System

Abstract

An object of the system according to the embodiment is to allow an idea provider to find an appropriate executor or supporter.SOLUTION: A system includes an idea input unit, an analysis unit, an executor suggestion unit, and a supporter suggestion unit. The idea input unit inputs an idea. The analysis unit analyzes the idea input by the idea input unit. The executor proposing section proposes an appropriate executor based on the idea analyzed by the analyzing section. The supporter proposing section proposes an appropriate supporter based on the idea analyzed by the analyzing section.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 have had the problem that it is difficult for idea providers to find appropriate implementers and supporters.

[0005] The system according to the embodiment aims to enable an idea provider to find appropriate implementers and supporters. [Means for solving the problem]

[0006] The system according to the embodiment includes an idea input unit, an analysis unit, a doer suggestion unit, and a supporter suggestion unit. The idea input unit inputs an idea. The analysis unit analyzes the idea input by the idea input unit. The doer suggestion unit suggests an appropriate doer based on the idea analyzed by the analysis unit. The supporter suggestion unit suggests an appropriate supporter based on the idea analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows an idea provider to find suitable implementers and supporters. [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 matching platform according to an embodiment of the present invention is a system that effectively connects idea providers, implementers, supporters, and participants. This system utilizes generative AI to analyze the content and needs of ideas and automatically suggest appropriate implementers and supporters. This allows the matching platform to effectively connect idea providers, implementers, supporters, and participants and support the realization of ideas.

[0029] A matching platform according to an embodiment includes an idea input unit, an analysis unit, an implementer suggestion unit, and a supporter suggestion unit. The idea input unit allows a user to input an idea. For example, the user may input the idea in text format. The idea input unit can also support voice input and handwritten input. The analysis unit analyzes the idea input by the idea input unit. For example, a generation AI may analyze the content of the idea using text analysis technology. The analysis unit can also analyze the needs for the idea using data mining technology. The implementer suggestion unit suggests an appropriate implementer based on the idea analyzed by the analysis unit. For example, the generation AI may analyze the implementer's skill set and suggest the implementer best suited to the idea. The implementer suggestion unit can also make suggestions based on the implementer's past project success rate. The supporter suggestion unit suggests an appropriate supporter based on the idea analyzed by the analysis unit. For example, the generation AI may analyze the supporter's investment history and suggest the supporter best suited to the idea. The supporter suggestion unit can also make suggestions based on the supporter's network and influence. This enables the matching platform according to an embodiment to effectively connect idea providers, implementers, and supporters.

[0030] The idea input unit can reference the user's past ideas and project history and automatically complete highly relevant information. For example, when a user inputs a new idea, the idea input unit's generation AI automatically searches for previously input ideas and project history and completes the relevant information. The idea input unit also analyzes the user's past project history and automatically suggests related resources and success stories. For example, it completes new ideas based on resources from successful projects in the past. Furthermore, when a user inputs an idea, the idea input unit's generation AI automatically suggests related keywords and phrases based on past ideas and project history. This makes it possible to utilize the user's past information to streamline idea input.

[0031] When analyzing the content of an idea, the analysis unit searches patent databases to clarify the differences between the idea and existing technologies and competing ideas. For example, when analyzing the content of an idea, the analysis unit has the generation AI automatically search patent databases to clarify the differences between the idea and existing technologies and competing ideas. In addition, the analysis unit has the generation AI analyze the content of the idea, search relevant patent databases, and automatically report the differences between the idea and existing technologies and competing ideas. For example, it provides patent numbers and inventor information. In addition, when analyzing an idea, the analysis unit has the generation AI refer to patent databases to visually display the differences between the idea and existing technologies and competing ideas. This allows the idea's uniqueness to be evaluated and differentiated from competitors.

[0032] The idea input unit also supports voice input and handwriting input, allowing users to input ideas in the way that is most convenient for them. For example, when inputting ideas, the idea input unit may have the generation AI support voice input, allowing users to input ideas simply by speaking. For example, it may use voice recognition technology to convert the user's speech into text. In addition, to support handwriting input, the generation AI may introduce handwriting recognition technology, allowing users to input ideas by hand. For example, handwriting input may be performed using a tablet or smartphone. In addition, the idea input unit may provide options for voice input and handwriting input so that users can input ideas in the way that is most convenient for them. This allows users to input ideas in the way that is most convenient for them.

[0033] The analysis unit can evaluate the market adaptability of an idea by referring to trend data from different industries when analyzing the content of the idea. For example, when analyzing the content of an idea, the analysis unit has the generation AI automatically collect trend data from different industries and evaluate the market adaptability of the idea. The analysis unit also builds a system in which the generation AI analyzes the content of an idea and refers to trend data from different industries to evaluate the market adaptability of the idea. For example, the analysis unit makes an evaluation based on industry reports and market research data. Furthermore, when analyzing an idea, the analysis unit has the generation AI refer to trend data from different industries and visually displays the market adaptability of the idea. This makes it possible to evaluate the market adaptability of an idea and increase the chances of success.

[0034] The executor suggestion unit can suggest the most suitable executor based on the executor's past project success rate and evaluation. For example, the generation AI analyzes the executor's past project success rate and suggests the most suitable executor. The executor suggestion unit also builds a system in which the generation AI suggests the most suitable executor based on the executor's evaluation data. For example, the executor is selected based on user evaluations and feedback. The executor suggestion unit also analyzes the executor's past project history and suggests the most suitable executor based on the success rate and evaluation. This makes it possible to suggest the most suitable executor based on the executor's past performance.

