System

Abstract

An object of the system according to the embodiment is to efficiently collect and search for information in an organization and provide an idea that creates synergy.SOLUTION: A system includes a collection unit, a search unit, a generation unit, and a proposal unit. The collection unit collects information. The search unit makes the information collected by the collection unit searchable. The generation unit analyzes the information retrieved by the retrieval unit and generates an idea for creating synergy. The suggestion unit provides the idea generated by the generation unit to the user.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 technologies result in insufficient information sharing within an organization, which can lead to duplication of business considerations and a lack of synergy.

[0005] The system according to the embodiment aims to efficiently collect and search information within an organization and provide ideas that create synergy. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a search unit, a generation unit, and a proposal unit. The collection unit collects information. The search unit makes the information collected by the collection unit searchable. The generation unit analyzes the information searched by the search unit and generates ideas that create synergy. The proposal unit provides the ideas generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and search information within an organization and provide ideas for creating synergy. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business information sharing system according to an embodiment of the present invention collects information, makes it searchable, and uses a generative AI to generate ideas for creating synergies and provide them to users. The business information sharing system registers business information being considered by each department in a database and sets the scope of information sharing. The registered information is then made searchable, allowing users to search by keyword or category to obtain the information they need. Furthermore, the system uses a generative AI to generate and output possibilities and ideas for creating synergies between the business being considered by each department and businesses being considered by other departments. For example, the business information sharing system registers business information being considered by each department in a database. The scope of information sharing is set to prevent confidential information from leaking. For example, project overviews, progress, and contact information for related parties can be registered. The business information sharing system then makes the registered information searchable. Users can easily obtain the information they need by searching by keyword or category. For example, users can obtain related business information by searching for keywords such as "new product development" or "market research." The business information sharing system also uses a generative AI to generate and output possibilities and ideas for creating synergies between the business being considered by each department and businesses being considered by other departments. Generative AI analyzes registered business information and combines related information to propose new ideas. For example, by combining a product being developed in one department with a project underway in another department, new business opportunities can be discovered. This allows the business information sharing system to improve the efficiency of business considerations and speed up business growth. This allows the business information sharing system to share business consideration information within an organization and increase its efficiency. For example, instead of each department conducting business considerations independently, overlapping considerations can be avoided by utilizing information from other departments. Furthermore, using generative AI can create new synergies and speed up business growth. For example, departments developing products targeting the same market can work together to create more effective marketing strategies.

[0029] A business information sharing system according to an embodiment includes a collection unit, a search unit, a generation unit, and a proposal unit. The collection unit collects business information being considered by each department. For example, the collection unit registers the business information being considered by each department in a database to the extent that it can be shared. The collection unit can register, for example, project overviews, progress, and contact information of relevant parties. The collection unit can collect information by methods such as web scraping, data acquisition through an API, and manual input. The search unit makes the information collected by the collection unit searchable. The search unit enables searches by, for example, keywords, categories, or natural language. The search unit can make information searchable by methods such as indexing, full-text search, and metadata search. The generation unit uses a generation AI to analyze the information searched by the search unit and generate ideas that create synergy. For example, the generation AI analyzes registered business information and combines related information to generate new ideas. The generation AI generates ideas using techniques such as machine learning models and neural networks. The proposal unit provides the ideas generated by the generation unit to a user. The proposal unit can provide the generated ideas to the user by, for example, notifying the user, displaying a dashboard, sending an email, etc. As a result, the business information sharing system according to the embodiment can improve the efficiency of business considerations and create synergies through the collection, search, analysis, and proposal of information.

[0030] The collection unit can register business information being considered by each department in the database to the extent that it can be shared. For example, the collection unit registers business information being considered by each department in the database to the extent that it can be shared. For example, the collection unit can register project overviews, progress status, and contact information for related parties. For example, the collection unit sets the scope of information sharing to prevent confidential information from leaking. The scope of sharing includes, for example, access rights and data confidentiality. This makes it easier to share information by registering business information from each department in the database. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automatically collect business information being considered by each department using AI and register it in the database.

[0031] The search unit can enable searches by keyword, category, or natural language. The search unit can enable searches by keyword, category, or natural language, for example. The search unit can make information searchable by methods such as indexing, full-text search, and metadata search. Searches by keyword, category, or natural language include, for example, keyword matching, category classification, and natural language processing technology. This allows users to easily obtain the information they need by providing a variety of search methods. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can use AI to perform searches by keyword, category, or natural language to obtain the required information.

[0032] The generation unit can use the generation AI to analyze registered business information and combine related information to generate new ideas. The generation unit, for example, uses the generation AI to analyze registered business information and combine related information to generate new ideas. The generation AI generates ideas using technologies such as machine learning models and neural networks. The generation AI, for example, analyzes registered business information and combines related information based on common keywords, correlations, etc. In this way, new ideas with high relevance can be generated using the generation AI. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit inputs business information to the generation AI, and the generation AI combines related information to generate new ideas.

[0033] The suggestion unit can provide the generated idea to the user. The suggestion unit, for example, provides the generated idea to the user. The suggestion unit can provide the generated idea to the user by, for example, notifying the user, displaying a dashboard, sending an email, etc. Methods of providing the idea to the user include, for example, notifying the user, displaying a dashboard, sending an email, etc. In this way, by providing the generated idea to the user, the efficiency of business considerations is improved. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can provide the generated idea to the user by using AI.

[0034] The suggestion unit can receive feedback from the user. The suggestion unit receives, for example, feedback from the user. The suggestion unit can receive feedback by methods such as questionnaires, comments, and evaluations. Examples of feedback include questionnaires, comments, and evaluations. This allows the system to be improved by receiving feedback from the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can automatically collect feedback from users using AI and reflect it in improving the system.

[0035] The search unit can display search results in ranking. The search unit, for example, displays the search results in ranking. The search unit can display the search results in ranking, for example, using a method such as scoring or a ranking algorithm. Ranking display includes, for example, scoring and a ranking algorithm. In this way, the search results are displayed in ranking, allowing the user to quickly grasp important information. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can score search results using AI and display them in ranking.

[0036] The collection unit can analyze each department's past information collection history and select an appropriate collection method. For example, the collection unit can analyze each department's past information collection history and identify the most effective collection method. For example, the collection unit can prioritize collection of frequently used information sources from each department's information collection history. For example, the collection unit can customize the collection method based on each department's information collection history to collect information efficiently. The past information collection history includes, for example, the collection date and time, the collection content, and the collection frequency. This allows the optimal collection method to be selected by analyzing the past information collection history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can use AI to analyze each department's past information collection history and select the optimal collection method.

