System

Abstract

An object of a system according to an embodiment is to realize appropriate matching and efficient cooperation between partners.SOLUTION: A system includes a collection part, an analysis part, and a cooperation part. The collection unit collects information of each partner. The analysis unit analyzes the information collected by the collection unit and performs appropriate matching. The cooperation unit provides a function for the partners matched by the analysis unit to efficiently cooperate with each other.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 do not adequately match partners and provide efficient cooperation, leaving room for improvement.

[0005] The system according to the embodiment aims to realize appropriate matching and efficient cooperation between partners. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a collaboration unit. The collection unit collects information about each partner. The analysis unit analyzes the information collected by the collection unit and performs appropriate matching. The collaboration unit provides a function that enables partners matched by the analysis unit to efficiently collaborate with each other. [Effects of the Invention]

[0007] The system according to the embodiment can realize appropriate matching and efficient cooperation between partners. [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 system according to an embodiment of the present invention uses a generation AI to match solution partners, sales partners, and innovative partners. The matching system collects information about each partner, and the generation AI analyzes the collected information to perform optimal matching. This allows the partners to cooperate efficiently and promote business success. For example, the matching system collects detailed information about each partner's business, strengths, needs, etc., such as the service content provided by the solution partner, the sales channels of the sales partner, and the technical capabilities of the innovative partner. Next, the matching system uses a generation AI to analyze the collected information. The generation AI analyzes the information about each partner and performs optimal matching. For example, the matching system matches the services provided by the solution partner with the sales channels of the sales partner, and furthermore, adds the technical capabilities of the innovative partner, creating new business opportunities. Partners matched by the generation AI can cooperate efficiently. For example, the sales partner sells the services provided by the solution partner, and the innovative partner provides technical support, promoting business success. This allows the matching system to efficiently collaborate with each partner and promote business success. For example, new business opportunities can be created when a sales partner sells the services offered by a solution partner and an innovative partner provides technical support. Furthermore, the generative AI can make optimal matches, maximizing the strengths of each partner.

[0029] The matching system according to the embodiment includes a collection unit, an analysis unit, and a collaboration unit. The collection unit collects information about each partner. The information about each partner includes, for example, business details, strengths, and needs, but is not limited to these examples. The collection unit can collect information by, for example, retrieving information from a database or conducting a questionnaire survey. The collection unit can also automatically collect information using AI. For example, the collection unit can collect information from the Internet using web scraping technology. The analysis unit uses a generation AI to analyze the information collected by the collection unit and perform optimal matching. The analysis can be performed by, for example, data mining or statistical analysis, but is not limited to these examples. For example, the generation AI analyzes the information about each partner and performs optimal matching using a similarity measure or a specific algorithm. For example, the generation AI performs matching so that the services provided by the solution partner match with the sales channels of the sales partner and, by adding the technical capabilities of the innovative partner, create new business opportunities. The collaboration unit provides specific support to help the matched partners collaborate efficiently. The cooperation unit can provide specific support, such as technical support and marketing support. For example, the cooperation unit promotes business success by having the sales partner sell the services provided by the solution partner and the innovative partner provide technical support. As a result, the matching system according to the embodiment can efficiently collect, analyze, and match information on each partner to promote cooperation.

[0030] The collection unit can collect information about each partner's business details, strengths, needs, and other information. For example, the collection unit collects each partner's business details. The business details include specific types and scopes, such as manufacturing or service. The collection unit also collects each partner's strengths. The strengths include specific details and evaluation criteria, such as technical capabilities and market share. The collection unit also collects each partner's needs. The needs include specific details and evaluation criteria, such as technical needs and business needs. By collecting detailed information about each partner, more accurate matching is possible. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information about each partner into AI, which then automatically collects the information.

[0031] The analysis unit can analyze the collected information and perform optimal matching. The analysis unit can, for example, analyze the collected information using data mining techniques. Data mining techniques include, for example, clustering, classification, and regression analysis. The analysis unit can also analyze the collected information using statistical analysis techniques. Statistical analysis techniques include, for example, correlation analysis, analysis of variance, and regression analysis. The analysis unit can also analyze the collected information and perform optimal matching using a generation AI. The generation AI can, for example, analyze information about each partner and perform optimal matching using a similarity measure or a specific algorithm. This makes it possible to match optimal partners by analyzing the collected information. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the collected information into the generation AI, which can then automatically perform the analysis.