[0035] The executor suggestion unit can make optimal matches by considering not only the executor's skill set but also their cultural background and working style. For example, the executor suggestion unit uses a generation AI to make optimal matches by considering not only the executor's skill set but also their cultural background and working style. The executor suggestion unit also analyzes the executor's cultural background and working style, and builds a system in which the generation AI makes optimal matches. For example, the executor suggestion unit suggests executors suitable for remote work. The executor suggestion unit also uses a generation AI to comprehensively evaluate the executor's skill set, cultural background, and working style and propose the most suitable executor. This makes it possible to make optimal matches by considering not only the executor's skill set but also their cultural background and working style.

[0036] The supporter suggestion unit can analyze the supporter's past investment history and success stories, and suggest the most suitable supporter. For example, the supporter suggestion unit uses a generation AI to analyze the supporter's past investment history and suggest the most suitable supporter. The supporter suggestion unit also builds a system in which the generation AI analyzes the supporter's success stories, and suggests the most suitable supporter. For example, the supporter suggestion unit preferentially suggests supporters with a high success rate. The supporter suggestion unit also uses a generation AI to suggest the most suitable supporter based on the supporter's past investment history and success stories. This makes it possible to suggest the most suitable supporter based on the supporter's past performance.

[0037] The backer suggestion unit can suggest backers who can contribute to the success of a project, taking into account the backer's network and influence. For example, the generation AI analyzes the backer's network and influence and suggests backers who can contribute to the success of a project. The backer suggestion unit also builds a system in which the generation AI evaluates the influence of backers and suggests the most suitable backers. For example, the generation AI prioritizes suggesting backers who have a high level of influence within the industry. The backer suggestion unit also suggests backers who can contribute to the success of a project, based on the backer's network and influence. This makes it possible to suggest the most suitable backers, taking into account the backer's network and influence.

[0038] The supporter suggestion unit can analyze the supporter's field of expertise and interests and suggest the supporter best suited to the project. For example, the generation AI of the supporter suggestion unit analyzes the supporter's field of expertise and interests and suggests the supporter best suited to the project. The supporter suggestion unit also builds a system in which the supporter's interests are evaluated and the generation AI suggests the most suitable supporter. For example, the selection is made based on the supporter's past project history. The supporter suggestion unit also builds a system in which the generation AI suggests the most suitable supporter for the project based on the supporter's field of expertise and interests. This makes it possible to suggest the most suitable supporter taking into account the supporter's field of expertise and interests.

[0039] The supporter suggestion unit can analyze supporters' past feedback and reviews and suggest highly reliable supporters. For example, the generation AI analyzes supporters' past feedback and reviews and suggests highly reliable supporters. The supporter suggestion unit also constructs a system in which the generation AI evaluates supporters' reviews and suggests highly reliable supporters. For example, the supporter suggestion unit selects supporters with many positive reviews. The generation AI also suggests highly reliable supporters based on the supporters' past feedback and reviews. This makes it possible to suggest highly reliable supporters.

[0040] The participant suggestion unit can suggest optimal participants based on the participants' past project participation history and evaluations. For example, the generation AI analyzes the participants' past project participation history and suggests optimal participants. The participant suggestion unit also builds a system in which the generation AI suggests optimal participants based on the participants' evaluation data. For example, participants are selected based on user evaluations and feedback. The generation AI also analyzes the participants' past project history and suggests optimal participants based on their success rate and evaluations. This makes it possible to suggest optimal participants based on the participants' past performance.

[0041] The participant suggestion unit can perform optimal matching by considering not only the skill sets of participants but also their interests and hobbies. For example, the generation AI of the participant suggestion unit performs optimal matching by considering not only the skill sets of participants but also their interests and hobbies. The participant suggestion unit also analyzes the interests and hobbies of participants, and builds a system in which the generation AI performs optimal matching. For example, it proposes participants who share common hobbies. The generation AI of the participant suggestion unit also comprehensively evaluates the skill sets, interests, and hobbies of participants and proposes the most suitable participants. This allows optimal matching to be performed by considering not only the skill sets of participants but also their interests and hobbies.

[0042] The participant suggestion unit also takes into account the geographical location information of the participants and can suggest participants that suit the characteristics of each region. For example, the generation AI of the participant suggestion unit takes into account the geographical location information of the participants and suggests participants that suit the characteristics of each region. The participant suggestion unit also builds a system in which the generation AI analyzes the geographical location information of the participants and suggests participants that suit the characteristics of each region. For example, participants that correspond to the culture and market needs of the region are selected. The participant suggestion unit also takes into account the geographical location information of the participants and the generation AI suggests participants that suit the characteristics of each region. This makes it possible to suggest participants that suit the characteristics of each region.

[0043] The participant suggestion unit can analyze the social media activity of participants and suggest the most suitable participants based on the latest activity status. In the participant suggestion unit, for example, a generation AI analyzes the social media activity of participants and suggests the most suitable participants based on the latest activity status. The participant suggestion unit also builds a system in which the generation AI analyzes the social media activity of participants and suggests participants based on the latest activity status. For example, participants are selected based on ratings and feedback on social media. In addition, the participant suggestion unit can have the generation AI evaluate the latest activity status based on the participants' social media activity and suggest the most suitable participants. This makes it possible to suggest the most suitable participants based on the latest activity status.

