System

Abstract

An object of a system according to an embodiment is to enable even an ordinary person to easily perform a patent procedure and management of patent information.SOLUTION: A system according to an embodiment includes a user input section, a handling method proposing section, a reminding section, and an information providing section. The user input unit inputs a situation of a user. The handling method proposing section proposes a handling method based on the situation input by the user input section. The reminding unit manages a progress status of the patent application or the examination and reminds a procedure. The information providing part provides an example of a patent invention or an invention law, and latest patent trend information.SELECTED DRAWING: Figure 1

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] Conventional technology has the problem that patent procedures and patent information management are difficult for the general public to understand and require the help of experts.

[0005] The system according to the embodiment aims to make it easy for even ordinary people to manage patent procedures and patent information. [Means for solving the problem]

[0006] The system according to the embodiment includes a user input unit, a solution proposal unit, a reminder unit, and an information providing unit. The user input unit inputs the user's situation. The solution proposal unit proposes a solution based on the situation input by the user input unit. The reminder unit manages the progress of patent applications or examinations and provides procedure reminders. The information providing unit provides examples of patent inventions or invention methods, and the latest patent trend information. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible for even ordinary people to easily manage patent procedures and patent information. [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) The AI ​​system according to the embodiment of the present invention is a system in which a user talks to the AI ​​about the situation regarding a patent, and the AI ​​system suggests solutions, reminds users of procedures, and provides information. This allows the AI ​​system to provide various support related to patents, streamline the patent acquisition process, and reduce user concerns.

[0029] The AI ​​system according to the embodiment includes a user input unit, a solution proposal unit, a reminder unit, and an information provision unit. The user input unit inputs the user's situation. For example, the user can input details of their patent-related concerns by talking about them. The solution proposal unit proposes solutions based on the situation input by the user input unit. For example, in response to a request for information about patent application procedures, the solution proposal unit provides specific explanations of the patent application procedure, necessary documents, important points, etc. The reminder unit manages the progress of patent applications or examinations and provides procedure reminders. For example, it notifies users that a patent application deadline is approaching. The information provision unit provides examples of patented inventions, invention methods, and the latest patent trend information. For example, it provides examples of inventions that have been patented in the past and explains methods for realizing invention ideas. As a result, the AI ​​system according to the embodiment can provide various support related to patents, streamline the patent acquisition process, and reduce user concerns.

[0030] The user input unit can refer to the user's past consultation history and propose solutions based on similar cases. The user input unit, for example, searches a database for the user's past consultation history and proposes solutions based on similar cases. For example, it introduces success stories of users who have had similar problems in the past. It also analyzes the user's past consultation content, extracts patterns, and proposes solutions. For example, it identifies common problems from past consultation content and provides advice based on those. It also refers to the user's past consultation history and automatically generates solutions based on similar cases. For example, it compares past consultation content with current consultation content and proposes the optimal solution. This makes it possible to provide more appropriate support by proposing solutions based on past consultation history.

[0031] The user input unit can convert what the user says into text using voice recognition technology and generate detailed solutions based on the text data. The user input unit, for example, converts what the user says into text using voice recognition technology and generates detailed solutions based on the text data. For example, it explains the specific steps for filing a patent application. It also converts what the user says into text using voice recognition technology and analyzes the text data to suggest solutions. For example, it provides a list of documents required for a patent application. It also converts what the user says into text using voice recognition technology and automatically generates detailed solutions based on the text data. For example, it provides detailed explanations of points to note when filing a patent application and examination criteria. In this way, by providing detailed solutions using voice recognition technology, user convenience is improved.

[0032] The user input unit can visualize what the user says and present solutions in a visually easy-to-understand format. The user input unit, for example, visualizes what the user says and presents solutions visually. For example, it displays the steps for filing a patent application in a flowchart. It also visualizes what the user says and shows solutions in diagrams and graphs. For example, it displays the documents required for a patent application in list format. It also visualizes what the user says and provides solutions in infographics. For example, it explains the patent application process in diagrams. This visualization makes it easier for the user to understand solutions.

[0033] The user input unit can also accommodate consultations in different languages ​​and provide solutions in multiple languages. The user input unit, for example, allows users to consult in different languages ​​and provides solutions in multiple languages. For example, patent application procedures are explained in English, French, Chinese, etc. In addition, multilingual voice recognition technology is used to translate what the user says in real time and provide solutions. For example, what the user says in Japanese is translated into English and solutions are presented. In addition, in order to accommodate consultations in different languages, a multilingual database is built and solutions are provided. For example, information on patent application procedures in various countries is provided in multiple languages. This multilingual support makes it possible to accommodate users who speak different languages.

[0034] The solution proposal unit can customize and propose solutions according to the user's level of expertise. The solution proposal unit, for example, analyzes the user's level of expertise and proposes solutions that are customized accordingly. For example, it provides patent application procedures for beginners. In addition, a system is constructed that customizes solutions based on the user's level of expertise. For example, it provides simple explanations that avoid technical jargon. In addition, it dynamically customizes solutions taking into account the user's level of expertise. For example, it provides detailed technical information to users with extensive expertise. This makes it possible to provide more appropriate support by providing solutions according to the user's level of expertise.

[0035] The solution proposal unit can predict and propose the optimal solution based on the user's past behavioral history. The solution proposal unit, for example, analyzes the user's past behavioral history and predicts and proposes the optimal solution. For example, it makes new proposals based on solutions that have been successful in the past. It also searches a database for the user's behavioral history and proposes solutions based on similar cases. For example, it compares past consultation content with current consultation content to provide the optimal solution. It also develops an algorithm to predict solutions based on the user's past behavioral history and makes suggestions. For example, it automatically generates the optimal solution based on past data. This enables more appropriate support by providing solutions based on past behavioral history.

[0036] The solution proposal unit can explain the solution using video or animation to make it easier to understand visually. The solution proposal unit, for example, explains the solution using video or animation to make it easier to understand visually. For example, the steps for filing a patent application are shown using animation. Furthermore, the solution proposal unit uses video or animation to explain the solution so that the user can visually understand it. For example, the method for preparing documents required for a patent application is shown using video. Furthermore, a system is constructed that automatically generates video or animation to make the solution easier to understand visually. For example, the patent application process is explained using animation. This provides a solution that is easy to understand visually, thereby deepening the user's understanding.