[0037] The collection unit may filter information based on each department's current projects and areas of interest when collecting information. For example, the collection unit may filter information based on each department's current projects and areas of interest when collecting information. For example, the collection unit may collect only information related to each department's current projects. For example, the collection unit may preferentially collect highly relevant information based on each department's areas of interest. For example, the collection unit may filter and collect necessary information based on the progress of each department's projects. Current projects and areas of interest include, for example, project names and field categories. Thus, highly relevant information can be collected by filtering information based on each department's projects and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may use AI to filter information based on each department's current projects and areas of interest.

[0038] The collection unit can select an appropriate collection means according to the input method of each department when collecting information. For example, the collection unit selects the optimal collection means according to the input method (voice, text, image, etc.) of each department when collecting information. For example, if each department uses voice input, the collection unit can prioritize collecting voice data. For example, if each department uses text input, the collection unit can also prioritize collecting text data. For example, if each department uses image input, the collection unit can also prioritize collecting image data. Input methods include, for example, voice input, text input, image input, etc. This enables efficient information collection by selecting the optimal collection means according to the input method of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to select the optimal collection means according to the input method of each department.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each department. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of each department. For example, the collection unit prioritizes collecting nearby information based on the geographical location information of each department. For example, the collection unit can also collect region-specific information by taking into account the geographical location information of each department. For example, the collection unit can also filter and collect highly relevant information based on the geographical location information of each department. Geographical location information includes, for example, GPS data, address information, etc. In this way, highly relevant information can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information by using AI and taking into account the geographical location information of each department.

[0040] The collection unit can analyze the social media activities of each department when collecting information and collect relevant information. For example, the collection unit can analyze the social media activities of each department when collecting information and collect relevant information. For example, the collection unit can analyze the social media activities of each department and collect relevant information. For example, the collection unit can also collect highly relevant information based on the content of posts on social media by each department. For example, the collection unit can also collect relevant information by referring to the activities of each department's friends on social media. Social media activities include, for example, post content and engagement. In this way, highly relevant information can be collected by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the social media activities of each department using AI and collect relevant information.

[0041] The collection unit can customize the collection method by reflecting past feedback from each department when collecting information. For example, the collection unit customizes the collection method by reflecting past feedback from each department when collecting information. The collection unit customizes the collection method based on, for example, past feedback from each department. The collection unit can also adjust the type of information to be collected by reflecting past feedback from each department. For example, the collection unit can also optimize the collection means by referring to past feedback from each department. Past feedback includes, for example, user comments, evaluation results, etc. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze past feedback from each department using AI to customize the collection method.

[0042] The search unit can adjust the level of detail of search results based on the importance of information during a search. The search unit, for example, adjusts the level of detail of search results based on the importance of information during a search. For example, the search unit prioritizes displaying information with high importance and provides detailed information. For example, the search unit can also briefly display information with low importance and display more details as needed. For example, the search unit can adjust the display order of search results based on the importance of information. The importance of information includes, for example, scoring, ranking, etc. In this way, by adjusting the level of detail of search results based on the importance of information, necessary information can be quickly obtained. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can evaluate the importance of information using AI and adjust the level of detail of search results.

[0043] The search unit can apply different search algorithms depending on the category of information during a search. For example, the search unit applies different search algorithms depending on the category of information during a search. For example, the search unit applies a search algorithm specialized for the product category when searching for product information. For example, the search unit can apply a search algorithm specialized for the market category when searching for market research information. For example, the search unit can apply a search algorithm specialized for the project category when searching for project information. Information categories include, for example, topic classification and industry classification. Search algorithms include, for example, TF-IDF and BM25. By applying a search algorithm depending on the category of information, search accuracy is improved. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can apply a search algorithm depending on the category of information using AI.

[0044] The search unit can improve search accuracy by referring to the user's past search history when searching. The search unit can improve search accuracy by referring to the user's past search history, for example. The search unit can prioritize displaying highly relevant information based on the user's past search history, for example. The search unit can also analyze the user's past search history and optimize the search algorithm, for example. The search unit can also adjust the display order of search results by referring to the user's past search history, for example. The past search history includes, for example, search keywords, search date and time, etc. Thus, by referring to the past search history, search accuracy is improved. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can analyze the user's past search history using AI to improve search accuracy.

[0045] The search unit can determine the priority of search results based on the time of information submission during a search. The search unit, for example, determines the priority of search results based on the time of information submission during a search. For example, the search unit prioritizes displaying the most recent information and postpones older information. The search unit can also adjust the display order of search results based on the time of information submission, for example. The search unit can also group and display information that was submitted recently. The time of information submission includes, for example, the submission date and time, the update date and time, etc. In this way, by determining the priority of search results based on the time of information submission, the most recent information can be preferentially obtained. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can evaluate the time of information submission using AI to determine the priority of search results.

[0046] The search unit can adjust the order of search results based on the relevance of information during a search. The search unit, for example, adjusts the order of search results based on the relevance of information during a search. For example, the search unit prioritizes displaying highly relevant information and postpones displaying less relevant information. The search unit can also adjust the display order of search results based on the relevance of information. For example, the search unit can group and display highly relevant information. The relevance of information includes, for example, common keywords, correlations, etc. In this way, by adjusting the order of search results based on the relevance of information, highly relevant information can be preferentially obtained. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can evaluate the relevance of information using AI and adjust the order of search results.

[0047] The search unit can adjust the use of technical terms in search results according to the user's level of expertise during a search. For example, the search unit can adjust the use of technical terms in search results according to the user's level of expertise during a search. For example, if the user's level of expertise is high, the search unit can display search results that use a lot of technical terms. For example, if the user's level of expertise is low, the search unit can also display concise and easy-to-understand search results. The search unit can also adjust the display content of search results according to the user's level of expertise. The user's level of expertise includes, for example, survey results, past search history, etc. The use of technical terms includes, for example, definitions of technical terms, simplification of terms, etc. This allows the use of technical terms in search results according to the user's level of expertise to be adjusted, thereby providing easy-to-understand search results. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without AI. For example, the search unit can use AI to evaluate the user's level of expertise and adjust the use of technical terms in search results.

[0048] The generation unit can improve the accuracy of generation by taking into account the interrelationships of information during generation. The generation unit, for example, improves the accuracy of generation by taking into account the interrelationships of information during generation. The generation unit, for example, analyzes the interrelationships of information and generates ideas by combining highly related information. The generation unit can also generate optimal ideas by taking into account the interrelationships of information. The generation unit can also generate ideas with high synergy effects based on the interrelationships of information. Examples of the interrelationships of information include co-occurrence networks and correlation analysis. As a result, the accuracy of generation is improved by taking the interrelationships of information into consideration. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the interrelationships of information into the generation AI, and the generation AI generates ideas.