[0044] The notification unit can provide past success stories and reference materials when notifying the matching result, thereby deepening the idea provider's understanding. For example, when the generation AI notifies the matching result, the notification unit can provide past success stories and reference materials to deepen the idea provider's understanding. The notification unit also builds a system in which the generation AI provides reference materials when notifying the matching result, thereby deepening the idea provider's understanding. For example, it can provide related technical literature and market reports. The notification unit can also deepen the idea provider's understanding based on past success stories and reference materials when the generation AI notifies the matching result. In this way, the provision of past success stories and reference materials can deepen the idea provider's understanding.

[0045] The notification department can automatically track the progress of the project and report on it periodically during follow-up. For example, the notification department builds a system where the generation AI automatically tracks the progress of the project and reports on it periodically. Furthermore, the notification department can track the progress of the project in real time during follow-up and report on it periodically. For example, the notification department can notify of important milestones in the project. Furthermore, the notification department can develop a system where the generation AI automatically tracks the progress of the project and reports on it periodically. This makes it possible to automatically track the progress of the project and report on it periodically.

[0046] The notification unit can use different communication channels (email, SMS, app notification) when notifying the user of the matching result, and can notify the user according to their preferences. For example, the notification unit uses different communication channels when the generation AI notifies the user of the matching result, according to their preferences. In addition, the notification unit builds a system in which the generation AI analyzes the user's past notification history and suggests the optimal communication channel when notifying the user of the matching result. For example, the channel that has had the most positive response in the past is given priority. In addition, the notification unit uses different communication channels when the generation AI notifies the user of the matching result, according to their preferences. This allows notifications to be tailored to the user's preferences.

[0047] The notification unit can collect user feedback during follow-up and utilize it to improve the accuracy of the next match. For example, the notification unit constructs a system in which the generation AI collects user feedback during follow-up and utilizes it to improve the accuracy of the next match. Furthermore, the notification unit allows the generation AI to collect user feedback in real time during follow-up and improve the accuracy of the next match. For example, the matching conditions are adjusted based on the user's opinion. Furthermore, the notification unit allows the generation AI to collect user feedback during follow-up and utilizes it to improve the accuracy of the next match. In this way, user feedback can be collected and the accuracy of the next match can be improved.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] When analyzing the content of an idea, the analysis unit can automatically detect potential problems and risks in the idea using natural language processing technology. For example, the generation AI can use text analysis technology to extract potential legal risks and technical issues from the content of the idea. The analysis unit can also have the generation AI analyze the content of the idea and evaluate the risks based on past failure cases. Furthermore, the analysis unit can have the generation AI analyze the content of the idea and visually display potential problems, making it easier for users to understand the risks. This enables risk management to increase the feasibility of ideas.

[0050] The executor suggestion unit can suggest compatible executors by taking into account not only the executor's skill set but also their past collaboration history. For example, the generation AI analyzes the executor's past collaboration history and makes suggestions based on successful project partnerships. The executor suggestion unit also builds a system in which the generation AI analyzes the executor's communication style and working style to suggest compatible executors. Furthermore, the executor suggestion unit can also suggest compatible executors based on the executor's past feedback. This strengthens cooperative relationships between executors and increases the project success rate.

[0051] The backer suggestion unit can analyze the investment portfolio of a backer and suggest the most suitable backer from the perspective of risk diversification. For example, the generation AI analyzes the investment portfolio of a backer and suggests backers from different industries or fields for risk diversification. The backer suggestion unit also builds a system in which the generation AI analyzes the investment history of a backer and suggests the most suitable backer from the perspective of risk diversification. Furthermore, the backer suggestion unit can also suggest backers for risk diversification based on the generation AI's investment portfolio. This can diversify the project's risks and increase the chances of success.

[0052] When analyzing the content of an idea, the analysis unit can refer to trend data from different industries and evaluate the market adaptability of the idea. For example, the generation AI automatically collects trend data from different industries and evaluates the market adaptability of the idea. The analysis unit also builds a system in which the generation AI analyzes the content of an idea and refers to trend data from different industries to evaluate the market adaptability of the idea. For example, the analysis unit makes an evaluation based on industry reports and market research data. When analyzing an idea, the generation AI also refers to trend data from different industries and visually displays the market adaptability of the idea. This makes it possible to evaluate the market adaptability of an idea and increase the chances of success.

[0053] The backer suggestion unit can suggest backers who can contribute to the success of a project, taking into account the backer's network and influence. For example, the generation AI analyzes the backer's network and influence and suggests backers who can contribute to the success of a project. The backer suggestion unit also builds a system in which the generation AI evaluates the influence of backers and suggests the most suitable backers. For example, the generation AI prioritizes suggesting backers with high influence within the industry. The backer suggestion unit also suggests backers who can contribute to the success of a project, based on the backer's network and influence. This makes it possible to suggest the most suitable backers, taking into account the backer's network and influence.

