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI.

[0079] When receiving technical information, the reception unit can prioritize highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. Examples of highly relevant information include, but are not limited to, geographically nearby technical information and region-specific technical information. For example, if the user is in a specific region, the reception unit can prioritize receiving technical information related to that region. Furthermore, if the user is traveling, the reception unit can also prioritize receiving relevant technical information based on the user's current location. Furthermore, if the user is in a specific country, the reception unit can prioritize receiving technical information related to the country's patent system. This allows highly relevant technical information to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0080] When receiving the technical information, the reception unit can analyze the user's social media activity and receive related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of followers, and the engagement rate. For example, the reception unit automatically receives the technical information shared by the user on social media. The reception unit can also analyze the user's social media activity and receive related technical information. Furthermore, the reception unit can receive related technical information by referring to the activity of the user's friends on social media. In this way, related technical information can be received based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0081] The reception unit can customize the reception method by reflecting the user's past feedback when receiving technical information. Feedback includes, but is not limited to, user ratings, comments, and survey results, for example. The reception unit can, for example, propose an optimal reception method based on feedback provided by the user in the past. The reception unit can also improve the reception procedure based on the user's past feedback. Furthermore, the reception unit can customize the reception interface by reflecting the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The search unit can estimate the user's emotions and adjust the search criteria for prior art based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, anger, etc. Search criteria include, but are not limited to, keyword weighting and search range setting. The search unit can, for example, estimate the user's emotions and adjust the search criteria for prior art based on the estimated user emotions. For example, if the user is relaxed, broad search criteria can be used to search for prior art. Alternatively, if the user is in a hurry, strict search criteria can be used to quickly search for prior art. Furthermore, if the user is excited, more relevant technical literature can be prioritized. This allows for more appropriate prior art to be searched by adjusting the search criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or without AI.

[0083] When searching for prior art, the search department can improve the accuracy of the search by taking into account the interrelationships between technical information. Examples of interrelationships between technical information include, but are not limited to, the relevance of technical fields and citation relationships. The search department, for example, examines search results by taking into account the citation relationships between related technical documents. The search department can also prioritize displaying highly relevant documents based on cross-referencing of technical information. Furthermore, the search department can analyze the interrelationships between technical information and improve the accuracy of the search results. Thus, by taking into account the interrelationships between technical information, the accuracy of the search is improved. Some or all of the above-described processing in the search department may be performed, for example, using AI, or may be performed without using AI.

[0084] When searching for prior art, the search department can take into account attribute information of the submitter of the technical document. The submitter's attribute information includes, but is not limited to, for example, occupation, field of expertise, affiliated institution, etc. The search department can search for relevant technical documents, for example, based on the submitter's field of expertise. The search department can also filter technical documents based on the submitter's affiliated institution. Furthermore, the search department can search for relevant technical documents taking into account the submitter's past research history. This makes it possible to search for highly relevant technical documents by taking into account the submitter's attribute information. Some or all of the above-mentioned processing in the search department can be performed, for example, using AI, or can be performed without using AI.

[0085] When searching for prior art, the research department can weight the search results based on the frequency of submission of technical documents. Examples of the frequency of submission include, but are not limited to, the number of submissions per month or the number of submissions per year. Examples of weighting include, but are not limited to, weighting based on submission frequency or importance. For example, the research department can prioritize technical documents submitted more frequently in the search results. The research department can also display less frequently submitted technical documents in the search results as a complement. Furthermore, the research department can adjust the weighting of search results based on submission frequency. Thus, by weighting search results based on submission frequency, important technical documents can be prioritized. Some or all of the above-described processing in the research department can be performed, for example, using AI or without AI.

[0086] The research unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, and anger. Examples of display orders include, but are not limited to, relevance and importance. The research unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. For example, if the user is nervous, the research unit can prioritize displaying technical documents of high importance. Also, if the user is relaxed, the research unit can display search results in a normal display order. Furthermore, if the user is in a hurry, the research unit can prioritize displaying technical documents that require quick confirmation. This allows important technical documents to be prioritized by adjusting the display order of search results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the research unit can be performed using, for example, AI, or without AI.