[0037] The solution proposal unit can introduce other users' success stories when proposing a solution, allowing the user to refer to them. The solution proposal unit, for example, introduces other users' success stories when proposing a solution. For example, it can introduce the cases of users who have obtained patents in the past. Furthermore, a system can be constructed that proposes solutions based on other users' success stories. For example, it can search for success stories from a database and provide them to the user. Furthermore, it can refer to other users' success stories when proposing a solution. For example, it can provide specific advice based on success stories. In this way, by referring to other users' success stories, the user's options for solutions can be expanded.

[0038] The reminder unit can automatically analyze the user's schedule and send reminders at the optimal timing. The reminder unit, for example, automatically analyzes the user's schedule and sends reminders at the optimal timing. For example, it sends a reminder when the deadline for a patent application is approaching. It also uses a schedule analysis algorithm to analyze the user's schedule and send reminders at the optimal timing. For example, it sends reminders that avoid times when the user is busy. It also predicts the optimal timing for a reminder based on the user's schedule data and sends the reminder. For example, it sends a reminder when the user is relaxing. This improves user convenience by providing reminders that suit the user's schedule.

[0039] The reminding unit can customize the reminder content in detail and provide information that is optimal for the user's situation. The reminding unit, for example, customizes the reminder content in detail and provides information that is optimal for the user's situation. For example, specific reminders are given according to the progress status of a patent application. Furthermore, a system is constructed that analyzes the user's situation and customizes the reminder content based on that analysis. For example, reminders are given according to the status of documents submitted by the user. Furthermore, the reminder content is dynamically customized to suit the user's situation. For example, reminders are provided according to the progress status of patent examination. This improves user convenience by providing reminders that are optimal for the user's situation.

[0040] The reminding unit can provide reminders by voice messages or video messages, and can provide visual and audio notifications. For example, the reminding unit can provide reminders by voice messages and provide visual and audio notifications. For example, when a patent application deadline is approaching, a reminder can be sent by voice message. Also, the reminder can be provided by video messages and provide visual and audio notifications. For example, the patent application procedures can be explained by video messages. Also, a system can be built that provides visual and audio reminders using voice messages or video messages. For example, the progress of patent examination can be notified by video messages. In this way, providing visual and audio reminders makes it easier to attract the user's attention.

[0041] The reminding unit can provide related materials and links at the same time as a reminder. The reminding unit, for example, provides related materials and links at the same time as a reminder. For example, a link related to patent application procedures is included in the remind message. In addition, a system is constructed that automatically attaches related materials and links to remind messages. For example, materials related to the progress of patent examination are attached to the remind message. In addition, related materials and links are dynamically provided at the time of a reminder. For example, templates of documents required for patent applications are included in the remind message. In this way, by providing related materials and links, the user can quickly access the information they need.

[0042] The information providing unit can provide customized example inventions based on the user's interests and concerns. The information providing unit, for example, analyzes the user's interests and concerns and provides customized example inventions based on the analysis. For example, it introduces example inventions in technical fields that interest the user. It also builds a system that customizes example inventions based on the user's interests and concerns. For example, it provides example inventions related to themes that interest the user. It also dynamically provides customized example inventions taking the user's interests and concerns into consideration. For example, it introduces example inventions related to keywords that the user has searched for in the past. In this way, it is possible to provide example inventions that match the user's interests and concerns, thereby improving the user's motivation.

[0043] The information providing unit can suggest related invention examples based on the user's past search history. The information providing unit, for example, analyzes the user's past search history and suggests related invention examples based on it. For example, it introduces invention examples in technical fields that have been searched in the past. It also builds a system that searches the user's search history from a database and suggests similar invention examples. For example, it provides invention examples related to the content of past searches. It also dynamically suggests related invention examples based on the user's past search history. For example, it introduces invention examples related to keywords that have been searched in the past. This improves user convenience by providing invention examples based on past search history.

[0044] The information providing unit can explain invention examples or invention methods using videos or animations to make them easier to understand visually. The information providing unit, for example, explains invention examples or invention methods using videos or animations to make them easier to understand visually. For example, specific examples of patent inventions are shown using animations. Furthermore, video and animations are used to explain invention examples and invention methods so that users can visually understand them. For example, videos are used to show how to materialize an invention idea. Furthermore, a system is constructed that automatically generates videos and animations to make invention examples and invention methods easier to understand visually. For example, animations are used to explain how to prepare patent application documents. In this way, by providing invention examples and invention methods that are easy to understand visually, users can deepen their understanding.

[0045] The information provision unit can introduce examples of inventions from different fields and provide ideas for interdisciplinary integration. For example, the information provision unit introduces examples of inventions from different fields and provides ideas for interdisciplinary integration. For example, it introduces an idea that combines examples of inventions from the medical and IT fields. It also builds a system that proposes ideas for interdisciplinary integration based on examples of inventions from different fields. For example, it searches a database for examples of inventions from different technical fields and provides them to users. It also introduces examples of inventions from different fields and dynamically provides ideas for interdisciplinary integration. For example, it introduces examples of inventions that combine technologies from different fields. This provides ideas for interdisciplinary integration, thereby broadening the scope of users' inventions.

[0046] The information providing unit can provide customized trend information based on the user's interests and concerns. The information providing unit, for example, analyzes the user's interests and concerns and provides customized trend information based on the analysis. For example, trend information on technical fields in which the user is interested is provided. A system is also constructed that customizes trend information based on the user's interests and concerns. For example, trend information related to themes in which the user is interested is provided. Customized trend information is also dynamically provided taking the user's interests and concerns into consideration. For example, trend information related to keywords that the user has searched for in the past is provided. This improves the user's motivation by providing trend information that matches the user's interests and concerns.

[0047] The information providing unit can suggest related trend information based on the user's past search history. The information providing unit, for example, analyzes the user's past search history and suggests related trend information based on it. For example, it provides trend information in technical fields previously searched. It also builds a system that searches a database for the user's search history and suggests similar trend information. For example, it provides trend information related to the content of past searches. It also dynamically suggests related trend information based on the user's past search history. For example, it provides trend information related to keywords previously searched. This improves user convenience by providing trend information based on past search history.