[0054] The participant suggestion unit also takes into account the geographical location information of participants and can suggest participants that suit the characteristics of each region. For example, the generation AI takes into account the geographical location information of participants and suggests participants that suit the characteristics of each region. The participant suggestion unit also analyzes the geographical location information of participants and builds a system in which the generation AI suggests participants that suit the characteristics of each region. For example, participants that correspond to the culture and market needs of the region are selected. The participant suggestion unit also takes into account the geographical location information of participants and the generation AI suggests participants that suit the characteristics of each region. This makes it possible to suggest participants that suit the characteristics of each region.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The idea input unit receives an idea from the user. For example, the user inputs the idea in text format. The idea input unit can also support voice input and handwritten input. Step 2: The analysis unit analyzes the ideas input by the idea input unit. For example, the generation AI analyzes the content of the ideas using text analysis technology. The analysis unit can also analyze the needs of the ideas using data mining technology. Step 3: The executor suggestion unit suggests appropriate executors based on the idea analyzed by the analysis unit. For example, the generation AI analyzes the executor's skill set and suggests the executor best suited to the idea. The executor suggestion unit can also make suggestions based on the executor's past project success rate. Step 4: The backer suggestion unit suggests suitable backers based on the idea analyzed by the analysis unit. For example, the generation AI analyzes the investment history of backers and suggests the best backers for the idea. The backer suggestion unit can also make suggestions based on the backer's network and influence.

[0057] (Example 2) A matching platform according to an embodiment of the present invention is a system that effectively connects idea providers, implementers, supporters, and participants. This system utilizes generative AI to analyze the content and needs of ideas and automatically suggest appropriate implementers and supporters. This allows the matching platform to effectively connect idea providers, implementers, supporters, and participants and support the realization of ideas.

[0058] A matching platform according to an embodiment includes an idea input unit, an analysis unit, an implementer suggestion unit, and a supporter suggestion unit. The idea input unit allows a user to input an idea. For example, the user may input the idea in text format. The idea input unit can also support voice input and handwritten input. The analysis unit analyzes the idea input by the idea input unit. For example, a generation AI may analyze the content of the idea using text analysis technology. The analysis unit can also analyze the needs for the idea using data mining technology. The implementer suggestion unit suggests an appropriate implementer based on the idea analyzed by the analysis unit. For example, the generation AI may analyze the implementer's skill set and suggest the implementer best suited to the idea. The implementer suggestion unit can also make suggestions based on the implementer's past project success rate. The supporter suggestion unit suggests an appropriate supporter based on the idea analyzed by the analysis unit. For example, the generation AI may analyze the supporter's investment history and suggest the supporter best suited to the idea. The supporter suggestion unit can also make suggestions based on the supporter's network and influence. This enables the matching platform according to an embodiment to effectively connect idea providers, implementers, and supporters.

[0059] The idea input unit can reference the user's past ideas and project history and automatically complete highly relevant information. For example, when a user inputs a new idea, the idea input unit's generation AI automatically searches for previously input ideas and project history and completes the relevant information. The idea input unit also analyzes the user's past project history and automatically suggests related resources and success stories. For example, it completes new ideas based on resources from successful projects in the past. Furthermore, when a user inputs an idea, the idea input unit's generation AI automatically suggests related keywords and phrases based on past ideas and project history. This makes it possible to utilize the user's past information to streamline idea input.

[0060] When analyzing the content of an idea, the analysis unit searches patent databases to clarify the differences between the idea and existing technologies and competing ideas. For example, when analyzing the content of an idea, the analysis unit has the generation AI automatically search patent databases to clarify the differences between the idea and existing technologies and competing ideas. In addition, the analysis unit has the generation AI analyze the content of the idea, search relevant patent databases, and automatically report the differences between the idea and existing technologies and competing ideas. For example, it provides patent numbers and inventor information. In addition, when analyzing an idea, the analysis unit has the generation AI refer to patent databases to visually display the differences between the idea and existing technologies and competing ideas. This allows the idea's uniqueness to be evaluated and differentiated from competitors.

[0061] The analysis unit uses the emotion estimation function to analyze the user's emotions when entering ideas and can provide real-time feedback to elicit positive emotions. For example, when entering ideas, the analysis unit has the generation AI analyze the user's facial expressions and voice to estimate emotions. To elicit positive emotions, the analysis unit provides encouraging messages and success stories in real time. The analysis unit also uses the emotion estimation function to analyze the user's emotions when entering ideas and provides feedback to elicit positive emotions. For example, if the user expresses negative emotions, the analysis unit plays relaxing music. When the user enters an idea, the analysis unit has the generation AI provide real-time feedback based on the emotion estimation data and provide advice to elicit positive emotions. This can elicit positive emotions from the user and encourage idea entry.

[0062] The idea input unit also supports voice input and handwriting input, allowing users to input ideas in the way that is most convenient for them. For example, when inputting ideas, the idea input unit may have the generation AI support voice input, allowing users to input ideas simply by speaking. For example, it may use voice recognition technology to convert the user's speech into text. In addition, to support handwriting input, the generation AI may introduce handwriting recognition technology, allowing users to input ideas by hand. For example, handwriting input may be performed using a tablet or smartphone. In addition, the idea input unit may provide options for voice input and handwriting input so that users can input ideas in the way that is most convenient for them. This allows users to input ideas in the way that is most convenient for them.