[0087] When searching for prior art, the search department can take into account the geographical distribution of technical documents. Geographical distribution includes, but is not limited to, the distribution of technical documents by country or region. For example, the search department may prioritize searching for technical documents related to a specific region. The search department can also filter out highly relevant technical documents based on the geographical distribution. Furthermore, the search department can adjust the display order of search results taking the geographical distribution into account. This allows for prioritized searching of highly relevant technical documents by taking the geographical distribution into account. Some or all of the above-described processing in the search department may be performed, for example, using AI, or may be performed without using AI.

[0088] When searching for prior art, the search department can improve the accuracy of the search by referring to related documents. Related documents include, but are not limited to, cited documents and reference documents. The search department, for example, examines the search results based on the citation relationships of the related documents. The search department can also improve the accuracy of the search results by referring to related documents. Furthermore, the search department can prioritize displaying highly relevant technical documents based on cross-referencing of the related documents. By referring to related documents, the accuracy of the search is thereby improved. Some or all of the above-described processing in the search department may be performed, for example, using AI, or may be performed without using AI.

[0089] When searching for prior art, the research department can take into account the market value of technical documents. Market value includes, but is not limited to, sales revenue, market share, etc. The research department, for example, can prioritize displaying technical documents with high market value in the search results. The research department can also display technical documents with low market value in the search results in a complementary manner. Furthermore, the research department can adjust the weighting of search results based on market value. This allows important technical documents to be searched preferentially by taking market value into consideration. Some or all of the above-mentioned processing in the research department can be performed, for example, using AI, or can be performed without using AI.

[0090] The creation unit can estimate a user's emotions and adjust the expression of the patent application documents based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger. Expression methods include, but are not limited to, writing style and terminology. The creation unit can, for example, estimate a user's emotions and adjust the expression of the patent application documents based on the estimated user emotions. For example, if the user is nervous, a simple and clear expression can be used. If the user is relaxed, an expression that includes detailed explanations can be used. Furthermore, if the user is in a hurry, a concise expression that focuses on the main points can be used. This allows for more appropriate patent application documents to be created by adjusting the expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the creation unit can be performed using, for example, AI, or without AI.

[0091] When preparing patent application documents, the preparation unit can adjust the level of detail of the documents based on the importance of the technical information. Examples of the importance of the technical information include, but are not limited to, technical innovation and market value. Examples of the level of detail include, but are not limited to, the depth of description and comprehensiveness of information. For example, the preparation unit can prepare documents that include detailed explanations for technical information of high importance. The preparation unit can also prepare documents that include concise explanations for technical information of low importance. Furthermore, the preparation unit can adjust the level of detail of the documents based on the importance of the technical information. This allows the level of detail of the documents to be adjusted based on the importance of the technical information. Some or all of the above-described processing in the preparation unit may be performed, for example, using AI or without AI.

[0092] When preparing patent application documents, the creation unit can apply different creation algorithms depending on the category of technical information. Examples of technical information categories include, but are not limited to, technical fields and applications. Examples of creation algorithms include, but are not limited to, natural language generation algorithms and template-based generation. For example, the creation unit applies a creation algorithm specifically for chemistry to technical information in the chemical field. Furthermore, the creation unit can also apply a creation algorithm specifically for machinery to technical information in the mechanical field. Furthermore, the creation unit can also apply a creation algorithm specifically for electronics to technical information in the electronic field. This allows the application of the optimal creation algorithm depending on the category of technical information. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI.

[0093] When preparing patent application documents, the preparation unit can improve the accuracy of the preparation by referring to the user's past application results. Past application results include, but are not limited to, for example, the success or failure of patent acquisition and examination results. The preparation unit, for example, analyzes the user's past application results and refers to the format of successful application documents. The preparation unit can also improve the accuracy of the preparation by extracting areas for improvement from the user's past application results. Furthermore, the preparation unit can suggest an optimal document preparation method based on the user's past application results. This improves the accuracy of the preparation by referring to the past application results. Some or all of the above-mentioned processing in the preparation unit may be performed, for example, using AI or without AI.

[0094] The creation unit can estimate the user's emotions and adjust the length of the patent application document based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. Document length includes, but is not limited to, the number of pages and the number of characters. The creation unit can estimate the user's emotions and adjust the length of the patent application document based on the estimated user emotions. For example, if the user is nervous, the creation unit can create a short, to-the-point document. Alternatively, if the user is relaxed, the creation unit can create a longer document with detailed explanations. Furthermore, if the user is in a hurry, the creation unit can create a document that highlights parts that require quick confirmation. This allows for the creation of more appropriate patent application documents by adjusting the document length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the creation unit can be performed using, for example, AI, or without AI.