[0048] The information providing unit can provide trend information in the form of infographics or visual notes to make it easier to understand visually. The information providing unit, for example, provides trend information in the form of infographics to make it easier to understand visually. For example, trends in patent technology are shown in graphs or diagrams. Also, the information providing unit provides trend information in the form of visual notes to make it easier to understand visually. For example, trends in patent technology are explained using diagrams. Also, to make trend information easier to understand visually, a system is constructed that automatically generates infographics and visual notes. For example, trends in patent technology are shown in infographics. This provides trend information that is easy to understand visually, thereby deepening the user's understanding.

[0049] The information providing unit can compare trend information from different industries and fields and provide comprehensive insights. For example, the information providing unit compares trend information from different industries and fields and provides comprehensive insights. For example, it compares patent technology trends in the IT and medical fields. In addition, a system is built that provides comprehensive insights based on trend information from different industries. For example, it searches a database for trend information from different technology fields and provides it to users. In addition, it compares trend information from different industries and fields and dynamically provides comprehensive insights. For example, it compares and shows technology trends in different fields. In this way, it is possible to provide comprehensive insights to users by comparing trend information from different industries and fields.

[0050] The information providing unit can provide customized foreign patent information based on the user's interests and concerns. The information providing unit, for example, analyzes the user's interests and concerns and provides customized foreign patent information based on the analysis. For example, it provides patent information on countries in which the user is interested. In addition, a system is constructed that customizes foreign patent information based on the user's interests and concerns. For example, it provides foreign patent information related to themes in which the user is interested. In addition, it dynamically provides customized foreign patent information taking the user's interests and concerns into consideration. For example, it provides foreign patent information related to keywords that the user has searched for in the past. In this way, providing foreign patent information that matches the user's interests and concerns improves the user's motivation.

[0051] The information providing unit can suggest related foreign patent information based on the user's past search history. The information providing unit, for example, analyzes the user's past search history and suggests related foreign patent information based on that. For example, it provides patent information from countries previously searched. It also builds a system that searches a database for the user's search history and suggests similar foreign patent information. For example, it provides foreign patent information related to the content of past searches. It also dynamically suggests related foreign patent information based on the user's past search history. For example, it provides foreign patent information related to keywords previously searched. This improves user convenience by providing foreign patent information based on past search history.

[0052] The information providing unit can provide foreign patent information in the form of infographics or visual notes to make it easier to understand visually. The information providing unit, for example, provides foreign patent information in the form of infographics to make it easier to understand visually. For example, the patent application procedures of each country are shown in graphs and diagrams. The information providing unit also provides foreign patent information in the form of visual notes to make it easier to understand visually. For example, the patent examination standards of each country are explained in diagrams. In addition, a system is constructed to automatically generate infographics and visual notes to make foreign patent information easier to understand visually. For example, changes to patent laws of each country are shown in infographics. This provides foreign patent information that is easier to understand visually, thereby deepening the user's understanding.

[0053] The information providing unit can compare patent information from different countries and provide comprehensive insights. The information providing unit, for example, compares patent information from different countries and provides comprehensive insights. For example, it compares patent application procedures between the United States and Japan. It also builds a system that provides comprehensive insights based on patent information from different countries. For example, it searches a database for patent examination standards from each country and provides them to users. It also compares patent information from different countries and dynamically provides comprehensive insights. For example, it compares and shows changes to patent laws in each country. This makes it possible to provide comprehensive insights to users by comparing patent information from different countries.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The user input unit can refer to the user's past consultation history and suggest solutions based on similar cases. For example, it can introduce success stories of users who have had similar problems in the past. It can also analyze the user's past consultation content, extract patterns, and suggest solutions. For example, it can identify common problems from past consultation content and provide advice based on them. It can also refer to the user's past consultation history and automatically generate solutions based on similar cases. For example, it can compare past consultation content with the current consultation content and suggest the optimal solution. This makes it possible to provide more appropriate support by suggesting solutions based on past consultation history.

[0056] The user input unit can convert what the user says into text using voice recognition technology and generate detailed solutions based on the text data. For example, it can explain the specific steps for filing a patent application. It can also convert what the user says into text using voice recognition technology and analyze the text data to suggest solutions. For example, it can provide a list of documents required for a patent application. It can also convert what the user says into text using voice recognition technology and automatically generate detailed solutions based on the text data. For example, it can provide detailed explanations of points to note when filing a patent application and examination criteria. This improves user convenience by providing detailed solutions using voice recognition technology.

[0057] The user input unit can visualize what the user says and present solutions in a visually easy-to-understand format. For example, it can display the steps for filing a patent application in a flowchart. It can also visualize what the user says and show solutions in diagrams and graphs. For example, it can display the documents required for a patent application in list format. It can also visualize what the user says and provide solutions in the form of infographics. For example, it can explain the patent application process in diagrams. This visualization makes it easier for users to understand solutions.

[0058] The user input unit can also accommodate consultations in different languages ​​and provide solutions in multiple languages. For example, it allows users to consult in different languages ​​and provides solutions in multiple languages. For example, patent application procedures are explained in English, French, Chinese, etc. In addition, multilingual voice recognition technology is used to translate what the user says in real time and provide solutions. For example, what the user says in Japanese is translated into English and solutions are presented. In addition, in order to accommodate consultations in different languages, a multilingual database is built and solutions are provided. For example, information on patent application procedures in various countries is provided in multiple languages. This multilingual support makes it possible to accommodate users who speak different languages.

[0059] The solution suggestion unit can customize and suggest solutions according to the user's level of expertise. For example, it analyzes the user's level of expertise and customizes and suggests solutions according to that level. For example, it provides patent application procedures for beginners. It also builds a system that customizes solutions based on the user's level of expertise. For example, it provides simple explanations that avoid technical jargon. It also dynamically customizes solutions taking into account the user's level of expertise. For example, it provides detailed technical information to users with extensive expertise. This allows for more appropriate support by providing solutions according to the user's level of expertise.

[0060] The solution suggestion unit can predict and suggest the optimal solution based on the user's past behavioral history. For example, it analyzes the user's past behavioral history to predict and suggest the optimal solution. For example, it makes new suggestions based on solutions that have been successful in the past. It also searches a database for the user's behavioral history to suggest solutions based on similar cases. For example, it compares past consultation content with the current consultation content to provide the optimal solution. It also develops an algorithm to predict solutions based on the user's past behavioral history and makes suggestions. For example, it automatically generates the optimal solution based on past data. This makes it possible to provide more appropriate support by providing solutions based on past behavioral history.