[0063] The analysis unit can evaluate the market adaptability of an idea by referring to trend data from different industries when analyzing the content of the idea. For example, when analyzing the content of an idea, the analysis unit has the generation AI automatically collect trend data from different industries and evaluate the market adaptability of the idea. The analysis unit also builds a system in which the generation AI analyzes the content of an idea and refers to trend data from different industries to evaluate the market adaptability of the idea. For example, the analysis unit makes an evaluation based on industry reports and market research data. Furthermore, when analyzing an idea, the analysis unit has the generation AI refer to trend data from different industries and visually displays the market adaptability of the idea. This makes it possible to evaluate the market adaptability of an idea and increase the chances of success.

[0064] The analysis unit can use the emotion estimation function to analyze the user's emotions when entering ideas and provide relaxation music or a message to alleviate negative emotions. For example, when entering ideas, the generation AI analyzes the user's facial expressions and voice, and if negative emotions are detected, the analysis unit plays relaxation music. The analysis unit also uses the emotion estimation function to analyze the user's emotions when entering ideas and provides a message to alleviate negative emotions. For example, if the user is feeling anxious, an encouraging message is displayed. Furthermore, when the user enters an idea, the analysis unit allows the generation AI to provide real-time feedback based on the emotion estimation data, providing advice to alleviate negative emotions. This helps to alleviate the user's negative emotions and encourages idea entry.

[0065] The executor suggestion unit can suggest the most suitable executor based on the executor's past project success rate and evaluation. For example, the generation AI analyzes the executor's past project success rate and suggests the most suitable executor. The executor suggestion unit also builds a system in which the generation AI suggests the most suitable executor based on the executor's evaluation data. For example, the executor is selected based on user evaluations and feedback. The executor suggestion unit also analyzes the executor's past project history and suggests the most suitable executor based on the success rate and evaluation. This makes it possible to suggest the most suitable executor based on the executor's past performance.

[0066] The executor suggestion unit can make optimal matches by considering not only the executor's skill set but also their cultural background and working style. For example, the executor suggestion unit uses a generation AI to make optimal matches by considering not only the executor's skill set but also their cultural background and working style. The executor suggestion unit also analyzes the executor's cultural background and working style, and builds a system in which the generation AI makes optimal matches. For example, the executor suggestion unit suggests executors suitable for remote work. The executor suggestion unit also uses a generation AI to comprehensively evaluate the executor's skill set, cultural background, and working style and propose the most suitable executor. This makes it possible to make optimal matches by considering not only the executor's skill set but also their cultural background and working style.

[0067] The executor suggestion unit uses the emotion estimation function to analyze the emotional state of the executor and preferentially suggest executors who are highly motivated for the project. For example, the executor suggestion unit uses a generation AI to analyze the emotional state of the executor and preferentially suggest executors who are highly motivated for the project. The executor suggestion unit also uses the emotion estimation function to analyze the emotional state of the executor in real time and build a system that suggests executors who are highly motivated. For example, executors with high emotion scores are given priority. The executor suggestion unit also analyzes the emotional state of the executor and the generation AI suggests executors who are highly motivated for the project. This makes it possible to preferentially suggest executors who are highly motivated for the project.

[0068] The supporter suggestion unit can analyze the supporter's past investment history and success stories, and suggest the most suitable supporter. For example, the supporter suggestion unit uses a generation AI to analyze the supporter's past investment history and suggest the most suitable supporter. The supporter suggestion unit also builds a system in which the generation AI analyzes the supporter's success stories, and suggests the most suitable supporter. For example, the supporter suggestion unit preferentially suggests supporters with a high success rate. The supporter suggestion unit also uses a generation AI to suggest the most suitable supporter based on the supporter's past investment history and success stories. This makes it possible to suggest the most suitable supporter based on the supporter's past performance.

[0069] The backer suggestion unit can suggest backers who can contribute to the success of a project, taking into account the backer's network and influence. For example, the generation AI analyzes the backer's network and influence and suggests backers who can contribute to the success of a project. The backer suggestion unit also builds a system in which the generation AI evaluates the influence of backers and suggests the most suitable backers. For example, the generation AI prioritizes suggesting backers who have a high level of influence within the industry. The backer suggestion unit also suggests backers who can contribute to the success of a project, based on the backer's network and influence. This makes it possible to suggest the most suitable backers, taking into account the backer's network and influence.

[0070] The supporter suggestion unit uses the emotion estimation function to analyze the emotional state of the supporter and can preferentially suggest supporters who are highly enthusiastic about the project. For example, the supporter suggestion unit uses a generation AI to analyze the emotional state of the supporter and preferentially suggest supporters who are highly enthusiastic about the project. The supporter suggestion unit also uses the emotion estimation function to build a system that analyzes the emotional state of the supporter in real time and suggests supporters who are highly enthusiastic. For example, supporters with high emotion scores are given priority. The supporter suggestion unit also analyzes the emotional state of the supporter and the generation AI suggests supporters who are highly enthusiastic about the project. This allows supporters who are highly enthusiastic about the project to be preferentially suggested.

[0071] The supporter suggestion unit can analyze the supporter's field of expertise and interests and suggest the supporter best suited to the project. For example, the generation AI of the supporter suggestion unit analyzes the supporter's field of expertise and interests and suggests the supporter best suited to the project. The supporter suggestion unit also builds a system in which the supporter's interests are evaluated and the generation AI suggests the most suitable supporter. For example, the selection is made based on the supporter's past project history. The supporter suggestion unit also builds a system in which the generation AI suggests the most suitable supporter for the project based on the supporter's field of expertise and interests. This makes it possible to suggest the most suitable supporter taking into account the supporter's field of expertise and interests.