[0049] The generation unit can generate ideas taking into account the attribute information of the information submitter when generating the ideas. For example, the generation unit generates ideas taking into account the attribute information of the information submitter when generating the ideas. For example, the generation unit generates optimal ideas taking into account the expertise level of the information submitter. For example, the generation unit can also generate highly relevant ideas based on the past performance of the information submitter. For example, the generation unit can also generate ideas with high synergy effects taking into account the attribute information of the information submitter. The attribute information of the submitter includes, for example, job position, field of expertise, etc. In this way, highly relevant ideas can be generated by taking into account the attribute information of the information submitter. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the attribute information of the information submitter into the generation AI, which then generates ideas.

[0050] The generation unit can weight the generation based on the frequency of information submission during generation. For example, the generation unit weights the generation based on the frequency of information submission during generation. For example, if the frequency of information submission is high, the generation unit generates ideas by prioritizing that information. For example, if the frequency of information submission is low, the generation unit can also generate ideas by using that information as a complement. For example, the generation unit can determine the priority of ideas to be generated based on the frequency of information submission. The frequency of information submission includes, for example, the number of submissions and the submission interval. The weighting of generation includes, for example, an importance score and a frequency score. In this way, by weighting the generation based on the frequency of information submission, ideas that prioritize important information can be generated. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the frequency of information submission into the generation AI, and the generation AI weights the ideas.

[0051] The generation unit can generate ideas taking into account the geographical distribution of information during generation. For example, the generation unit generates ideas taking into account the geographical distribution of information during generation. For example, the generation unit generates region-specific ideas based on the geographical distribution of information. For example, the generation unit can also generate optimal ideas taking into account the geographical distribution of information. For example, the generation unit can also generate ideas with high synergy effects based on the geographical distribution of information. The geographical distribution includes, for example, regional data, map information, etc. In this way, region-specific ideas can be generated by taking into account the geographical distribution of information. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the geographical distribution of information into the generation AI, which then generates ideas.

[0052] The generation unit can improve the accuracy of generation by referring to literature related to the information during generation. The generation unit, for example, improves the accuracy of generation by referring to literature related to the information during generation. The generation unit, for example, refers to literature related to the information to generate highly relevant ideas. The generation unit can also generate optimal ideas based on literature related to the information. The generation unit can also generate ideas with high synergy effects by referring to literature related to the information. Related literature includes, for example, academic papers, patent documents, etc. As a result, the accuracy of generation is improved by referring to related literature. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs literature related to the information into the generation AI, which generates ideas.

[0053] The generation unit can generate ideas taking into account the market value of the information when generating them. For example, the generation unit generates ideas taking into account the market value of the information when generating them. For example, the generation unit generates the most valuable idea based on the market value of the information. For example, the generation unit can also generate optimal ideas taking into account the market value of the information. For example, the generation unit can also generate ideas with high synergy effects based on the market value of the information. Market value includes, for example, market research data, sales forecasts, etc. In this way, valuable ideas can be generated by taking into account the market value of the information. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the market value of the information into the generation AI, which then generates ideas.

[0054] The suggestion unit can adjust the level of detail of the proposal based on the importance of the generated idea when making a suggestion. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the generated idea when making a suggestion. For example, the suggestion unit can prioritize suggesting ideas with high importance and provide detailed information. For example, the suggestion unit can briefly suggest ideas with low importance and provide details as needed. For example, the suggestion unit can adjust the display order of proposals based on the importance of the ideas. The importance of ideas includes, for example, scoring, ranking, etc. The level of detail of the proposal includes, for example, summary display, full-text display, etc. In this way, by adjusting the level of detail of the proposal based on the importance of the idea, important ideas can be prioritized for suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can evaluate the importance of ideas using AI and adjust the level of detail of the proposal.

[0055] The suggestion unit can apply different suggestion algorithms depending on the category of the generated idea when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the generated idea when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm specialized for a product category when proposing a product development idea. For example, the suggestion unit can apply a suggestion algorithm specialized for a market category when proposing a market research idea. For example, the suggestion unit can apply a suggestion algorithm specialized for a project category when proposing a project idea. Idea categories include, for example, topic classification, industry classification, etc. Proposal algorithms include, for example, recommendation systems, collaborative filtering, etc. As a result, by applying a suggestion algorithm depending on the category of the idea, the accuracy of the suggestion is improved. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can apply a suggestion algorithm depending on the category of the idea using AI.

[0056] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit prioritizes suggesting highly relevant ideas based on the user's past suggestion results. For example, the suggestion unit can also analyze the user's past suggestion results and optimize the suggestion algorithm. For example, the suggestion unit can also adjust the display order of the suggestion by referring to the user's past suggestion results. The past suggestion results include, for example, the acceptance rate of the suggestion, feedback content, etc. In this way, the accuracy of the suggestion is improved by referring to the past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can analyze the user's past suggestion results using AI to improve the accuracy of the suggestion.

[0057] The suggestion unit can determine the priority of the proposals based on the submission dates of the generated ideas at the time of proposal. For example, the suggestion unit determines the priority of the proposals based on the submission dates of the generated ideas at the time of proposal. For example, the suggestion unit preferentially proposes the most recent ideas and postpones older ideas. The suggestion unit can also adjust the display order of the proposals based on the submission dates of the ideas. For example, the suggestion unit can group and propose ideas that are submitted close in time. The submission dates of ideas include, for example, the submission date and time, the update date and time, etc. In this way, by determining the priority of the proposals based on the submission dates of the ideas, the most recent ideas can be preferentially proposed. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can use AI to evaluate the submission dates of ideas and determine the priority of the proposals.

[0058] The suggestion unit can adjust the order of suggestions based on the relevance of the generated ideas when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the generated ideas when making suggestions. For example, the suggestion unit preferentially suggests highly relevant ideas and postpones less relevant ideas. The suggestion unit can also adjust the display order of suggestions based on the relevance of the ideas. For example, the suggestion unit can group highly relevant ideas and suggest them. The relevance of ideas includes, for example, common keywords, correlations, etc. In this way, by adjusting the order of suggestions based on the relevance of ideas, highly relevant ideas can be preferentially suggested. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can evaluate the relevance of ideas using AI and adjust the order of suggestions.

[0059] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise. For example, the suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user's level of expertise is high, the suggestion unit may provide a proposal that uses a lot of technical terms. For example, if the user's level of expertise is low, the suggestion unit may provide a concise and easy-to-understand proposal. The suggestion unit may also adjust the display content of the proposal according to the user's level of expertise. The user's level of expertise may include, for example, survey results, past search history, etc. The use of technical terms may include, for example, definitions of technical terms, simplification of terms, etc. By adjusting the use of technical terms in the proposal according to the user's level of expertise, it is possible to provide an easy-to-understand proposal. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may use AI to evaluate the user's level of expertise and adjust the use of technical terms in the proposal.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When collecting business information from each department, the collection department can evaluate the reliability of the information and prioritize collecting highly reliable information. For example, the collection department can evaluate the reliability of the information source and provider, and register highly reliable information in the database on a priority basis. The collection department can also score the reliability of the information and prioritize collecting information with a high score. Furthermore, the collection department can identify highly reliable information sources based on past information provision history and prioritize collecting information. In this way, the collection department can improve the information quality of the database by prioritizing the collection of highly reliable information.