[0061] The solution suggestion unit can explain the solution using video or animation to make it easier to understand visually. For example, the solution can be explained using video or animation to make it easier to understand visually. For example, the steps for filing a patent application can be shown using animation. Furthermore, the solution can be explained using video or animation so that the user can visually understand it. For example, a video can be shown to show how to prepare the documents required for a patent application. Furthermore, a system can be built that automatically generates video or animation to make the solution easier to understand visually. For example, the patent application process can be explained using animation. This can provide a solution that is easy to understand visually, thereby deepening the user's understanding.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The user input unit inputs the user's situation. For example, the user can input the details of their concerns about patents. Step 2: The solution suggestion unit proposes a solution based on the situation input by the user input unit. For example, in response to a request to learn about patent application procedures, the unit provides a detailed explanation of the patent application procedures, necessary documents, points to note, etc. Step 3: The Reminders Department manages the progress of patent applications and examinations and provides procedural reminders, such as notifying users that patent application deadlines are approaching. Step 4: The information provider provides examples of patented inventions, invention methods, and the latest patent trend information. For example, they provide examples of inventions that have been patented in the past and explain how to materialize invention ideas.

[0064] (Example 2) The AI ​​system according to the embodiment of the present invention is a system in which a user talks to the AI ​​about the situation regarding a patent, and the AI ​​system suggests solutions, reminds users of procedures, and provides information. This allows the AI ​​system to provide various support related to patents, streamline the patent acquisition process, and reduce user concerns.

[0065] The AI ​​system according to the embodiment includes a user input unit, a solution proposal unit, a reminder unit, and an information provision unit. The user input unit inputs the user's situation. For example, the user can input details of their patent-related concerns by talking about them. The solution proposal unit proposes solutions based on the situation input by the user input unit. For example, in response to a request for information about patent application procedures, the solution proposal unit provides specific explanations of the patent application procedure, necessary documents, important points, etc. The reminder unit manages the progress of patent applications or examinations and provides procedure reminders. For example, it notifies users that a patent application deadline is approaching. The information provision unit provides examples of patented inventions, invention methods, and the latest patent trend information. For example, it provides examples of inventions that have been patented in the past and explains methods for realizing invention ideas. As a result, the AI ​​system according to the embodiment can provide various support related to patents, streamline the patent acquisition process, and reduce user concerns.

[0066] The user input unit can infer emotions from what the user is saying in real time and suggest ways to deal with the situation based on those emotions. For example, the user input unit analyzes what the user is saying to the AI ​​in real time and infers emotions. For example, if the user is feeling anxious, it suggests ways to deal with the situation that will give the user a sense of security. It also converts what the user is saying into text using voice recognition technology and performs emotion analysis. For example, if the user is excited, it provides advice on how to stay calm. It also monitors the user's emotional state in real time and dynamically generates ways to deal with the situation based on those emotions. For example, if the user is feeling down, it provides an encouraging message. This enables more appropriate support by providing ways to deal with the situation based on the user's emotions.

[0067] The user input unit can refer to the user's past consultation history and propose solutions based on similar cases. The user input unit, for example, searches a database for the user's past consultation history and proposes solutions based on similar cases. For example, it introduces success stories of users who have had similar problems in the past. It also analyzes the user's past consultation content, extracts patterns, and proposes solutions. For example, it identifies common problems from past consultation content and provides advice based on those. It also refers to the user's past consultation history and automatically generates solutions based on similar cases. For example, it compares past consultation content with current consultation content and proposes the optimal solution. This makes it possible to provide more appropriate support by proposing solutions based on past consultation history.

[0068] The user input unit can convert what the user says into text using voice recognition technology and generate detailed solutions based on the text data. The user input unit, for example, converts what the user says into text using voice recognition technology and generates detailed solutions based on the text data. For example, it explains the specific steps for filing a patent application. It also converts what the user says into text using voice recognition technology and analyzes the text data to suggest solutions. For example, it provides a list of documents required for a patent application. It also converts what the user says into text using voice recognition technology and automatically generates detailed solutions based on the text data. For example, it provides detailed explanations of points to note when filing a patent application and examination criteria. In this way, by providing detailed solutions using voice recognition technology, user convenience is improved.

[0069] The user input unit can visualize what the user says and present solutions in a visually easy-to-understand format. The user input unit, for example, visualizes what the user says and presents solutions visually. For example, it displays the steps for filing a patent application in a flowchart. It also visualizes what the user says and shows solutions in diagrams and graphs. For example, it displays the documents required for a patent application in list format. It also visualizes what the user says and provides solutions in infographics. For example, it explains the patent application process in diagrams. This visualization makes it easier for the user to understand solutions.

[0070] The user input unit can also accommodate consultations in different languages ​​and provide solutions in multiple languages. The user input unit, for example, allows users to consult in different languages ​​and provides solutions in multiple languages. For example, patent application procedures are explained in English, French, Chinese, etc. In addition, multilingual voice recognition technology is used to translate what the user says in real time and provide solutions. For example, what the user says in Japanese is translated into English and solutions are presented. In addition, in order to accommodate consultations in different languages, a multilingual database is built and solutions are provided. For example, information on patent application procedures in various countries is provided in multiple languages. This multilingual support makes it possible to accommodate users who speak different languages.

[0071] The user input unit can use the emotion estimation function to provide relaxing music and advice according to the user's emotions. The user input unit, for example, uses the emotion estimation function to provide relaxing music according to the user's emotions. For example, if the user is feeling stressed, relaxing music is played. The unit also analyzes the user's emotional state in real time and provides advice according to the emotions. For example, if the user is feeling anxious, advice that gives a sense of security is provided. The emotion estimation function is also used to automatically generate relaxing music and advice according to the user's emotions. For example, if the user is feeling depressed, an encouraging message is provided. In this way, the user's stress is reduced by providing relaxing music and advice according to the user's emotions.