[0072] The supporter suggestion unit can analyze supporters' past feedback and reviews and suggest highly reliable supporters. For example, the generation AI analyzes supporters' past feedback and reviews and suggests highly reliable supporters. The supporter suggestion unit also constructs a system in which the generation AI evaluates supporters' reviews and suggests highly reliable supporters. For example, the supporter suggestion unit selects supporters with many positive reviews. The generation AI also suggests highly reliable supporters based on the supporters' past feedback and reviews. This makes it possible to suggest highly reliable supporters.

[0073] The supporter suggestion unit can use the emotion estimation function to analyze the emotional state of the supporter and suggest supporters with positive emotions. For example, the supporter suggestion unit uses a generation AI to analyze the emotional state of the supporter and suggest supporters with positive emotions. The supporter suggestion unit also uses the emotion estimation function to build a system that analyzes the emotional state of the supporter in real time and suggests supporters with positive emotions. For example, the supporter suggestion unit selects supporters based on emotion scores. The supporter suggestion unit also analyzes the emotional state of the supporter and the generation AI suggests supporters with positive emotions. This makes it possible to suggest supporters with positive emotions.

[0074] The participant suggestion unit can suggest optimal participants based on the participants' past project participation history and evaluations. For example, the generation AI analyzes the participants' past project participation history and suggests optimal participants. The participant suggestion unit also builds a system in which the generation AI suggests optimal participants based on the participants' evaluation data. For example, participants are selected based on user evaluations and feedback. The generation AI also analyzes the participants' past project history and suggests optimal participants based on their success rate and evaluations. This makes it possible to suggest optimal participants based on the participants' past performance.

[0075] The participant suggestion unit can perform optimal matching by considering not only the skill sets of participants but also their interests and hobbies. For example, the generation AI of the participant suggestion unit performs optimal matching by considering not only the skill sets of participants but also their interests and hobbies. The participant suggestion unit also analyzes the interests and hobbies of participants, and builds a system in which the generation AI performs optimal matching. For example, it proposes participants who share common hobbies. The generation AI of the participant suggestion unit also comprehensively evaluates the skill sets, interests, and hobbies of participants and proposes the most suitable participants. This allows optimal matching to be performed by considering not only the skill sets of participants but also their interests and hobbies.

[0076] The participant suggestion unit uses the emotion estimation function to analyze the emotional state of participants and can prioritize suggesting participants who are highly motivated for the project. For example, the participant suggestion unit uses a generation AI to analyze the emotional state of participants and prioritize suggesting participants who are highly motivated for the project. The participant suggestion unit also uses the emotion estimation function to analyze the emotional state of participants in real time and build a system that suggests highly motivated participants. For example, participants with high emotion scores are prioritized. The participant suggestion unit also analyzes the emotional state of participants and the generation AI suggests participants who are highly motivated for the project. This allows participants who are highly motivated for the project to be prioritized.

[0077] The participant suggestion unit also takes into account the geographical location information of the participants and can suggest participants that suit the characteristics of each region. For example, the generation AI of the participant suggestion unit takes into account the geographical location information of the participants and suggests participants that suit the characteristics of each region. The participant suggestion unit also builds a system in which the generation AI analyzes the geographical location information of the participants and suggests participants that suit the characteristics of each region. For example, participants that correspond to the culture and market needs of the region are selected. The participant suggestion unit also takes into account the geographical location information of the participants and the generation AI suggests participants that suit the characteristics of each region. This makes it possible to suggest participants that suit the characteristics of each region.

[0078] The participant suggestion unit can analyze the social media activity of participants and suggest the most suitable participants based on the latest activity status. In the participant suggestion unit, for example, a generation AI analyzes the social media activity of participants and suggests the most suitable participants based on the latest activity status. The participant suggestion unit also builds a system in which the generation AI analyzes the social media activity of participants and suggests participants based on the latest activity status. For example, participants are selected based on ratings and feedback on social media. In addition, the participant suggestion unit can have the generation AI evaluate the latest activity status based on the participants' social media activity and suggest the most suitable participants. This makes it possible to suggest the most suitable participants based on the latest activity status.

[0079] The participant suggestion unit uses the emotion estimation function to analyze the emotional state of participants and can suggest participants who are low in stress and have positive emotions. For example, the participant suggestion unit uses a generation AI to analyze the emotional state of participants and suggest participants who are low in stress and have positive emotions. The participant suggestion unit also uses the emotion estimation function to build a system that analyzes the emotional state of participants in real time and suggests participants who are low in stress and have positive emotions. For example, participants with high emotion scores are given priority. The participant suggestion unit also analyzes the emotional state of participants and the generation AI suggests participants who are low in stress and have positive emotions. This makes it possible to suggest participants who are low in stress and have positive emotions.

[0080] The notification unit can provide past success stories and reference materials when notifying the matching result, thereby deepening the idea provider's understanding. For example, when the generation AI notifies the matching result, the notification unit can provide past success stories and reference materials to deepen the idea provider's understanding. The notification unit also builds a system in which the generation AI provides reference materials when notifying the matching result, thereby deepening the idea provider's understanding. For example, it can provide related technical literature and market reports. The notification unit can also deepen the idea provider's understanding based on past success stories and reference materials when the generation AI notifies the matching result. In this way, the provision of past success stories and reference materials can deepen the idea provider's understanding.