[0032] The collaboration department can provide specific support for matched partners to collaborate efficiently. The collaboration department, for example, provides technical support. Technical support includes, for example, resolving technical issues and providing technical advice. The collaboration department can also provide marketing support. Marketing support includes, for example, developing marketing strategies and creating marketing materials. The collaboration department can also provide project management support. Project management support includes, for example, project progress management and resource allocation. This allows specific support to be provided for matched partners to collaborate efficiently. Some or all of the above-described processing in the collaboration department can be performed using, or without, AI. For example, the collaboration department can input the details of technical support into AI, which can then automatically provide support.

[0033] The analysis unit can perform matching to create new business opportunities by matching the services provided by the solution partner with the sales partner's sales channels and further adding the innovative partner's technical capabilities. The analysis unit, for example, analyzes the services provided by the solution partner and evaluates whether the services match the sales partner's sales channels. The evaluation includes, for example, the characteristics of the service, the characteristics of the sales channel, and the degree of match with the customer base. The analysis unit can also analyze the innovative partner's technical capabilities and evaluate how the technical capabilities can contribute to the solution partner's services and the sales partner's sales channels. The evaluation includes, for example, the innovativeness of the technology, the scope of application of the technology, and the market value of the technology. Furthermore, the analysis unit can use a generation AI to comprehensively analyze the services provided by the solution partner, the sales partner's sales channels, and the innovative partner's technical capabilities, and perform matching to create new business opportunities. This enables optimal matching to create new business opportunities. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information about the solution partner's services, the sales partner's sales channels, and the innovative partner's technical capabilities into the generation AI, which can then automatically perform matching.

[0034] The collaboration department can promote business success by having sales partners sell services provided by solution partners and innovative partners provide technical support. For example, the collaboration department provides support for sales partners to sell services provided by solution partners. Examples of support include developing sales strategies, creating sales materials, and conducting sales training. The collaboration department can also provide support for innovative partners to provide technical support. Examples of support include resolving technical issues, providing technical advice, and conducting technical training. Furthermore, the collaboration department can provide project management support to enable solution partners, sales partners, and innovative partners to collaborate efficiently. Examples of support include project progress management, resource allocation, and communication coordination. This can provide specific collaboration methods to promote business success. Some or all of the above-described processing in the collaboration department can be performed using, or without, AI. For example, the collaboration department can input sales strategy development and technical advice into AI, which can then automatically provide support.

[0035] The collection unit can analyze the past cooperation history of each partner and select the optimal information collection method. For example, the collection unit analyzes the information collection methods (questionnaires, interviews, etc.) used by each partner in the past and selects the optimal method. The collection unit can also select the most effective information collection method from the past cooperation history of each partner. Furthermore, the collection unit can adjust the frequency and timing of information collection based on the past cooperation history of each partner. This enables efficient information collection by selecting the optimal information collection method based on the past cooperation history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past cooperation history of each partner into AI, and the AI ​​can automatically select the optimal information collection method.

[0036] When collecting information, the collection unit can filter the information based on each partner's current project or area of ​​interest. For example, the collection unit collects only information related to each partner's current project. The collection unit can also prioritize collecting relevant information based on each partner's area of ​​interest. Furthermore, the collection unit can filter unnecessary information based on each partner's current project or area of ​​interest. This allows for efficient collection of highly relevant information by filtering information based on the current project or area of ​​interest. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input information on each partner's current project or area of ​​interest into AI, and the AI ​​can automatically perform filtering.