[0072] The coping method suggestion unit can consider the user's emotional state and suggest coping methods according to the emotions. The coping method suggestion unit, for example, analyzes the user's emotional state in real time and suggests coping methods according to the emotions. For example, if the user is feeling anxious, it provides coping methods that give a sense of security. Furthermore, it uses an emotion estimation function to automatically generate coping methods according to the user's emotions. For example, if the user is feeling stressed, it suggests ways to help the user relax. Furthermore, it considers the user's emotional state and dynamically generates coping methods according to the emotions. For example, if the user is feeling down, it provides an encouraging message. This allows for more appropriate support by providing coping methods according to the user's emotions.

[0073] The solution proposal unit can customize and propose solutions according to the user's level of expertise. The solution proposal unit, for example, analyzes the user's level of expertise and proposes solutions that are customized accordingly. For example, it provides patent application procedures for beginners. In addition, a system is constructed that customizes solutions based on the user's level of expertise. For example, it provides simple explanations that avoid technical jargon. In addition, it dynamically customizes solutions taking into account the user's level of expertise. For example, it provides detailed technical information to users with extensive expertise. This makes it possible to provide more appropriate support by providing solutions according to the user's level of expertise.

[0074] The solution proposal unit can predict and propose the optimal solution based on the user's past behavioral history. The solution proposal unit, for example, analyzes the user's past behavioral history and predicts and proposes the optimal solution. For example, it makes new proposals based on solutions that have been successful in the past. It also searches a database for the user's behavioral history and proposes solutions based on similar cases. For example, it compares past consultation content with current consultation content to provide the optimal solution. It also develops an algorithm to predict solutions based on the user's past behavioral history and makes suggestions. For example, it automatically generates the optimal solution based on past data. This enables more appropriate support by providing solutions based on past behavioral history.

[0075] The solution proposal unit can explain the solution using video or animation to make it easier to understand visually. The solution proposal unit, for example, explains the solution using video or animation to make it easier to understand visually. For example, the steps for filing a patent application are shown using animation. Furthermore, the solution proposal unit uses video or animation to explain the solution so that the user can visually understand it. For example, the method for preparing documents required for a patent application is shown using video. Furthermore, a system is constructed that automatically generates video or animation to make the solution easier to understand visually. For example, the patent application process is explained using animation. This provides a solution that is easy to understand visually, thereby deepening the user's understanding.

[0076] The solution proposal unit can introduce other users' success stories when proposing a solution, allowing the user to refer to them. The solution proposal unit, for example, introduces other users' success stories when proposing a solution. For example, it can introduce the cases of users who have obtained patents in the past. Furthermore, a system can be constructed that proposes solutions based on other users' success stories. For example, it can search for success stories from a database and provide them to the user. Furthermore, it can refer to other users' success stories when proposing a solution. For example, it can provide specific advice based on success stories. In this way, by referring to other users' success stories, the user's options for solutions can be expanded.

[0077] The coping method suggestion unit can use the emotion estimation function to provide encouragement and advice according to the user's emotions. The coping method suggestion unit, for example, uses the emotion estimation function to provide encouragement and advice according to the user's emotions. For example, if the user is feeling down, an encouraging message is provided. The coping method suggestion unit also analyzes the user's emotional state in real time and provides advice according to the emotions. For example, if the user is feeling anxious, advice that gives a sense of security is provided. The emotion estimation function is also used to automatically generate encouragement and advice according to the user's emotions. For example, if the user is feeling stressed, ways to relax are suggested. In this way, by providing encouragement and advice according to the user's emotions, the user's motivation is improved.

[0078] The reminding unit can select a reminding method according to the user's emotion, taking into account the user's emotional state. The reminding unit, for example, analyzes the user's emotional state in real time and selects a reminding method according to the emotion. For example, if the user is feeling stressed, the reminder is given in a gentle tone. Furthermore, the reminding unit automatically generates a reminding method according to the user's emotion using an emotion estimation function. For example, if the user is feeling anxious, the reminder is given to give a sense of security. Furthermore, the reminding unit dynamically selects a reminding method according to the user's emotion, taking into account the user's emotional state. For example, if the user is feeling depressed, an encouraging message is provided as a reminder. In this way, the reminding method according to the user's emotion is provided, thereby reducing the user's stress.

[0079] The reminder unit can automatically analyze the user's schedule and send reminders at the optimal timing. The reminder unit, for example, automatically analyzes the user's schedule and sends reminders at the optimal timing. For example, it sends a reminder when the deadline for a patent application is approaching. It also uses a schedule analysis algorithm to analyze the user's schedule and send reminders at the optimal timing. For example, it sends reminders that avoid times when the user is busy. It also predicts the optimal timing for a reminder based on the user's schedule data and sends the reminder. For example, it sends a reminder when the user is relaxing. This improves user convenience by providing reminders that suit the user's schedule.

[0080] The reminding unit can customize the reminder content in detail and provide information that is optimal for the user's situation. The reminding unit, for example, customizes the reminder content in detail and provides information that is optimal for the user's situation. For example, specific reminders are given according to the progress status of a patent application. Furthermore, a system is constructed that analyzes the user's situation and customizes the reminder content based on that analysis. For example, reminders are given according to the status of documents submitted by the user. Furthermore, the reminder content is dynamically customized to suit the user's situation. For example, reminders are provided according to the progress status of patent examination. This improves user convenience by providing reminders that are optimal for the user's situation.

[0081] The reminding unit can provide reminders by voice messages or video messages, and can provide visual and audio notifications. For example, the reminding unit can provide reminders by voice messages and provide visual and audio notifications. For example, when a patent application deadline is approaching, a reminder can be sent by voice message. Also, the reminder can be provided by video messages and provide visual and audio notifications. For example, the patent application procedures can be explained by video messages. Also, a system can be built that provides visual and audio reminders using voice messages or video messages. For example, the progress of patent examination can be notified by video messages. In this way, providing visual and audio reminders makes it easier to attract the user's attention.

[0082] The reminding unit can provide related materials and links at the same time as a reminder. The reminding unit, for example, provides related materials and links at the same time as a reminder. For example, a link related to patent application procedures is included in the remind message. In addition, a system is constructed that automatically attaches related materials and links to remind messages. For example, materials related to the progress of patent examination are attached to the remind message. In addition, related materials and links are dynamically provided at the time of a reminder. For example, templates of documents required for patent applications are included in the remind message. In this way, by providing related materials and links, the user can quickly access the information they need.