[0062] The search unit can analyze the user's search history and personalize search results based on the user's search patterns. For example, the search unit can analyze keywords and categories that the user has searched for in the past and prioritize displaying highly relevant information. The search unit can also provide optimal search results by taking into account the user's search frequency and search time period. Furthermore, the search unit can predict and suggest information that the user is likely to be interested in based on the user's search history. This allows the search unit to utilize the user's search history to provide more personalized search results.

[0063] The generation department can use the generation AI to analyze trends in business information and generate ideas based on those trends. For example, the generation department can analyze past business information and identify current trends. The generation department can also predict future business opportunities based on those trends and generate ideas based on those. Furthermore, the generation department can generate ideas that create synergies with other departments based on trend information. In this way, the generation department can improve the competitiveness of the business by generating ideas based on trends.

[0064] The suggestion unit can collect user feedback in real time and immediately improve the suggestion content. For example, the suggestion unit can request feedback from the user immediately after receiving a suggestion and improve the suggestion content based on the feedback. The suggestion unit can also analyze user feedback, identify common areas for improvement, and reflect them in the next suggestion. Furthermore, the suggestion unit can adjust the display and notification methods of the suggestion based on the feedback. In this way, the suggestion unit can utilize user feedback to provide more effective suggestions.

[0065] The suggestion unit can adjust the order of suggestions based on the relevance of the generated ideas when making suggestions. For example, the suggestion unit can preferentially suggest highly relevant ideas and postpone less relevant ideas. The suggestion unit can also adjust the display order of suggestions based on the relevance of the ideas. Furthermore, the suggestion unit can group highly relevant ideas and suggest them. In this way, the suggestion unit can preferentially suggest highly relevant ideas by adjusting the order of suggestions based on the relevance of the ideas.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection department collects business information being considered by each department. For example, the collection department registers the business information being considered by each department in a database to the extent that it can be shared. For example, the collection department can register project overviews, progress, contact information for stakeholders, etc. The collection department can collect information by methods such as web scraping, data acquisition through APIs, and manual input. Step 2: The search unit makes the information collected by the collection unit searchable. The search unit enables searches by keyword, category, or natural language, for example. The search unit can make the information searchable by methods such as indexing, full-text search, and metadata search. Step 3: The generation unit uses the generation AI to analyze the information retrieved by the search unit and generate ideas that create synergy. For example, the generation unit uses the generation AI to analyze registered business information and combine related information to generate new ideas. The generation AI generates ideas using technologies such as machine learning models and neural networks. Step 4: The suggestion unit provides the idea generated by the generation unit to the user. The suggestion unit can provide the generated idea to the user by, for example, notifying the user, displaying it on a dashboard, sending an email, or the like.

[0068] (Example 2) A business information sharing system according to an embodiment of the present invention collects information, makes it searchable, and uses a generative AI to generate ideas for creating synergies and provide them to users. The business information sharing system registers business information being considered by each department in a database and sets the scope of information sharing. The registered information is then made searchable, allowing users to search by keyword or category to obtain the information they need. Furthermore, the system uses a generative AI to generate and output possibilities and ideas for creating synergies between the business being considered by each department and businesses being considered by other departments. For example, the business information sharing system registers business information being considered by each department in a database. The scope of information sharing is set to prevent confidential information from leaking. For example, project overviews, progress, and contact information for related parties can be registered. The business information sharing system then makes the registered information searchable. Users can easily obtain the information they need by searching by keyword or category. For example, users can obtain related business information by searching for keywords such as "new product development" or "market research." The business information sharing system also uses a generative AI to generate and output possibilities and ideas for creating synergies between the business being considered by each department and businesses being considered by other departments. Generative AI analyzes registered business information and combines related information to propose new ideas. For example, by combining a product being developed in one department with a project underway in another department, new business opportunities can be discovered. This allows the business information sharing system to improve the efficiency of business considerations and speed up business growth. This allows the business information sharing system to share business consideration information within an organization and increase its efficiency. For example, instead of each department conducting business considerations independently, overlapping considerations can be avoided by utilizing information from other departments. Furthermore, using generative AI can create new synergies and speed up business growth. For example, departments developing products targeting the same market can work together to create more effective marketing strategies.

[0069] A business information sharing system according to an embodiment includes a collection unit, a search unit, a generation unit, and a proposal unit. The collection unit collects business information being considered by each department. For example, the collection unit registers the business information being considered by each department in a database to the extent that it can be shared. The collection unit can register, for example, project overviews, progress, and contact information of relevant parties. The collection unit can collect information by methods such as web scraping, data acquisition through an API, and manual input. The search unit makes the information collected by the collection unit searchable. The search unit enables searches by, for example, keywords, categories, or natural language. The search unit can make information searchable by methods such as indexing, full-text search, and metadata search. The generation unit uses a generation AI to analyze the information searched by the search unit and generate ideas that create synergy. For example, the generation AI analyzes registered business information and combines related information to generate new ideas. The generation AI generates ideas using techniques such as machine learning models and neural networks. The proposal unit provides the ideas generated by the generation unit to a user. The proposal unit can provide the generated ideas to the user by, for example, notifying the user, displaying a dashboard, sending an email, etc. As a result, the business information sharing system according to the embodiment can improve the efficiency of business considerations and create synergies through the collection, search, analysis, and proposal of information.

[0070] The collection unit can register business information being considered by each department in the database to the extent that it can be shared. For example, the collection unit registers business information being considered by each department in the database to the extent that it can be shared. For example, the collection unit can register project overviews, progress status, and contact information for related parties. For example, the collection unit sets the scope of information sharing to prevent confidential information from leaking. The scope of sharing includes, for example, access rights and data confidentiality. This makes it easier to share information by registering business information from each department in the database. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can automatically collect business information being considered by each department using AI and register it in the database.

[0071] The search unit can enable searches by keyword, category, or natural language. The search unit can enable searches by keyword, category, or natural language, for example. The search unit can make information searchable by methods such as indexing, full-text search, and metadata search. Searches by keyword, category, or natural language include, for example, keyword matching, category classification, and natural language processing technology. This allows users to easily obtain the information they need by providing a variety of search methods. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can use AI to perform searches by keyword, category, or natural language to obtain the required information.