[0037] When collecting information, the collection unit can select an appropriate collection means according to each partner's input method. For example, if each partner prefers voice input, the collection unit can collect information by voice. Furthermore, if each partner prefers text input, the collection unit can also collect information by text. Furthermore, if each partner prefers image input, the collection unit can also collect information by image. This enables efficient information collection by selecting the optimal collection means according to each partner's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information about each partner's input method into AI, and the AI ​​can automatically select the optimal collection means.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each partner. For example, the collection unit can prioritize collecting information about nearby business partners based on the geographical location information of each partner. The collection unit can also collect information about business opportunities specific to a region based on the geographical location information of each partner. Furthermore, the collection unit can prioritize collecting information about regional market trends based on the geographical location information of each partner. This allows for efficient collection of highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of each partner into AI, and the AI ​​can automatically prioritize collecting highly relevant information.

[0039] When collecting information, the collection unit can analyze the social media activities of each partner and collect highly relevant information. For example, the collection unit can analyze the content of each partner's social media posts to collect related business information. The collection unit can also collect related information by referring to the activities of each partner's friends and followers on social media. Furthermore, the collection unit can also collect related business information based on each partner's check-in information on social media. In this way, highly relevant information can be collected efficiently by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information on each partner's social media activities into AI, which can then automatically collect highly relevant information.

[0040] When collecting information, the collection unit can customize the collection method by reflecting past feedback from each partner. For example, the collection unit improves the information collection method based on past feedback from each partner. The collection unit can also adjust the timing of information collection by reflecting past feedback from each partner. Furthermore, the collection unit can customize the content of information collection by referring to past feedback from each partner. In this way, by reflecting past feedback, the collection method can be optimized and efficient information collection becomes possible. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback from each partner into AI, and the AI ​​can automatically customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of each partner. For example, the analysis unit can provide detailed analysis results to partners with high importance. The analysis unit can also provide concise analysis results to partners with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of each partner. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of each partner. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information on the importance of each partner to the generation AI, and the generation AI can automatically adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of each partner. For example, the analysis unit can apply an analysis algorithm specialized for the services provided to solution partners. The analysis unit can also apply an analysis algorithm specialized for sales channels to sales partners. Furthermore, the analysis unit can apply an analysis algorithm specialized for technical capabilities to innovative partners. In this way, highly accurate analysis is possible by applying an analysis algorithm according to the category of each partner. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input information on the category of each partner into the generation AI, and the generation AI can automatically apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of each partner. The analysis unit, for example, improves the analysis algorithm based on past analysis results of each partner. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results of each partner. Furthermore, the analysis unit can analyze past analysis results of each partner and select the optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information on past analysis results of each partner into the generation AI, and the generation AI can automatically improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the order of analysis based on the submission time of each partner. For example, the analysis unit prioritizes the analysis of a partner who submitted earlier. The analysis unit can also postpone the analysis of a partner who submitted later. Furthermore, the analysis unit can adjust the analysis schedule based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information on the submission time of each partner into the generation AI, and the generation AI can automatically determine the order of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of each partner. For example, the analysis unit prioritizes the analysis of highly related partners. The analysis unit can also postpone the analysis of less related partners. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of each partner. This enables efficient analysis by adjusting the order of analysis based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information on the relevance of each partner to the generation AI, and the generation AI can automatically adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the expertise level of each partner. For example, the analysis unit can provide analysis results that use a lot of technical terminology to partners with a high level of expertise. The analysis unit can also provide analysis results that avoid technical terminology to partners with a low level of expertise. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the expertise level of each partner. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terminology according to the expertise level. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information about the expertise level of each partner into the generation AI, which can then automatically adjust the use of technical terminology.

[0047] During cooperation, the cooperation unit can analyze each partner's past cooperation history and select the optimal cooperation method. For example, the cooperation unit selects the optimal cooperation method based on each partner's past cooperation history. The cooperation unit can also analyze each partner's past cooperation history and propose an effective cooperation method. Furthermore, the cooperation unit can improve the cooperation method by referring to each partner's past cooperation history. This enables efficient cooperation by selecting the optimal cooperation method based on the past cooperation history. Some or all of the above-mentioned processing in the cooperation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooperation unit can input information on each partner's past cooperation history into AI, and the AI ​​can automatically select the optimal cooperation method.