[0083] The reminder unit can use the emotion estimation function to generate a reminder message according to the user's emotion. The reminder unit, for example, uses the emotion estimation function to generate a reminder message according to the user's emotion. For example, if the user is feeling stressed, the reminder is given in a gentle tone. The reminder unit also analyzes the user's emotional state in real time and automatically generates a reminder message according to the emotion. For example, if the user is feeling anxious, a reminder that gives a sense of security is given. The reminder unit also uses the emotion estimation function to dynamically generate a reminder message according to the user's emotion. For example, if the user is feeling depressed, an encouraging message is provided as a reminder. In this way, by providing a reminder message according to the user's emotion, the user's stress is reduced.

[0084] The information providing unit can take the user's emotional state into consideration and provide invention examples and invention methods that correspond to the user's emotions. The information providing unit, for example, analyzes the user's emotional state in real time and provides invention examples and invention methods that correspond to the user's emotions. For example, if the user is feeling anxious, it introduces success stories. In addition, it uses an emotion estimation function to automatically generate invention examples and invention methods that correspond to the user's emotions. For example, if the user is feeling stressed, it suggests invention methods that will help them relax. In addition, it takes the user's emotional state into consideration and dynamically provides invention examples and invention methods that correspond to the user's emotions. For example, if the user is feeling down, it introduces encouraging invention examples. In this way, by providing invention examples and invention methods that correspond to the user's emotions, the user's motivation is improved.

[0085] The information providing unit can provide customized example inventions based on the user's interests and concerns. The information providing unit, for example, analyzes the user's interests and concerns and provides customized example inventions based on the analysis. For example, it introduces example inventions in technical fields that interest the user. It also builds a system that customizes example inventions based on the user's interests and concerns. For example, it provides example inventions related to themes that interest the user. It also dynamically provides customized example inventions taking the user's interests and concerns into consideration. For example, it introduces example inventions related to keywords that the user has searched for in the past. In this way, it is possible to provide example inventions that match the user's interests and concerns, thereby improving the user's motivation.

[0086] The information providing unit can suggest related invention examples based on the user's past search history. The information providing unit, for example, analyzes the user's past search history and suggests related invention examples based on it. For example, it introduces invention examples in technical fields that have been searched in the past. It also builds a system that searches the user's search history from a database and suggests similar invention examples. For example, it provides invention examples related to the content of past searches. It also dynamically suggests related invention examples based on the user's past search history. For example, it introduces invention examples related to keywords that have been searched in the past. This improves user convenience by providing invention examples based on past search history.

[0087] The information providing unit can explain invention examples or invention methods using videos or animations to make them easier to understand visually. The information providing unit, for example, explains invention examples or invention methods using videos or animations to make them easier to understand visually. For example, specific examples of patent inventions are shown using animations. Furthermore, video and animations are used to explain invention examples and invention methods so that users can visually understand them. For example, videos are used to show how to materialize an invention idea. Furthermore, a system is constructed that automatically generates videos and animations to make invention examples and invention methods easier to understand visually. For example, animations are used to explain how to prepare patent application documents. In this way, by providing invention examples and invention methods that are easy to understand visually, users can deepen their understanding.

[0088] The information provision unit can introduce examples of inventions from different fields and provide ideas for interdisciplinary integration. For example, the information provision unit introduces examples of inventions from different fields and provides ideas for interdisciplinary integration. For example, it introduces an idea that combines examples of inventions from the medical and IT fields. It also builds a system that proposes ideas for interdisciplinary integration based on examples of inventions from different fields. For example, it searches a database for examples of inventions from different technical fields and provides them to users. It also introduces examples of inventions from different fields and dynamically provides ideas for interdisciplinary integration. For example, it introduces examples of inventions that combine technologies from different fields. This provides ideas for interdisciplinary integration, thereby broadening the scope of users' inventions.

[0089] The information providing unit can use the emotion estimation function to provide invention examples and invention methods that correspond to the user's emotions. The information providing unit, for example, uses the emotion estimation function to provide invention examples and invention methods that correspond to the user's emotions. For example, if the user is feeling anxious, it introduces success stories. It also analyzes the user's emotional state in real time and automatically generates invention examples and invention methods that correspond to the emotions. For example, if the user is feeling stressed, it suggests invention methods that will help the user relax. It also takes the user's emotional state into consideration and dynamically provides invention examples and invention methods that correspond to the emotions. For example, if the user is feeling down, it introduces encouraging invention examples. In this way, by providing invention examples and invention methods that correspond to the user's emotions, the user's motivation is improved.

[0090] The information providing unit can provide trend information according to the emotion, taking into account the user's emotional state. The information providing unit, for example, analyzes the user's emotional state in real time and provides trend information according to the emotion. For example, if the user is feeling anxious, it provides trend information that gives a sense of security. Furthermore, it automatically generates trend information according to the user's emotion using an emotion estimation function. For example, if the user is feeling stressed, it provides trend information that helps the user relax. Furthermore, it takes the user's emotional state into account and dynamically provides trend information according to the emotion. For example, if the user is feeling depressed, it provides encouraging trend information. In this way, providing trend information according to the user's emotion improves the user's motivation.

[0091] The information providing unit can provide customized trend information based on the user's interests and concerns. The information providing unit, for example, analyzes the user's interests and concerns and provides customized trend information based on the analysis. For example, trend information on technical fields in which the user is interested is provided. A system is also constructed that customizes trend information based on the user's interests and concerns. For example, trend information related to themes in which the user is interested is provided. Customized trend information is also dynamically provided taking the user's interests and concerns into consideration. For example, trend information related to keywords that the user has searched for in the past is provided. This improves the user's motivation by providing trend information that matches the user's interests and concerns.

[0092] The information providing unit can suggest related trend information based on the user's past search history. The information providing unit, for example, analyzes the user's past search history and suggests related trend information based on it. For example, it provides trend information in technical fields previously searched. It also builds a system that searches a database for the user's search history and suggests similar trend information. For example, it provides trend information related to the content of past searches. It also dynamically suggests related trend information based on the user's past search history. For example, it provides trend information related to keywords previously searched. This improves user convenience by providing trend information based on past search history.