[0095] When preparing patent application documents, the preparation unit can determine the priority of documents based on the submission date of technical information. Submission dates include, but are not limited to, for example, the submission date and the frequency of submission. Priority includes, but is not limited to, for example, importance and urgency. For example, the preparation unit can prioritize the preparation of documents for technical information that is submitted early. The preparation unit can also postpone the preparation of documents for technical information that is submitted later. Furthermore, the preparation unit can adjust the priority of document preparation based on the submission date. Thus, by determining the priority of documents based on the submission date, documents can be prepared efficiently. Some or all of the above-described processing in the preparation unit may be performed, for example, using AI, or may be performed without using AI.

[0096] When preparing patent application documents, the preparation unit can adjust the order of documents based on the relevance of technical information. Examples of the relevance of technical information include, but are not limited to, the relevance of technical fields and citation relationships. Examples of the order of documents include, but are not limited to, the order of relevance or importance. For example, the preparation unit prioritizes the preparation of documents for highly relevant technical information. The preparation unit can also postpone the preparation of documents for less relevant technical information. Furthermore, the preparation unit can adjust the order of document preparation based on the relevance of technical information. This allows documents to be prepared efficiently by adjusting the order of documents based on the relevance of technical information. Some or all of the above-described processing in the preparation unit may be performed, for example, using AI or without AI.

[0097] When preparing patent application documents, the creation unit can adjust the use of technical terminology in the documents according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and expert. Examples of technical terminology include, but are not limited to, technical terms and industry jargon. For example, if the user has technical expertise, the creation unit can create documents that use a lot of technical terminology. Alternatively, if the user does not have technical expertise, the creation unit can create documents using simple language. Furthermore, the creation unit can adjust the use of technical terminology in the documents based on the user's level of expertise. This allows for the creation of documents that are easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the creation unit may be performed, for example, using AI or without AI.

[0098] The support unit can estimate the user's emotions and adjust the correction suggestion method based on the estimated user's emotions. Emotions include, but are not limited to, joy, sadness, anger, and the like. Correction suggestion methods include, but are not limited to, the form and content of the suggestions. For example, the support unit can estimate the user's emotions and adjust the correction suggestion method based on the estimated user's emotions. For example, if the user is nervous, the support unit can provide simple and clear correction suggestions. For example, if the user is relaxed, the support unit can provide correction suggestions with detailed explanations. Furthermore, if the user is in a hurry, the support unit can provide correction suggestions that highlight parts that require quick confirmation. This allows for more appropriate correction suggestions by adjusting the correction suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or without AI.

[0099] When proposing a correction, the support unit can provide an optimal suggestion by referring to the user's past feedback. Examples of feedback include, but are not limited to, user ratings, comments, and survey results. The support unit, for example, makes an optimal suggestion based on feedback provided by the user in the past. The support unit can also extract improvements from the user's past feedback and make a suggestion. Furthermore, the support unit can customize the content of the suggestion by reflecting the user's feedback. This allows the optimal suggestion to be provided by referring to the past feedback. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI.

[0100] When proposing a correction, the support unit can customize the content of the proposal based on the user's current technical information. Examples of technical information include, but are not limited to, technical documents, drawings, and data sheets. The support unit, for example, makes optimal correction proposals based on the user's current technical information. The support unit can also analyze the user's current technical information and make relevant correction proposals. Furthermore, the support unit can customize the content of the correction proposals according to the user's progress in the technical information. Customizing the content of the proposal based on the current technical information allows for more appropriate correction proposals. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI.

[0101] When proposing a correction, the support unit can improve the suggestion method by reflecting user feedback. Feedback includes, but is not limited to, user ratings, comments, and survey results, for example. The support unit improves the suggestion method for correction, for example, based on the feedback provided by the user. The support unit can also extract improvements to the suggestion method from the user feedback. Furthermore, the support unit can improve the interface for suggesting correction by reflecting user feedback. In this way, the suggestion method can be improved by reflecting the feedback. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI.