[0048] During collaboration, the collaboration unit can customize the means of collaboration based on each partner's current project. For example, the collaboration unit can suggest collaboration means related to each partner's current project. The collaboration unit can also customize the collaboration method based on each partner's current project. Furthermore, the collaboration unit can select the optimal collaboration means according to each partner's current project. This enables more effective collaboration by customizing the means of collaboration based on the current project. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input information about each partner's current project into AI, which can then automatically select the optimal collaboration means.

[0049] The cooperation unit can improve the method of cooperation by reflecting the feedback of each partner during cooperation. For example, the cooperation unit improves the method of cooperation based on the feedback of each partner. The cooperation unit can also adjust the timing of cooperation by reflecting the feedback of each partner. Furthermore, the cooperation unit can customize the content of cooperation by referring to the feedback of each partner. In this way, by reflecting the feedback, the method of cooperation can be optimized and efficient cooperation can be achieved. Some or all of the above-mentioned processing in the cooperation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooperation unit can input the feedback information of each partner into AI, and the AI ​​can automatically improve the method of cooperation.

[0050] During collaboration, the collaboration unit can select the optimal collaboration method by taking into account the geographical location information of each partner. For example, the collaboration unit prioritizes collaboration with nearby partners based on the geographical location information of each partner. The collaboration unit can also propose a collaboration method specific to a region based on the geographical location information of each partner. Furthermore, the collaboration unit can select a collaboration method that corresponds to regional market trends based on the geographical location information of each partner. This allows for the selection of the optimal collaboration method by taking geographical location information into consideration, enabling efficient collaboration. Some or all of the above-described processing in the collaboration unit may be performed using, or without, AI. For example, the collaboration unit can input the geographical location information of each partner into AI, which can then automatically select the optimal collaboration method.

[0051] During collaboration, the collaboration unit can analyze the social media activities of each partner and suggest means of collaboration. For example, the collaboration unit can analyze the content of each partner's social media posts and suggest relevant means of collaboration. The collaboration unit can also suggest means of collaboration based on the activities of each partner's friends and followers on social media. Furthermore, the collaboration unit can suggest relevant means of collaboration based on each partner's social media check-in information. In this way, by analyzing social media activities, highly relevant means of collaboration can be suggested. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input information on each partner's social media activities into AI, which can then automatically suggest relevant means of collaboration.

[0052] During collaboration, the collaboration unit can customize the collaboration method by reflecting each partner's past feedback. For example, the collaboration unit can improve the collaboration method based on each partner's past feedback. The collaboration unit can also adjust the timing of collaboration by reflecting each partner's past feedback. Furthermore, the collaboration unit can customize the content of collaboration by referring to each partner's past feedback. In this way, by reflecting past feedback, the collaboration method can be optimized and efficient collaboration can be achieved. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input information on each partner's past feedback into AI, and the AI ​​can automatically customize the collaboration method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can analyze the past cooperation history of each partner and select the optimal information collection method. For example, it analyzes the information collection methods (surveys, interviews, etc.) used by each partner in the past and selects the optimal method. The collection unit can also select the most effective information collection method from the past cooperation history of each partner. Furthermore, the collection unit can adjust the frequency and timing of information collection based on the past cooperation history of each partner. This enables efficient information collection by selecting the optimal information collection method based on the past cooperation history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past cooperation history of each partner into AI, and the AI ​​can automatically select the optimal information collection method.

[0055] When collecting information, the collection unit can filter the information based on each partner's current project or area of ​​interest. For example, the collection unit can collect only information related to each partner's current project. The collection unit can also prioritize collecting relevant information based on each partner's area of ​​interest. Furthermore, the collection unit can filter unnecessary information based on each partner's current project or area of ​​interest. This allows for efficient collection of highly relevant information by filtering information based on the current project or area of ​​interest. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input information on each partner's current project or area of ​​interest into AI, and the AI ​​can automatically perform filtering.

[0056] When collecting information, the collection unit can select an appropriate collection means according to each partner's input method. For example, if each partner prefers voice input, the collection unit can collect information by voice. Also, if each partner prefers text input, the collection unit can collect information by text. Furthermore, if each partner prefers image input, the collection unit can collect information by image. This enables efficient information collection by selecting the optimal collection means according to each partner's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information about each partner's input method into AI, and the AI ​​can automatically select the optimal collection means.