[0081] The notification department can automatically track the progress of the project and report on it periodically during follow-up. For example, the notification department builds a system where the generation AI automatically tracks the progress of the project and reports on it periodically. Furthermore, the notification department can track the progress of the project in real time during follow-up and report on it periodically. For example, the notification department can notify of important milestones in the project. Furthermore, the notification department can develop a system where the generation AI automatically tracks the progress of the project and reports on it periodically. This makes it possible to automatically track the progress of the project and report on it periodically.

[0082] The notification unit can use the emotion estimation function to analyze the user's emotions when notified of the matching result and provide a message to elicit positive emotions. For example, the generation AI in the notification unit analyzes the user's emotions when notified of the matching result and provides a message to elicit positive emotions. The notification unit also uses the emotion estimation function to build a system that analyzes the user's emotions in real time when notified of the matching result and provides a message to elicit positive emotions. For example, words of encouragement or introducing success stories can be presented. The notification unit can also use the generation AI in the notification unit to analyze the user's emotions when notified of the matching result and provide a message to elicit positive emotions. This makes it possible to provide a message to elicit positive emotions.

[0083] The notification unit can use different communication channels (email, SMS, app notification) when notifying the user of the matching result, and can notify the user according to their preferences. For example, the notification unit uses different communication channels when the generation AI notifies the user of the matching result, according to their preferences. In addition, the notification unit builds a system in which the generation AI analyzes the user's past notification history and suggests the optimal communication channel when notifying the user of the matching result. For example, the channel that has had the most positive response in the past is given priority. In addition, the notification unit uses different communication channels when the generation AI notifies the user of the matching result, according to their preferences. This allows notifications to be tailored to the user's preferences.

[0084] The notification unit can collect user feedback during follow-up and utilize it to improve the accuracy of the next match. For example, the notification unit constructs a system in which the generation AI collects user feedback during follow-up and utilizes it to improve the accuracy of the next match. Furthermore, the notification unit allows the generation AI to collect user feedback in real time during follow-up and improve the accuracy of the next match. For example, the matching conditions are adjusted based on the user's opinion. Furthermore, the notification unit allows the generation AI to collect user feedback during follow-up and utilizes it to improve the accuracy of the next match. In this way, user feedback can be collected and the accuracy of the next match can be improved.

[0085] The notification unit can use the emotion estimation function to analyze the user's emotions during follow-up and provide support to reduce negative emotions. For example, the notification unit uses the generation AI to analyze the user's emotions during follow-up and provide support to reduce negative emotions. The notification unit also uses the emotion estimation function to build a system that analyzes the user's emotions during follow-up in real time and provides support to reduce negative emotions. For example, a positive suggestion is made when the emotion score is low. The notification unit also uses the generation AI to analyze the user's emotions during follow-up and provide support to reduce negative emotions. This makes it possible to provide support to reduce negative emotions.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] When analyzing the content of an idea, the analysis unit can automatically detect potential problems and risks in the idea using natural language processing technology. For example, the generation AI can use text analysis technology to extract potential legal risks and technical issues from the content of the idea. The analysis unit can also have the generation AI analyze the content of the idea and evaluate the risks based on past failure cases. Furthermore, the analysis unit can have the generation AI analyze the content of the idea and visually display potential problems, making it easier for users to understand the risks. This enables risk management to increase the feasibility of ideas.

[0088] The executor suggestion unit can suggest compatible executors by taking into account not only the executor's skill set but also their past collaboration history. For example, the generation AI analyzes the executor's past collaboration history and makes suggestions based on successful project partnerships. The executor suggestion unit also builds a system in which the generation AI analyzes the executor's communication style and working style to suggest compatible executors. Furthermore, the executor suggestion unit can also suggest compatible executors based on the executor's past feedback. This strengthens cooperative relationships between executors and increases the project success rate.

[0089] The backer suggestion unit can analyze the investment portfolio of a backer and suggest the most suitable backer from the perspective of risk diversification. For example, the generation AI analyzes the investment portfolio of a backer and suggests backers from different industries or fields for risk diversification. The backer suggestion unit also builds a system in which the generation AI analyzes the investment history of a backer and suggests the most suitable backer from the perspective of risk diversification. Furthermore, the backer suggestion unit can also suggest backers for risk diversification based on the generation AI's investment portfolio. This can diversify the project's risks and increase the chances of success.

[0090] The analysis unit uses the emotion estimation function to analyze the user's emotions when entering ideas and can provide real-time feedback to elicit positive emotions. For example, the generation AI analyzes the user's facial expressions and voice to estimate emotions. To elicit positive emotions, it provides encouraging messages and success stories in real time. The analysis unit also uses the emotion estimation function to analyze the user's emotions when entering ideas and provides feedback to elicit positive emotions. For example, if the user expresses negative emotions, it plays relaxing music. The analysis unit also enables the generation AI to provide real-time feedback based on the emotion estimation data when the user enters an idea, providing advice to elicit positive emotions. This can elicit positive emotions from the user and encourage idea entry.

[0091] The notification unit can use the emotion estimation function to analyze the user's emotions when notified of the matching result and provide a message to elicit positive emotions. For example, the generation AI can analyze the user's emotions when notified of the matching result and provide a message to elicit positive emotions. The notification unit also uses the emotion estimation function to build a system that analyzes the user's emotions in real time when notified of the matching result and provides a message to elicit positive emotions. For example, it can introduce words of encouragement or success stories. The notification unit also uses the generation AI to analyze the user's emotions when notified of the matching result and provide a message to elicit positive emotions. This makes it possible to provide a message to elicit positive emotions.