[0072] The generation unit can use the generation AI to analyze registered business information and combine related information to generate new ideas. The generation unit, for example, uses the generation AI to analyze registered business information and combine related information to generate new ideas. The generation AI generates ideas using technologies such as machine learning models and neural networks. The generation AI, for example, analyzes registered business information and combines related information based on common keywords, correlations, etc. In this way, new ideas with high relevance can be generated using the generation AI. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit inputs business information to the generation AI, and the generation AI combines related information to generate new ideas.

[0073] The suggestion unit can provide the generated idea to the user. The suggestion unit, for example, provides the generated idea to the user. The suggestion unit can provide the generated idea to the user by, for example, notifying the user, displaying a dashboard, sending an email, etc. Methods of providing the idea to the user include, for example, notifying the user, displaying a dashboard, sending an email, etc. In this way, by providing the generated idea to the user, the efficiency of business considerations is improved. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can provide the generated idea to the user by using AI.

[0074] The suggestion unit can receive feedback from the user. The suggestion unit receives, for example, feedback from the user. The suggestion unit can receive feedback by methods such as questionnaires, comments, and evaluations. Examples of feedback include questionnaires, comments, and evaluations. This allows the system to be improved by receiving feedback from the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can automatically collect feedback from users using AI and reflect it in improving the system.

[0075] The search unit can display search results in ranking. The search unit, for example, displays the search results in ranking. The search unit can display the search results in ranking, for example, using a method such as scoring or a ranking algorithm. Ranking display includes, for example, scoring and a ranking algorithm. In this way, the search results are displayed in ranking, allowing the user to quickly grasp important information. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can score search results using AI and display them in ranking.

[0076] The collection unit can analyze the user's emotions and adjust the timing of information collection based on the analyzed user emotions. For example, the collection unit analyzes the user's emotions and adjusts the timing of information collection based on the analyzed user emotions. For example, when the user is feeling stressed, the collection unit reduces the frequency of information collection and collects only important information. For example, when the user is relaxed, the collection unit can increase the frequency of information collection and collect detailed information. For example, when the user is in a hurry, the collection unit can quickly collect information and provide it immediately. This enables efficient information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the timing of information collection.

[0077] The collection unit can analyze each department's past information collection history and select an appropriate collection method. For example, the collection unit can analyze each department's past information collection history and identify the most effective collection method. For example, the collection unit can prioritize collection of frequently used information sources from each department's information collection history. For example, the collection unit can customize the collection method based on each department's information collection history to collect information efficiently. The past information collection history includes, for example, the collection date and time, the collection content, and the collection frequency. This allows the optimal collection method to be selected by analyzing the past information collection history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can use AI to analyze each department's past information collection history and select the optimal collection method.

[0078] The collection unit may filter information based on each department's current projects and areas of interest when collecting information. For example, the collection unit may filter information based on each department's current projects and areas of interest when collecting information. For example, the collection unit may collect only information related to each department's current projects. For example, the collection unit may preferentially collect highly relevant information based on each department's areas of interest. For example, the collection unit may filter and collect necessary information based on the progress of each department's projects. Current projects and areas of interest include, for example, project names and field categories. Thus, highly relevant information can be collected by filtering information based on each department's projects and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may use AI to filter information based on each department's current projects and areas of interest.

[0079] The collection unit can select an appropriate collection means according to the input method of each department when collecting information. For example, the collection unit selects the optimal collection means according to the input method (voice, text, image, etc.) of each department when collecting information. For example, if each department uses voice input, the collection unit can prioritize collecting voice data. For example, if each department uses text input, the collection unit can also prioritize collecting text data. For example, if each department uses image input, the collection unit can also prioritize collecting image data. Input methods include, for example, voice input, text input, image input, etc. This enables efficient information collection by selecting the optimal collection means according to the input method of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to select the optimal collection means according to the input method of each department.

[0080] The collection unit can analyze the user's emotions and determine the priority of information to be collected based on the analyzed user emotions. For example, the collection unit can analyze the user's emotions and determine the priority of information to be collected based on the analyzed user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting information of high importance. For example, when the user is relaxed, the collection unit can also prioritize collecting detailed information. For example, when the user is in a hurry, the collection unit can also prioritize collecting information that can be collected quickly. In this way, by determining the priority of information according to the user's emotions, important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit inputs the user's emotion data into a generation AI, and the generation AI determines the priority of information.

[0081] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each department. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of each department. For example, the collection unit prioritizes collecting nearby information based on the geographical location information of each department. For example, the collection unit can also collect region-specific information by taking into account the geographical location information of each department. For example, the collection unit can also filter and collect highly relevant information based on the geographical location information of each department. Geographical location information includes, for example, GPS data, address information, etc. In this way, highly relevant information can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect information by using AI and taking into account the geographical location information of each department.

[0082] The collection unit can analyze the social media activities of each department when collecting information and collect relevant information. For example, the collection unit can analyze the social media activities of each department when collecting information and collect relevant information. For example, the collection unit can analyze the social media activities of each department and collect relevant information. For example, the collection unit can also collect highly relevant information based on the content of posts on social media by each department. For example, the collection unit can also collect relevant information by referring to the activities of each department's friends on social media. Social media activities include, for example, post content and engagement. In this way, highly relevant information can be collected by analyzing social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the social media activities of each department using AI and collect relevant information.

[0083] The collection unit can customize the collection method by reflecting past feedback from each department when collecting information. For example, the collection unit customizes the collection method by reflecting past feedback from each department when collecting information. The collection unit customizes the collection method based on, for example, past feedback from each department. The collection unit can also adjust the type of information to be collected by reflecting past feedback from each department. For example, the collection unit can also optimize the collection means by referring to past feedback from each department. Past feedback includes, for example, user comments, evaluation results, etc. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze past feedback from each department using AI to customize the collection method.

[0084] The search unit can analyze the user's emotions and adjust the display method of the search results based on the analyzed user emotions. For example, the search unit can analyze the user's emotions and adjust the display method of the search results based on the analyzed user emotions. For example, if the user is nervous, the search unit provides a simple, highly visible display method. For example, if the user is relaxed, the search unit can provide a display method including detailed information. For example, if the user is in a hurry, the search unit can provide a display method that focuses on the main points. This improves visibility by adjusting the display method of the search results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit inputs user emotion data into a generation AI, and the generation AI adjusts the display method of the search results.

[0085] The search unit can adjust the level of detail of search results based on the importance of information during a search. The search unit, for example, adjusts the level of detail of search results based on the importance of information during a search. For example, the search unit prioritizes displaying information with high importance and provides detailed information. For example, the search unit can also briefly display information with low importance and display more details as needed. For example, the search unit can adjust the display order of search results based on the importance of information. The importance of information includes, for example, scoring, ranking, etc. In this way, by adjusting the level of detail of search results based on the importance of information, necessary information can be quickly obtained. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can evaluate the importance of information using AI and adjust the level of detail of search results.