[0057] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each partner. For example, the collection unit can prioritize collecting information about nearby business partners based on the geographical location information of each partner. The collection unit can also collect information about business opportunities specific to a region based on the geographical location information of each partner. Furthermore, the collection unit can prioritize collecting information about regional market trends based on the geographical location information of each partner. This allows for efficient collection of highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of each partner into AI, and the AI ​​can automatically prioritize collecting highly relevant information.

[0058] When collecting information, the collection unit can analyze the social media activities of each partner and collect highly relevant information. For example, it can analyze the content of each partner's social media posts to collect related business information. The collection unit can also collect related information by referring to the activities of each partner's friends and followers on social media. Furthermore, the collection unit can also collect related business information based on each partner's check-in information on social media. In this way, highly relevant information can be collected efficiently by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information on each partner's social media activities into AI, which can then automatically collect highly relevant information.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection department collects information about each partner. This information includes information about each partner's business operations, strengths, needs, etc. The collection department collects information by obtaining information from databases, conducting questionnaire surveys, automatically collecting information using AI, and collecting information from the Internet using web scraping technology. Step 2: The analysis unit uses generation AI to analyze the information collected by the collection unit and perform optimal matching. Analysis is performed using methods such as data mining and statistical analysis. The generation AI analyzes the information on each partner and performs optimal matching using similarity measures and specific algorithms. For example, it matches the services provided by a solution partner with the sales channel of a sales partner, and by adding the technical capabilities of an innovative partner, it performs matching that creates new business opportunities. Step 3: The Collaboration Department provides specific support to help the matched partners collaborate efficiently. The Collaboration Department provides specific support such as technical and marketing support. For example, the Sales Partner sells the services offered by the Solution Partner, and the Innovative Partner provides technical support, thereby promoting business success.

[0061] (Example 2) A matching system according to an embodiment of the present invention uses a generation AI to match solution partners, sales partners, and innovative partners. The matching system collects information about each partner, and the generation AI analyzes the collected information to perform optimal matching. This allows the partners to cooperate efficiently and promote business success. For example, the matching system collects detailed information about each partner's business, strengths, needs, etc., such as the service content provided by the solution partner, the sales channels of the sales partner, and the technical capabilities of the innovative partner. Next, the matching system uses a generation AI to analyze the collected information. The generation AI analyzes the information about each partner and performs optimal matching. For example, the matching system matches the services provided by the solution partner with the sales channels of the sales partner, and furthermore, adds the technical capabilities of the innovative partner, creating new business opportunities. Partners matched by the generation AI can cooperate efficiently. For example, the sales partner sells the services provided by the solution partner, and the innovative partner provides technical support, promoting business success. This allows the matching system to efficiently collaborate with each partner and promote business success. For example, new business opportunities can be created when a sales partner sells the services offered by a solution partner and an innovative partner provides technical support. Furthermore, the generative AI can make optimal matches, maximizing the strengths of each partner.

[0062] The matching system according to the embodiment includes a collection unit, an analysis unit, and a collaboration unit. The collection unit collects information about each partner. The information about each partner includes, for example, business details, strengths, and needs, but is not limited to these examples. The collection unit can collect information by, for example, retrieving information from a database or conducting a questionnaire survey. The collection unit can also automatically collect information using AI. For example, the collection unit can collect information from the Internet using web scraping technology. The analysis unit uses a generation AI to analyze the information collected by the collection unit and perform optimal matching. The analysis can be performed by, for example, data mining or statistical analysis, but is not limited to these examples. For example, the generation AI analyzes the information about each partner and performs optimal matching using a similarity measure or a specific algorithm. For example, the generation AI performs matching so that the services provided by the solution partner match with the sales channels of the sales partner and, by adding the technical capabilities of the innovative partner, create new business opportunities. The collaboration unit provides specific support to help the matched partners collaborate efficiently. The cooperation unit can provide specific support, such as technical support and marketing support. For example, the cooperation unit promotes business success by having the sales partner sell the services provided by the solution partner and the innovative partner provide technical support. As a result, the matching system according to the embodiment can efficiently collect, analyze, and match information on each partner to promote cooperation.