[0093] The information providing unit can provide trend information in the form of infographics or visual notes to make it easier to understand visually. The information providing unit, for example, provides trend information in the form of infographics to make it easier to understand visually. For example, trends in patent technology are shown in graphs or diagrams. Also, the information providing unit provides trend information in the form of visual notes to make it easier to understand visually. For example, trends in patent technology are explained using diagrams. Also, to make trend information easier to understand visually, a system is constructed that automatically generates infographics and visual notes. For example, trends in patent technology are shown in infographics. This provides trend information that is easy to understand visually, thereby deepening the user's understanding.

[0094] The information providing unit can compare trend information from different industries and fields and provide comprehensive insights. For example, the information providing unit compares trend information from different industries and fields and provides comprehensive insights. For example, it compares patent technology trends in the IT and medical fields. In addition, a system is built that provides comprehensive insights based on trend information from different industries. For example, it searches a database for trend information from different technology fields and provides it to users. In addition, it compares trend information from different industries and fields and dynamically provides comprehensive insights. For example, it compares and shows technology trends in different fields. In this way, it is possible to provide comprehensive insights to users by comparing trend information from different industries and fields.

[0095] The information providing unit can use the emotion estimation function to provide trend information according to the user's emotion. The information providing unit, for example, uses the emotion estimation function to provide trend information according to the user's emotion. For example, if the user is feeling anxious, trend information that gives a sense of security is provided. Furthermore, the information providing unit analyzes the user's emotional state in real time and automatically generates trend information according to the emotion. For example, if the user is feeling stressed, trend information that helps the user to relax is provided. Furthermore, trend information according to the emotion is dynamically provided taking the user's emotional state into consideration. For example, if the user is feeling depressed, encouraging trend information is provided. In this way, by providing trend information according to the user's emotion, the user's motivation is improved.

[0096] The information providing unit can provide foreign patent information according to the emotion, taking into account the user's emotional state. The information providing unit, for example, analyzes the user's emotional state in real time and provides foreign patent information according to the emotion. For example, if the user is feeling anxious, it provides foreign patent information that gives a sense of security. Furthermore, it uses an emotion estimation function to automatically generate foreign patent information according to the user's emotion. For example, if the user is feeling stressed, it provides foreign patent information that helps the user relax. Furthermore, it takes into account the user's emotional state and dynamically provides foreign patent information according to the emotion. For example, if the user is feeling down, it provides encouraging foreign patent information. In this way, providing foreign patent information according to the user's emotion improves the user's motivation.

[0097] The information providing unit can provide customized foreign patent information based on the user's interests and concerns. The information providing unit, for example, analyzes the user's interests and concerns and provides customized foreign patent information based on the analysis. For example, it provides patent information on countries in which the user is interested. In addition, a system is constructed that customizes foreign patent information based on the user's interests and concerns. For example, it provides foreign patent information related to themes in which the user is interested. In addition, it dynamically provides customized foreign patent information taking the user's interests and concerns into consideration. For example, it provides foreign patent information related to keywords that the user has searched for in the past. In this way, providing foreign patent information that matches the user's interests and concerns improves the user's motivation.

[0098] The information providing unit can suggest related foreign patent information based on the user's past search history. The information providing unit, for example, analyzes the user's past search history and suggests related foreign patent information based on that. For example, it provides patent information from countries previously searched. It also builds a system that searches a database for the user's search history and suggests similar foreign patent information. For example, it provides foreign patent information related to the content of past searches. It also dynamically suggests related foreign patent information based on the user's past search history. For example, it provides foreign patent information related to keywords previously searched. This improves user convenience by providing foreign patent information based on past search history.

[0099] The information providing unit can provide foreign patent information in the form of infographics or visual notes to make it easier to understand visually. The information providing unit, for example, provides foreign patent information in the form of infographics to make it easier to understand visually. For example, the patent application procedures of each country are shown in graphs and diagrams. The information providing unit also provides foreign patent information in the form of visual notes to make it easier to understand visually. For example, the patent examination standards of each country are explained in diagrams. In addition, a system is constructed to automatically generate infographics and visual notes to make foreign patent information easier to understand visually. For example, changes to patent laws of each country are shown in infographics. This provides foreign patent information that is easier to understand visually, thereby deepening the user's understanding.

[0100] The information providing unit can compare patent information from different countries and provide comprehensive insights. The information providing unit, for example, compares patent information from different countries and provides comprehensive insights. For example, it compares patent application procedures between the United States and Japan. It also builds a system that provides comprehensive insights based on patent information from different countries. For example, it searches a database for patent examination standards from each country and provides them to users. It also compares patent information from different countries and dynamically provides comprehensive insights. For example, it compares and shows changes to patent laws in each country. This makes it possible to provide comprehensive insights to users by comparing patent information from different countries.

[0101] The information providing unit can use the emotion estimation function to provide foreign patent information according to the user's emotion. The information providing unit, for example, uses the emotion estimation function to provide foreign patent information according to the user's emotion. For example, if the user is feeling anxious, it provides foreign patent information that gives a sense of security. Furthermore, it analyzes the user's emotional state in real time and automatically generates foreign patent information according to the emotion. For example, if the user is feeling stressed, it provides foreign patent information that helps the user relax. Furthermore, it takes the user's emotional state into consideration and dynamically provides foreign patent information according to the emotion. For example, if the user is feeling down, it provides encouraging foreign patent information. In this way, providing foreign patent information according to the user's emotion improves the user's motivation.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The user input unit can estimate the emotions of what the user is saying in real time and suggest ways to deal with the situation based on those emotions. For example, if the user is feeling anxious, it will suggest ways to deal with the situation that will give the user a sense of security. It also converts what the user is saying into text using speech recognition technology and performs emotion analysis. For example, if the user is excited, it will provide advice on how to stay calm. It also monitors the user's emotional state in real time and dynamically generates ways to deal with the situation based on those emotions. For example, if the user is feeling down, it will provide an encouraging message. This allows for more appropriate support by providing ways to deal with the situation based on the user's emotions.

[0104] The user input unit can refer to the user's past consultation history and suggest solutions based on similar cases. For example, it can introduce success stories of users who have had similar problems in the past. It can also analyze the user's past consultation content, extract patterns, and suggest solutions. For example, it can identify common problems from past consultation content and provide advice based on them. It can also refer to the user's past consultation history and automatically generate solutions based on similar cases. For example, it can compare past consultation content with the current consultation content and suggest the optimal solution. This makes it possible to provide more appropriate support by suggesting solutions based on past consultation history.