[0086] The search unit can apply different search algorithms depending on the category of information during a search. For example, the search unit applies different search algorithms depending on the category of information during a search. For example, the search unit applies a search algorithm specialized for the product category when searching for product information. For example, the search unit can apply a search algorithm specialized for the market category when searching for market research information. For example, the search unit can apply a search algorithm specialized for the project category when searching for project information. Information categories include, for example, topic classification and industry classification. Search algorithms include, for example, TF-IDF and BM25. By applying a search algorithm depending on the category of information, search accuracy is improved. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can apply a search algorithm depending on the category of information using AI.

[0087] The search unit can improve search accuracy by referring to the user's past search history when searching. The search unit can improve search accuracy by referring to the user's past search history, for example. The search unit can prioritize displaying highly relevant information based on the user's past search history, for example. The search unit can also analyze the user's past search history and optimize the search algorithm, for example. The search unit can also adjust the display order of search results by referring to the user's past search history, for example. The past search history includes, for example, search keywords, search date and time, etc. Thus, by referring to the past search history, search accuracy is improved. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can analyze the user's past search history using AI to improve search accuracy.

[0088] The search unit can analyze the user's emotions and adjust the length of search results based on the analyzed user emotions. For example, the search unit analyzes the user's emotions and adjusts the length of search results based on the analyzed user emotions. For example, if the user is nervous, the search unit displays short, to-the-point search results. For example, if the user is relaxed, the search unit can display longer search results with detailed information. For example, if the user is in a hurry, the search unit can display short search results that can be quickly viewed. This improves visibility by adjusting the length of search results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit inputs user emotion data into a generation AI, which then adjusts the length of the search results.

[0089] The search unit can determine the priority of search results based on the time of information submission during a search. The search unit, for example, determines the priority of search results based on the time of information submission during a search. For example, the search unit prioritizes displaying the most recent information and postpones older information. The search unit can also adjust the display order of search results based on the time of information submission, for example. The search unit can also group and display information that was submitted recently. The time of information submission includes, for example, the submission date and time, the update date and time, etc. In this way, by determining the priority of search results based on the time of information submission, the most recent information can be preferentially obtained. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can evaluate the time of information submission using AI to determine the priority of search results.

[0090] The search unit can adjust the order of search results based on the relevance of information during a search. The search unit, for example, adjusts the order of search results based on the relevance of information during a search. For example, the search unit prioritizes displaying highly relevant information and postpones displaying less relevant information. The search unit can also adjust the display order of search results based on the relevance of information. For example, the search unit can group and display highly relevant information. The relevance of information includes, for example, common keywords, correlations, etc. In this way, by adjusting the order of search results based on the relevance of information, highly relevant information can be preferentially obtained. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can evaluate the relevance of information using AI and adjust the order of search results.

[0091] The search unit can adjust the use of technical terms in search results according to the user's level of expertise during a search. For example, the search unit can adjust the use of technical terms in search results according to the user's level of expertise during a search. For example, if the user's level of expertise is high, the search unit can display search results that use a lot of technical terms. For example, if the user's level of expertise is low, the search unit can also display concise and easy-to-understand search results. The search unit can also adjust the display content of search results according to the user's level of expertise. The user's level of expertise includes, for example, survey results, past search history, etc. The use of technical terms includes, for example, definitions of technical terms, simplification of terms, etc. This allows the use of technical terms in search results according to the user's level of expertise to be adjusted, thereby providing easy-to-understand search results. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without AI. For example, the search unit can use AI to evaluate the user's level of expertise and adjust the use of technical terms in search results.

[0092] The generation unit can analyze the user's emotions and adjust the expression method of the generated idea based on the analyzed user's emotions. For example, the generation unit can analyze the user's emotions and adjust the expression method of the generated idea based on the analyzed user's emotions. For example, if the user is relaxed, the generation unit can generate an idea that proceeds at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate an idea that emphasizes the shortest route. For example, if the user is excited, the generation unit can generate an idea that adds visually stimulating effects. This allows for adjusting the expression method of the idea according to the user's emotions, thereby providing more appropriate ideas. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the expression method of the idea.

[0093] The generation unit can improve the accuracy of generation by taking into account the interrelationships of information during generation. The generation unit, for example, improves the accuracy of generation by taking into account the interrelationships of information during generation. The generation unit, for example, analyzes the interrelationships of information and generates ideas by combining highly related information. The generation unit can also generate optimal ideas by taking into account the interrelationships of information. The generation unit can also generate ideas with high synergy effects based on the interrelationships of information. Examples of the interrelationships of information include co-occurrence networks and correlation analysis. As a result, the accuracy of generation is improved by taking the interrelationships of information into consideration. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the interrelationships of information into the generation AI, and the generation AI generates ideas.

[0094] The generation unit can generate ideas taking into account the attribute information of the information submitter when generating the ideas. For example, the generation unit generates ideas taking into account the attribute information of the information submitter when generating the ideas. For example, the generation unit generates optimal ideas taking into account the expertise level of the information submitter. For example, the generation unit can also generate highly relevant ideas based on the past performance of the information submitter. For example, the generation unit can also generate ideas with high synergy effects taking into account the attribute information of the information submitter. The attribute information of the submitter includes, for example, job position, field of expertise, etc. In this way, highly relevant ideas can be generated by taking into account the attribute information of the information submitter. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the attribute information of the information submitter into the generation AI, which then generates ideas.

[0095] The generation unit can weight the generation based on the frequency of information submission during generation. For example, the generation unit weights the generation based on the frequency of information submission during generation. For example, if the frequency of information submission is high, the generation unit generates ideas by prioritizing that information. For example, if the frequency of information submission is low, the generation unit can also generate ideas by using that information as a complement. For example, the generation unit can determine the priority of ideas to be generated based on the frequency of information submission. The frequency of information submission includes, for example, the number of submissions and the submission interval. The weighting of generation includes, for example, an importance score and a frequency score. In this way, by weighting the generation based on the frequency of information submission, ideas that prioritize important information can be generated. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the frequency of information submission into the generation AI, and the generation AI weights the ideas.

[0096] The generation unit can analyze the user's emotions and determine the priority of ideas to be generated based on the analyzed user emotions. For example, the generation unit can analyze the user's emotions and determine the priority of ideas to be generated based on the analyzed user emotions. For example, when the user is stressed, the generation unit can prioritize generating ideas with high importance. For example, when the user is relaxed, the generation unit can also prioritize generating detailed ideas. For example, when the user is in a hurry, the generation unit can also prioritize generating ideas that can be generated quickly. In this way, by prioritizing ideas according to the user's emotions, important ideas can be generated preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, and the generation AI determines the priority of ideas.