[0063] The collection unit can collect information about each partner's business details, strengths, needs, and other information. For example, the collection unit collects each partner's business details. The business details include specific types and scopes, such as manufacturing or service. The collection unit also collects each partner's strengths. The strengths include specific details and evaluation criteria, such as technical capabilities and market share. The collection unit also collects each partner's needs. The needs include specific details and evaluation criteria, such as technical needs and business needs. By collecting detailed information about each partner, more accurate matching is possible. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information about each partner into AI, which then automatically collects the information.

[0064] The analysis unit can analyze the collected information and perform optimal matching. The analysis unit can, for example, analyze the collected information using data mining techniques. Data mining techniques include, for example, clustering, classification, and regression analysis. The analysis unit can also analyze the collected information using statistical analysis techniques. Statistical analysis techniques include, for example, correlation analysis, analysis of variance, and regression analysis. The analysis unit can also analyze the collected information and perform optimal matching using a generation AI. The generation AI can, for example, analyze information about each partner and perform optimal matching using a similarity measure or a specific algorithm. This makes it possible to match optimal partners by analyzing the collected information. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the collected information into the generation AI, which can then automatically perform the analysis.

[0065] The collaboration department can provide specific support for matched partners to collaborate efficiently. The collaboration department, for example, provides technical support. Technical support includes, for example, resolving technical issues and providing technical advice. The collaboration department can also provide marketing support. Marketing support includes, for example, developing marketing strategies and creating marketing materials. The collaboration department can also provide project management support. Project management support includes, for example, project progress management and resource allocation. This allows specific support to be provided for matched partners to collaborate efficiently. Some or all of the above-described processing in the collaboration department can be performed using, or without, AI. For example, the collaboration department can input the details of technical support into AI, which can then automatically provide support.

[0066] The analysis unit can perform matching to create new business opportunities by matching the services provided by the solution partner with the sales partner's sales channels and further adding the innovative partner's technical capabilities. The analysis unit, for example, analyzes the services provided by the solution partner and evaluates whether the services match the sales partner's sales channels. The evaluation includes, for example, the characteristics of the service, the characteristics of the sales channel, and the degree of match with the customer base. The analysis unit can also analyze the innovative partner's technical capabilities and evaluate how the technical capabilities can contribute to the solution partner's services and the sales partner's sales channels. The evaluation includes, for example, the innovativeness of the technology, the scope of application of the technology, and the market value of the technology. Furthermore, the analysis unit can use a generation AI to comprehensively analyze the services provided by the solution partner, the sales partner's sales channels, and the innovative partner's technical capabilities, and perform matching to create new business opportunities. This enables optimal matching to create new business opportunities. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information about the solution partner's services, the sales partner's sales channels, and the innovative partner's technical capabilities into the generation AI, which can then automatically perform matching.

[0067] The collaboration department can promote business success by having sales partners sell services provided by solution partners and innovative partners provide technical support. For example, the collaboration department provides support for sales partners to sell services provided by solution partners. Examples of support include developing sales strategies, creating sales materials, and conducting sales training. The collaboration department can also provide support for innovative partners to provide technical support. Examples of support include resolving technical issues, providing technical advice, and conducting technical training. Furthermore, the collaboration department can provide project management support to enable solution partners, sales partners, and innovative partners to collaborate efficiently. Examples of support include project progress management, resource allocation, and communication coordination. This can provide specific collaboration methods to promote business success. Some or all of the above-described processing in the collaboration department can be performed using, or without, AI. For example, the collaboration department can input sales strategy development and technical advice into AI, which can then automatically provide support.

[0068] The collection unit can estimate the emotions of each partner and adjust the timing of information collection based on the estimated emotions. For example, if each partner is feeling stressed, the collection unit can delay the timing of information collection and have them provide information in a relaxed state. Furthermore, if each partner is relaxed, the collection unit can also accelerate the timing of information collection to efficiently collect information. Furthermore, if each partner is busy, the collection unit can adjust the timing of information collection and have them provide information in between work. This enables more appropriate information collection by adjusting the timing of information collection according to each partner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input each partner's emotion data into a generation AI, which can automatically estimate the emotions and adjust the timing of information collection.