[0092] When analyzing the content of an idea, the analysis unit can refer to trend data from different industries and evaluate the market adaptability of the idea. For example, the generation AI automatically collects trend data from different industries and evaluates the market adaptability of the idea. The analysis unit also builds a system in which the generation AI analyzes the content of an idea and refers to trend data from different industries to evaluate the market adaptability of the idea. For example, the analysis unit makes an evaluation based on industry reports and market research data. When analyzing an idea, the generation AI also refers to trend data from different industries and visually displays the market adaptability of the idea. This makes it possible to evaluate the market adaptability of an idea and increase the chances of success.

[0093] The analysis unit uses the emotion estimation function to analyze the user's emotions when entering ideas and can provide relaxation music or messages to alleviate negative emotions. For example, the generation AI analyzes the user's facial expressions and voice and plays relaxation music if it detects negative emotions. The analysis unit also uses the emotion estimation function to analyze the user's emotions when entering ideas and can provide messages to alleviate negative emotions. For example, an encouraging message can be displayed if the user is feeling anxious. Furthermore, when the user enters an idea, the analysis unit allows the generation AI to provide real-time feedback based on the emotion estimation data and offer advice to alleviate negative emotions. This can alleviate the user's negative emotions and encourage idea entry.

[0094] The backer suggestion unit can suggest backers who can contribute to the success of a project, taking into account the backer's network and influence. For example, the generation AI analyzes the backer's network and influence and suggests backers who can contribute to the success of a project. The backer suggestion unit also builds a system in which the generation AI evaluates the influence of backers and suggests the most suitable backers. For example, the generation AI prioritizes suggesting backers with high influence within the industry. The backer suggestion unit also suggests backers who can contribute to the success of a project, based on the backer's network and influence. This makes it possible to suggest the most suitable backers, taking into account the backer's network and influence.

[0095] The participant suggestion unit also takes into account the geographical location information of participants and can suggest participants that suit the characteristics of each region. For example, the generation AI takes into account the geographical location information of participants and suggests participants that suit the characteristics of each region. The participant suggestion unit also analyzes the geographical location information of participants and builds a system in which the generation AI suggests participants that suit the characteristics of each region. For example, participants that correspond to the culture and market needs of the region are selected. The participant suggestion unit also takes into account the geographical location information of participants and the generation AI suggests participants that suit the characteristics of each region. This makes it possible to suggest participants that suit the characteristics of each region.

[0096] The participant suggestion unit uses the emotion estimation function to analyze the emotional state of participants and prioritize suggesting participants who are highly motivated for the project. For example, the generation AI analyzes the emotional state of participants and prioritizes suggesting participants who are highly motivated for the project. The participant suggestion unit also uses the emotion estimation function to analyze the emotional state of participants in real time and build a system that suggests highly motivated participants. For example, participants with high emotion scores are prioritized. The participant suggestion unit also analyzes the emotional state of participants and the generation AI suggests participants who are highly motivated for the project. This allows participants who are highly motivated for the project to be prioritized.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The idea input unit receives an idea from the user. For example, the user inputs the idea in text format. The idea input unit can also support voice input and handwritten input. Step 2: The analysis unit analyzes the ideas input by the idea input unit. For example, the generation AI analyzes the content of the ideas using text analysis technology. The analysis unit can also analyze the needs of the ideas using data mining technology. Step 3: The executor suggestion unit suggests appropriate executors based on the idea analyzed by the analysis unit. For example, the generation AI analyzes the executor's skill set and suggests the executor best suited to the idea. The executor suggestion unit can also make suggestions based on the executor's past project success rate. Step 4: The backer suggestion unit suggests suitable backers based on the idea analyzed by the analysis unit. For example, the generation AI analyzes the investment history of backers and suggests the best backers for the idea. The backer suggestion unit can also make suggestions based on the backer's network and influence.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 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. an idea input section for inputting ideas; an analysis unit that analyzes the idea input by the idea input unit; an implementer suggestion unit that suggests an appropriate implementer based on the idea analyzed by the analysis unit; a supporter suggestion unit that suggests an appropriate supporter based on the idea analyzed by the analysis unit; A system characterized by:

2. The idea input unit Referencing users' past ideas and project history to automatically complete relevant information 2. The system of claim 1.

3. The analysis unit When analyzing the content of the idea, search the patent database to clarify the differences between the idea and existing technologies and competing ideas.

2. The system of claim 1.

4. The analysis unit Analyzes user emotions when submitting ideas and provides real-time feedback to elicit positive emotions 2. The system of claim 1.

5. The idea input unit It also supports voice input and handwriting input, allowing users to input their ideas in the way that is most convenient for them.

2. The system of claim 1.

6. The analysis unit When analyzing the content of the idea, refer to trend data from different industries to evaluate the market adaptability of the idea.

2. The system of claim 1.

7. The analysis unit Analyzes the user's emotions when entering ideas and provides relaxation music and messages to alleviate negative emotions.

2. The system of claim 1.

8. The executor suggestion unit Propose the most suitable executor based on the executor's past project success rate and evaluation 2. The system of claim 1.

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