[0097] The generation unit can generate ideas taking into account the geographical distribution of information during generation. For example, the generation unit generates ideas taking into account the geographical distribution of information during generation. For example, the generation unit generates region-specific ideas based on the geographical distribution of information. For example, the generation unit can also generate optimal ideas taking into account the geographical distribution of information. For example, the generation unit can also generate ideas with high synergy effects based on the geographical distribution of information. The geographical distribution includes, for example, regional data, map information, etc. In this way, region-specific ideas can be generated by taking into account the geographical distribution of information. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the geographical distribution of information into the generation AI, which then generates ideas.

[0098] The generation unit can improve the accuracy of generation by referring to literature related to the information during generation. The generation unit, for example, improves the accuracy of generation by referring to literature related to the information during generation. The generation unit, for example, refers to literature related to the information to generate highly relevant ideas. The generation unit can also generate optimal ideas based on literature related to the information. The generation unit can also generate ideas with high synergy effects by referring to literature related to the information. Related literature includes, for example, academic papers, patent documents, etc. As a result, the accuracy of generation is improved by referring to related literature. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs literature related to the information into the generation AI, which generates ideas.

[0099] The generation unit can generate ideas taking into account the market value of the information when generating them. For example, the generation unit generates ideas taking into account the market value of the information when generating them. For example, the generation unit generates the most valuable idea based on the market value of the information. For example, the generation unit can also generate optimal ideas taking into account the market value of the information. For example, the generation unit can also generate ideas with high synergy effects based on the market value of the information. Market value includes, for example, market research data, sales forecasts, etc. In this way, valuable ideas can be generated by taking into account the market value of the information. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the market value of the information into the generation AI, which then generates ideas.

[0100] The suggestion unit can analyze the user's emotions and adjust the way the suggestions are presented based on the analyzed user emotions. For example, the suggestion unit can analyze the user's emotions and adjust the way the suggestions are presented based on the analyzed user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. For example, if the user is relaxed, the suggestion unit can provide suggestions that include detailed information. For example, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This allows for more appropriate suggestions to be provided by adjusting the way the suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the way the suggestions are presented.

[0101] The suggestion unit can adjust the level of detail of the proposal based on the importance of the generated idea when making a suggestion. For example, the suggestion unit can adjust the level of detail of the proposal based on the importance of the generated idea when making a suggestion. For example, the suggestion unit can prioritize suggesting ideas with high importance and provide detailed information. For example, the suggestion unit can briefly suggest ideas with low importance and provide details as needed. For example, the suggestion unit can adjust the display order of proposals based on the importance of the ideas. The importance of ideas includes, for example, scoring, ranking, etc. The level of detail of the proposal includes, for example, summary display, full-text display, etc. In this way, by adjusting the level of detail of the proposal based on the importance of the idea, important ideas can be prioritized for suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can evaluate the importance of ideas using AI and adjust the level of detail of the proposal.

[0102] The suggestion unit can apply different suggestion algorithms depending on the category of the generated idea when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the generated idea when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm specialized for a product category when proposing a product development idea. For example, the suggestion unit can apply a suggestion algorithm specialized for a market category when proposing a market research idea. For example, the suggestion unit can apply a suggestion algorithm specialized for a project category when proposing a project idea. Idea categories include, for example, topic classification, industry classification, etc. Proposal algorithms include, for example, recommendation systems, collaborative filtering, etc. As a result, by applying a suggestion algorithm depending on the category of the idea, the accuracy of the suggestion is improved. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can apply a suggestion algorithm depending on the category of the idea using AI.

[0103] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit prioritizes suggesting highly relevant ideas based on the user's past suggestion results. For example, the suggestion unit can also analyze the user's past suggestion results and optimize the suggestion algorithm. For example, the suggestion unit can also adjust the display order of the suggestion by referring to the user's past suggestion results. The past suggestion results include, for example, the acceptance rate of the suggestion, feedback content, etc. In this way, the accuracy of the suggestion is improved by referring to the past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can analyze the user's past suggestion results using AI to improve the accuracy of the suggestion.

[0104] The suggestion unit can analyze the user's emotions and adjust the length of the suggestion based on the analyzed user's emotions. For example, the suggestion unit can analyze the user's emotions and adjust the length of the suggestion based on the analyzed user's emotions. For example, if the user is nervous, the suggestion unit can provide short, to-the-point suggestions. For example, if the user is relaxed, the suggestion unit can provide longer suggestions including detailed information. For example, if the user is in a hurry, the suggestion unit can provide short suggestions that can be quickly confirmed. This improves visibility by adjusting the length of the suggestion based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the length of the suggestion.

[0105] The suggestion unit can determine the priority of the proposals based on the submission dates of the generated ideas at the time of proposal. For example, the suggestion unit determines the priority of the proposals based on the submission dates of the generated ideas at the time of proposal. For example, the suggestion unit preferentially proposes the most recent ideas and postpones older ideas. The suggestion unit can also adjust the display order of the proposals based on the submission dates of the ideas. For example, the suggestion unit can group and propose ideas that are submitted close in time. The submission dates of ideas include, for example, the submission date and time, the update date and time, etc. In this way, by determining the priority of the proposals based on the submission dates of the ideas, the most recent ideas can be preferentially proposed. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can use AI to evaluate the submission dates of ideas and determine the priority of the proposals.

[0106] The suggestion unit can adjust the order of suggestions based on the relevance of the generated ideas when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the generated ideas when making suggestions. For example, the suggestion unit preferentially suggests highly relevant ideas and postpones less relevant ideas. The suggestion unit can also adjust the display order of suggestions based on the relevance of the ideas. For example, the suggestion unit can group highly relevant ideas and suggest them. The relevance of ideas includes, for example, common keywords, correlations, etc. In this way, by adjusting the order of suggestions based on the relevance of ideas, highly relevant ideas can be preferentially suggested. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can evaluate the relevance of ideas using AI and adjust the order of suggestions.