[0102] The support unit can estimate the user's emotions and prioritize revision suggestions based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, and anger. Examples of priorities include, but are not limited to, importance and urgency. The support unit can estimate the user's emotions and prioritize revision suggestions based on the estimated user emotions. For example, if the user is nervous, revision suggestions with high importance can be prioritized. Alternatively, if the user is relaxed, revision suggestions can be prioritized with normal priority. Furthermore, if the user is in a hurry, revision suggestions that require immediate confirmation can be prioritized. In this way, by prioritizing revision suggestions according to the user's emotions, important revision suggestions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the support unit can be performed, for example, using AI or without AI.

[0103] When proposing revisions, the support unit can provide optimal suggestions by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user is in a specific region, the support unit can provide revision suggestions related to that region. Furthermore, if the user is traveling, the support unit can also provide optimal revision suggestions based on the user's current location. Furthermore, if the user is in a specific country, the support unit can also provide revision suggestions related to the patent system of that country. This allows optimal revision suggestions to be provided by taking into account the geographical location information. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI.

[0104] When making a correction suggestion, the support unit can analyze the user's social media activity and adjust the suggestion content. Social media activity includes, but is not limited to, for example, the content of posts, the number of followers, and the engagement rate. The support unit can make correction suggestions based on, for example, technical information shared by the user on social media. The support unit can also analyze the user's social media activity and make relevant correction suggestions. Furthermore, the support unit can make relevant correction suggestions based on the activity of the user's friends on social media. In this way, relevant correction suggestions can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without using AI.

[0105] When making a correction suggestion, the support unit can customize the suggestion method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, user ratings, comments, and survey results. The support unit makes optimal correction suggestions, for example, based on feedback provided by the user in the past. The support unit can also customize the suggestion method based on the user's past feedback. Furthermore, the support unit can customize the interface for suggesting corrections by reflecting the user's feedback. In this way, the suggestion method can be customized by reflecting the past feedback. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, research unit, creation unit, and support unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives technical information from a user in digital form. The research unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for prior art using a generation AI. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates patent application documents. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks the created patent application documents and suggests necessary corrections and additional information. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, research unit, creation unit, and support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives technical information from a user via voice input. The research unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for prior art using a generation AI. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates patent application documents. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks the created patent application documents and suggests necessary corrections and additional information. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, research unit, creation unit, and support unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives technical information from a user via voice input. The research unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for prior art using a generation AI. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates patent application documents. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks the created patent application documents and suggests necessary corrections and additional information. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, research unit, creation unit, and support unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives technical information from a user via voice input. The research unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for prior art using a generation AI. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates patent application documents. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks the created patent application documents and suggests necessary corrections and additional information.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When accepting technical information from a user, the acceptance unit can refer to the user's past patent application history and suggest the optimal acceptance method. For example, the acceptance unit can prioritize the proposal of patent application methods that the user has used successfully in the past. The acceptance unit can also suggest the optimal submission format and submission procedure based on the user's past application history. Furthermore, the acceptance unit can also suggest an acceptance method suitable for a specific technical field based on the user's past application history. In this way, the acceptance unit can provide the optimal acceptance method by referring to the user's past application history.

[0108] The research unit can estimate the user's emotions and filter search results for prior art based on the estimated user emotions. For example, if the user is feeling stressed, a concise summary of the search results can be provided. Also, if the user is feeling relaxed, detailed technical literature can be displayed preferentially. Furthermore, if the user is in a hurry, the most relevant technical literature can be displayed quickly. In this way, more appropriate information can be provided by filtering search results according to the user's emotions.

[0109] When creating patent application documents, the creation unit can adjust the content of the documents according to the user's level of expertise. For example, if the user is an expert, it can create detailed documents using a lot of technical terminology. If the user is a beginner, it can create easy-to-understand documents using simple language. Furthermore, if the user is an intermediate user, it can create documents using appropriate technical terminology. In this way, by adjusting the content of the documents according to the user's level of expertise, it is possible to create documents that are easier to understand.

[0110] The support unit can estimate the user's emotions and adjust the content of the correction suggestions based on the estimated user emotions. For example, if the user is nervous, a simple and clear correction suggestion can be made. If the user is relaxed, a correction suggestion including detailed explanations can be made. Furthermore, if the user is in a hurry, a correction suggestion that highlights parts that need to be checked quickly can be made. In this way, by adjusting the content of the correction suggestion according to the user's emotions, more appropriate correction suggestions can be made.