[0069] The collection unit can analyze the past cooperation history of each partner and select the optimal information collection method. For example, the collection unit analyzes the information collection methods (questionnaires, interviews, etc.) used by each partner in the past and selects the optimal method. The collection unit can also select the most effective information collection method from the past cooperation history of each partner. Furthermore, the collection unit can adjust the frequency and timing of information collection based on the past cooperation history of each partner. This enables efficient information collection by selecting the optimal information collection method based on the past cooperation history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past cooperation history of each partner into AI, and the AI ​​can automatically select the optimal information collection method.

[0070] When collecting information, the collection unit can filter the information based on each partner's current project or area of ​​interest. For example, the collection unit collects only information related to each partner's current project. The collection unit can also prioritize collecting relevant information based on each partner's area of ​​interest. Furthermore, the collection unit can filter unnecessary information based on each partner's current project or area of ​​interest. This allows for efficient collection of highly relevant information by filtering information based on the current project or area of ​​interest. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input information on each partner's current project or area of ​​interest into AI, and the AI ​​can automatically perform filtering.

[0071] When collecting information, the collection unit can select an appropriate collection means according to each partner's input method. For example, if each partner prefers voice input, the collection unit can collect information by voice. Furthermore, if each partner prefers text input, the collection unit can also collect information by text. Furthermore, if each partner prefers image input, the collection unit can also collect information by image. This enables efficient information collection by selecting the optimal collection means according to each partner's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information about each partner's input method into AI, and the AI ​​can automatically select the optimal collection means.

[0072] The collection unit can estimate the emotions of each partner and determine the priority of information to be collected based on the estimated emotions. For example, if each partner is feeling stressed, the collection unit can postpone collecting less important information. Furthermore, if each partner is relaxed, the collection unit can also prioritize collecting more important information. Furthermore, if each partner is busy, the collection unit can collect only more important information. This allows important information to be collected efficiently by determining the priority of information according to each partner's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input each partner's emotion data into a generation AI, which can automatically estimate the emotions and determine the priority of information.

[0073] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each partner. For example, the collection unit can prioritize collecting information about nearby business partners based on the geographical location information of each partner. The collection unit can also collect information about business opportunities specific to a region based on the geographical location information of each partner. Furthermore, the collection unit can prioritize collecting information about regional market trends based on the geographical location information of each partner. This allows for efficient collection of highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of each partner into AI, and the AI ​​can automatically prioritize collecting highly relevant information.

[0074] When collecting information, the collection unit can analyze the social media activities of each partner and collect highly relevant information. For example, the collection unit can analyze the content of each partner's social media posts to collect related business information. The collection unit can also collect related information by referring to the activities of each partner's friends and followers on social media. Furthermore, the collection unit can also collect related business information based on each partner's check-in information on social media. In this way, highly relevant information can be collected efficiently by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information on each partner's social media activities into AI, which can then automatically collect highly relevant information.

[0075] When collecting information, the collection unit can customize the collection method by reflecting past feedback from each partner. For example, the collection unit improves the information collection method based on past feedback from each partner. The collection unit can also adjust the timing of information collection by reflecting past feedback from each partner. Furthermore, the collection unit can customize the content of information collection by referring to past feedback from each partner. In this way, by reflecting past feedback, the collection method can be optimized and efficient information collection becomes possible. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback from each partner into AI, and the AI ​​can automatically customize the collection method.