[0107] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise. For example, the suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user's level of expertise is high, the suggestion unit may provide a proposal that uses a lot of technical terms. For example, if the user's level of expertise is low, the suggestion unit may provide a concise and easy-to-understand proposal. The suggestion unit may also adjust the display content of the proposal according to the user's level of expertise. The user's level of expertise may include, for example, survey results, past search history, etc. The use of technical terms may include, for example, definitions of technical terms, simplification of terms, etc. By adjusting the use of technical terms in the proposal according to the user's level of expertise, it is possible to provide an easy-to-understand proposal. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may use AI to evaluate the user's level of expertise and adjust the use of technical terms in the proposal. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, search unit, generation unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 38B of the smart device 14 and registers the information in a database using the control unit 46A. The search unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the search unit displays search results using the display 40A of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information using a generation AI to generate new ideas. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides the generated ideas to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, search unit, generation unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the smart glasses 214 and registers the information in a database via the control unit 46A. The search unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the search unit displays search results using the display of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information using a generation AI to generate new ideas. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides the generated ideas to a user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, search unit, generation unit, and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the headset-type terminal 314 and registers the information in a database via the control unit 46A. The search unit is realized, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the search unit displays search results using the display 343 of the headset-type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information using a generation AI to generate new ideas. The suggestion unit is realized, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides the generated ideas to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, search unit, generation unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the robot 414 and registers the information in a database via the control unit 46A. The search unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the search unit displays search results using a display of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes information using a generative AI to generate new ideas. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides the generated ideas to a user.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] When collecting business information from each department, the collection department can evaluate the reliability of the information and prioritize collecting highly reliable information. For example, the collection department can evaluate the reliability of the information source and provider, and register highly reliable information in the database on a priority basis. The collection department can also score the reliability of the information and prioritize collecting information with a high score. Furthermore, the collection department can identify highly reliable information sources based on past information provision history and prioritize collecting information. In this way, the collection department can improve the information quality of the database by prioritizing the collection of highly reliable information.

[0110] The search unit can analyze the user's search history and personalize search results based on the user's search patterns. For example, the search unit can analyze keywords and categories that the user has searched for in the past and prioritize displaying highly relevant information. The search unit can also provide optimal search results by taking into account the user's search frequency and search time period. Furthermore, the search unit can predict and suggest information that the user is likely to be interested in based on the user's search history. This allows the search unit to utilize the user's search history to provide more personalized search results.

[0111] The generation department can use the generation AI to analyze trends in business information and generate ideas based on those trends. For example, the generation department can analyze past business information and identify current trends. The generation department can also predict future business opportunities based on those trends and generate ideas based on those. Furthermore, the generation department can generate ideas that create synergies with other departments based on trend information. In this way, the generation department can improve the competitiveness of the business by generating ideas based on trends.

[0112] The suggestion unit can collect user feedback in real time and immediately improve the suggestion content. For example, the suggestion unit can request feedback from the user immediately after receiving a suggestion and improve the suggestion content based on the feedback. The suggestion unit can also analyze user feedback, identify common areas for improvement, and reflect them in the next suggestion. Furthermore, the suggestion unit can adjust the display and notification methods of the suggestion based on the feedback. In this way, the suggestion unit can utilize user feedback to provide more effective suggestions.

[0113] The collection unit can analyze the user's emotions and customize the method of information collection based on the analyzed user's emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of information collection and collect only important information. Also, if the user is relaxed, the collection unit can increase the frequency of information collection and collect more detailed information. Furthermore, if the user is in a hurry, the collection unit can quickly collect information and provide it immediately. In this way, the collection unit can customize the method of information collection according to the user's emotions, thereby enabling efficient information collection.

[0114] The search unit can analyze the user's emotions and adjust the display method of the search results based on the analyzed user's emotions. For example, if the user is nervous, the search unit can provide a simple, highly visible display method. If the user is relaxed, the search unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the search unit can also provide a display method that focuses on the main points. In this way, the search unit can adjust the display method of the search results according to the user's emotions, thereby improving visibility.

[0115] The generation unit can analyze the user's emotions and adjust the expression method of the generated idea based on the analyzed user's emotions. For example, if the user is relaxed, the generation unit can generate an idea that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate an idea that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate an idea that adds visually stimulating effects. In this way, the generation unit can provide more appropriate ideas by adjusting the expression method of the idea according to the user's emotions.

[0116] The suggestion unit can analyze the user's emotions and adjust the way in which suggestions are expressed based on the analyzed user's emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way in which suggestions are expressed according to the user's emotions.

[0117] The generation unit can analyze the user's emotions and determine the priority of ideas to be generated based on the analyzed user's emotions. For example, if the user is feeling stressed, the generation unit can prioritize generating ideas with high importance. Also, if the user is relaxed, the generation unit can prioritize generating detailed ideas. Furthermore, if the user is in a hurry, the generation unit can prioritize generating ideas that can be generated quickly. In this way, the generation unit can prioritize generating important ideas by determining the priority of ideas according to the user's emotions.

[0118] The suggestion unit can adjust the order of suggestions based on the relevance of the generated ideas when making suggestions. For example, the suggestion unit can preferentially suggest highly relevant ideas and postpone less relevant ideas. The suggestion unit can also adjust the display order of suggestions based on the relevance of the ideas. Furthermore, the suggestion unit can group highly relevant ideas and suggest them. In this way, the suggestion unit can preferentially suggest highly relevant ideas by adjusting the order of suggestions based on the relevance of the ideas.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection department collects business information being considered by each department. For example, the collection department registers the business information being considered by each department in a database to the extent that it can be shared. For example, the collection department can register project overviews, progress, contact information for stakeholders, etc. The collection department can collect information by methods such as web scraping, data acquisition through APIs, and manual input. Step 2: The search unit makes the information collected by the collection unit searchable. The search unit enables searches by keyword, category, or natural language, for example. The search unit can make the information searchable by methods such as indexing, full-text search, and metadata search. Step 3: The generation unit uses the generation AI to analyze the information retrieved by the search unit and generate ideas that create synergy. For example, the generation unit uses the generation AI to analyze registered business information and combine related information to generate new ideas. The generation AI generates ideas using technologies such as machine learning models and neural networks. Step 4: The suggestion unit provides the idea generated by the generation unit to the user. The suggestion unit can provide the generated idea to the user by, for example, notifying the user, displaying it on a dashboard, sending an email, or the like.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 AI 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 AI 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 collection unit that collects information; a search unit that makes the information collected by the collection unit searchable; a generation unit that analyzes the information searched by the search unit and generates ideas that will create synergies; a suggestion unit that provides the idea generated by the generation unit to a user. A system characterized by:

2. The collecting unit Register business information being considered by each department in a database to the extent that it can be shared.

2. The system of claim 1.

3. The search unit Allows searching by keyword, category, or natural language 2. The system of claim 1.

4. The generation unit Generative AI analyzes registered business information and combines relevant information to generate new ideas.

2. The system of claim 1.

5. The proposal unit Providing generated ideas to users 2. The system of claim 1.

6. The proposal unit Accepting user feedback 2. The system of claim 1.

7. The search unit Ranking search results 2. The system of claim 1.

8. The collecting unit Analyze user emotions and adjust the timing of information collection based on the analyzed user emotions.

2. The system of claim 1.

9. The collecting unit Analyze each department's past information collection history and select the appropriate collection method.

2. The system of claim 1.

Images