[0111] When searching for prior art, the research department can scrutinize the search results by taking into account the citation relationships of technical documents. For example, the search results can be scrutinized based on the citation relationships of related technical documents. The research department can also prioritize the display of highly relevant documents based on cross-referencing of technical information. Furthermore, the research department can analyze the interrelationships of technical information to improve the accuracy of search results. This improves the accuracy of searches by taking into account the interrelationships of technical information.

[0112] The reception unit can estimate the user's emotions and customize the method for receiving technical information based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided. Alternatively, if the user is relaxed, a detailed input form can be provided. Furthermore, if the user is in a hurry, a method that allows for quick input can be provided. In this way, by customizing the reception method according to the user's emotions, a more appropriate reception method can be provided.

[0113] When preparing patent application documents, the preparation department can determine the priority of documents based on the submission date of technical information. For example, documents can be prepared with priority for technical information that is submitted early. Also, documents can be prepared later for technical information that is submitted later. Furthermore, the preparation department can adjust the priority of document preparation based on the submission date. Thus, by determining the priority of documents based on the submission date, documents can be prepared efficiently.

[0114] The research department can estimate the user's emotions and adjust the display order of search results based on the estimated user's emotions. For example, if the user is nervous, technical documents of high importance can be displayed preferentially. Alternatively, if the user is relaxed, search results can be displayed in the normal display order. Furthermore, if the user is in a hurry, technical documents that require quick confirmation can be displayed preferentially. In this way, by adjusting the display order of search results according to the user's emotions, important technical documents can be displayed preferentially.

[0115] When receiving technical information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize receiving technical information related to that region. In addition, if the user is traveling, the reception unit can also receive relevant technical information based on the user's current location. Furthermore, if the user is in a specific country, the reception unit can also prioritize receiving technical information related to the patent system of that country. This allows highly relevant technical information to be received preferentially based on the user's geographical location information.

[0116] The support unit can estimate the user's emotions when making correction suggestions and determine the priority of the correction suggestions based on the estimated user emotions. For example, if the user is nervous, correction suggestions with high importance can be given priority. If the user is relaxed, correction suggestions can be given with normal priority. Furthermore, if the user is in a hurry, correction suggestions that require quick confirmation can be given priority. In this way, by determining the priority of correction suggestions according to the user's emotions, important correction suggestions can be given priority.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception department accepts technical information from users. This technical information includes technical documents, drawings, data sheets, etc. The reception department not only accepts technical information entered by users in digital format, but can also scan technical information submitted on paper and convert it into digital data. Step 2: The search department uses the generation AI to search for prior art based on the technical information received by the reception department. Prior art includes patent documents, academic papers, products on the market, etc. The search department uses the generation AI to search for and list relevant technical literature. It can also use search algorithms and databases to search for prior art. Step 3: The Creation Department uses the generation AI to create patent application documents based on the prior art information retrieved by the Search Department. Patent application documents include specifications, claims, drawings, etc. The Creation Department can generate patent application documents based on invention details and drawings entered by the user, or it can create patent application documents using document generation templates. Step 4: The support department uses the generation AI to check the patent application documents created by the creation department and propose any necessary corrections or additional information. Checks include grammar checks, technical content checks, format checks, etc. The support department checks the content of the generated patent application documents and proposes any necessary corrections or additional information.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives technical information from a user; a search unit that searches for prior art based on the technical information received by the reception unit; a preparation unit that prepares patent application documents based on the prior art information searched by the search unit; a support unit that checks the patent application documents created by the creation unit and makes correction suggestions. A system characterized by:

2. The research department Use generative AI to search and list relevant technical literature 2. The system of claim 1.

3. The creation unit Generate patent applications based on user-entered invention details and drawings 2. The system of claim 1.

4. The support portion is Check the content of the generated patent application documents and suggest any necessary corrections or additional information 2. The system of claim 1.

5. The research department Searching for prior art using search algorithms and databases 2. The system of claim 1.

6. The creation unit Create patent applications using document generation templates 2. The system of claim 1.

7. The reception unit Estimating user emotions and adjusting the timing of receiving technical information based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past technical information submission history and select the optimal reception method 2. The system of claim 1.

Images