[0105] The user input unit can convert what the user says into text using voice recognition technology and generate detailed solutions based on the text data. For example, it can explain the specific steps for filing a patent application. It can also convert what the user says into text using voice recognition technology and analyze the text data to suggest solutions. For example, it can provide a list of documents required for a patent application. It can also convert what the user says into text using voice recognition technology and automatically generate detailed solutions based on the text data. For example, it can provide detailed explanations of points to note when filing a patent application and examination criteria. This improves user convenience by providing detailed solutions using voice recognition technology.

[0106] The user input unit can visualize what the user says and present solutions in a visually easy-to-understand format. For example, it can display the steps for filing a patent application in a flowchart. It can also visualize what the user says and show solutions in diagrams and graphs. For example, it can display the documents required for a patent application in list format. It can also visualize what the user says and provide solutions in the form of infographics. For example, it can explain the patent application process in diagrams. This visualization makes it easier for users to understand solutions.

[0107] The user input unit can also accommodate consultations in different languages ​​and provide solutions in multiple languages. For example, it allows users to consult in different languages ​​and provides solutions in multiple languages. For example, patent application procedures are explained in English, French, Chinese, etc. In addition, multilingual voice recognition technology is used to translate what the user says in real time and provide solutions. For example, what the user says in Japanese is translated into English and solutions are presented. In addition, in order to accommodate consultations in different languages, a multilingual database is built and solutions are provided. For example, information on patent application procedures in various countries is provided in multiple languages. This multilingual support makes it possible to accommodate users who speak different languages.

[0108] The user input unit can use the emotion estimation function to provide relaxing music and advice according to the user's emotions. For example, the emotion estimation function is used to provide relaxing music according to the user's emotions. For example, if the user is feeling stressed, relaxing music is played. The unit also analyzes the user's emotional state in real time and provides advice according to the emotions. For example, if the user is feeling anxious, advice that gives a sense of security is provided. The emotion estimation function is also used to automatically generate relaxing music and advice according to the user's emotions. For example, if the user is feeling depressed, an encouraging message is provided. In this way, the user's stress is reduced by providing relaxing music and advice according to the user's emotions.

[0109] The coping method suggestion unit can consider the user's emotional state and suggest coping methods according to the emotion. For example, it analyzes the user's emotional state in real time and suggests coping methods according to the emotion. For example, if the user is feeling anxious, it provides coping methods that give a sense of security. It also uses an emotion estimation function to automatically generate coping methods according to the user's emotion. For example, if the user is feeling stressed, it suggests ways to help the user relax. It also considers the user's emotional state and dynamically generates coping methods according to the emotion. For example, if the user is feeling down, it provides an encouraging message. This makes it possible to provide more appropriate support by providing coping methods according to the user's emotion.

[0110] The solution suggestion unit can customize and suggest solutions according to the user's level of expertise. For example, it analyzes the user's level of expertise and customizes and suggests solutions according to that level. For example, it provides patent application procedures for beginners. It also builds a system that customizes solutions based on the user's level of expertise. For example, it provides simple explanations that avoid technical jargon. It also dynamically customizes solutions taking into account the user's level of expertise. For example, it provides detailed technical information to users with extensive expertise. This allows for more appropriate support by providing solutions according to the user's level of expertise.

[0111] The solution suggestion unit can predict and suggest the optimal solution based on the user's past behavioral history. For example, it analyzes the user's past behavioral history to predict and suggest the optimal solution. For example, it makes new suggestions based on solutions that have been successful in the past. It also searches a database for the user's behavioral history to suggest solutions based on similar cases. For example, it compares past consultation content with the current consultation content to provide the optimal solution. It also develops an algorithm to predict solutions based on the user's past behavioral history and makes suggestions. For example, it automatically generates the optimal solution based on past data. This makes it possible to provide more appropriate support by providing solutions based on past behavioral history.

[0112] The solution suggestion unit can explain the solution using video or animation to make it easier to understand visually. For example, the solution can be explained using video or animation to make it easier to understand visually. For example, the steps for filing a patent application can be shown using animation. Furthermore, the solution can be explained using video or animation so that the user can visually understand it. For example, a video can be shown to show how to prepare the documents required for a patent application. Furthermore, a system can be built that automatically generates video or animation to make the solution easier to understand visually. For example, the patent application process can be explained using animation. This can provide a solution that is easy to understand visually, thereby deepening the user's understanding.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The user input unit inputs the user's situation. For example, the user can input the details of their concerns about patents. Step 2: The solution suggestion unit proposes a solution based on the situation input by the user input unit. For example, in response to a request to learn about patent application procedures, the unit provides a detailed explanation of the patent application procedures, necessary documents, points to note, etc. Step 3: The Reminders Department manages the progress of patent applications and examinations and provides procedural reminders, such as notifying users that patent application deadlines are approaching. Step 4: The information provider provides examples of patented inventions, invention methods, and the latest patent trend information. For example, they provide examples of inventions that have been patented in the past and explain how to materialize invention ideas.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] In the robot 414, 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 robot 414 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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. [Explanation of symbols]

[0182] 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 user input unit for inputting a user's situation; a solution proposal unit that proposes a solution based on the situation input by the user input unit; A reminder department that manages the progress of patent applications or examinations and reminds applicants of procedures; and an information providing unit that provides examples of patent inventions or invention methods, and the latest patent trend information. A system characterized by:

2. The user input unit Inferring the emotions of what the user is saying in real time and suggesting ways to deal with the emotion 2. The system of claim 1.

3. The user input unit Refer to the user's past consultation history and suggest solutions based on similar cases 2. The system of claim 1.

4. The user input unit What the user says is converted into text using voice recognition technology, and detailed solutions are generated based on the text data.

2. The system of claim 1.

5. The user input unit Visualize what the user is saying and present solutions in a visually easy-to-understand format 2. The system of claim 1.

6. The user input unit We also handle consultations in different languages ​​and provide solutions in multiple languages.

2. The system of claim 1.

7. The user input unit Providing relaxing music and advice according to the user's emotions 2. The system of claim 1.

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