[0076] The analysis unit can estimate each partner's emotion and adjust the analysis presentation method based on the estimated emotion. For example, if each partner is stressed, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if each partner is relaxed. Furthermore, if each partner is busy, the analysis unit can provide a concise analysis result that focuses on the main points. This allows for more appropriate analysis results to be provided by adjusting the analysis presentation method according to each partner's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input each partner's emotion data into the generation AI, which can then automatically estimate the emotion and adjust the analysis presentation method.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of each partner. For example, the analysis unit can provide detailed analysis results to partners with high importance. The analysis unit can also provide concise analysis results to partners with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of each partner. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of each partner. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information on the importance of each partner to the generation AI, and the generation AI can automatically adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of each partner. For example, the analysis unit can apply an analysis algorithm specialized for the services provided to solution partners. The analysis unit can also apply an analysis algorithm specialized for sales channels to sales partners. Furthermore, the analysis unit can apply an analysis algorithm specialized for technical capabilities to innovative partners. In this way, highly accurate analysis is possible by applying an analysis algorithm according to the category of each partner. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input information on the category of each partner into the generation AI, and the generation AI can automatically apply an appropriate analysis algorithm.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of each partner. The analysis unit, for example, improves the analysis algorithm based on past analysis results of each partner. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results of each partner. Furthermore, the analysis unit can analyze past analysis results of each partner and select the optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information on past analysis results of each partner into the generation AI, and the generation AI can automatically improve the accuracy of the analysis.

[0080] The analysis unit can estimate the emotions of each partner and adjust the length of the analysis based on the estimated emotions. For example, if each partner is stressed, the analysis unit can provide a short and concise analysis result. Alternatively, if each partner is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if each partner is busy, the analysis unit can provide a concise analysis result. This allows for adjusting the length of the analysis according to each partner's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input each partner's emotion data into the generation AI, which can then automatically estimate the emotions and adjust the length of the analysis.

[0081] During analysis, the analysis unit can determine the order of analysis based on the submission time of each partner. For example, the analysis unit prioritizes the analysis of a partner who submitted earlier. The analysis unit can also postpone the analysis of a partner who submitted later. Furthermore, the analysis unit can adjust the analysis schedule based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information on the submission time of each partner into the generation AI, and the generation AI can automatically determine the order of analysis.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of each partner. For example, the analysis unit prioritizes the analysis of highly related partners. The analysis unit can also postpone the analysis of less related partners. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of each partner. This enables efficient analysis by adjusting the order of analysis based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information on the relevance of each partner to the generation AI, and the generation AI can automatically adjust the order of analysis.

[0083] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the expertise level of each partner. For example, the analysis unit can provide analysis results that use a lot of technical terminology to partners with a high level of expertise. The analysis unit can also provide analysis results that avoid technical terminology to partners with a low level of expertise. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the expertise level of each partner. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terminology according to the expertise level. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information about the expertise level of each partner into the generation AI, which can then automatically adjust the use of technical terminology.

[0084] The collaboration unit can estimate the emotions of each partner and adjust the collaboration method based on the estimated emotions. For example, if each partner is stressed, the collaboration unit can suggest a simple and efficient collaboration method. Furthermore, if each partner is relaxed, the collaboration unit can suggest a detailed collaboration method. Furthermore, if each partner is busy, the collaboration unit can suggest a concise and quick collaboration method. This enables more effective collaboration by adjusting the collaboration method according to each partner's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit can be performed using, for example, AI, or without AI. For example, the collaboration unit can input each partner's emotion data into the generation AI, which can automatically estimate the emotions and adjust the collaboration method.

[0085] During cooperation, the cooperation unit can analyze each partner's past cooperation history and select the optimal cooperation method. For example, the cooperation unit selects the optimal cooperation method based on each partner's past cooperation history. The cooperation unit can also analyze each partner's past cooperation history and propose an effective cooperation method. Furthermore, the cooperation unit can improve the cooperation method by referring to each partner's past cooperation history. This enables efficient cooperation by selecting the optimal cooperation method based on the past cooperation history. Some or all of the above-mentioned processing in the cooperation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooperation unit can input information on each partner's past cooperation history into AI, and the AI ​​can automatically select the optimal cooperation method.

[0086] During collaboration, the collaboration unit can customize the means of collaboration based on each partner's current project. For example, the collaboration unit can suggest collaboration means related to each partner's current project. The collaboration unit can also customize the collaboration method based on each partner's current project. Furthermore, the collaboration unit can select the optimal collaboration means according to each partner's current project. This enables more effective collaboration by customizing the means of collaboration based on the current project. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input information about each partner's current project into AI, which can then automatically select the optimal collaboration means.

[0087] The cooperation unit can improve the method of cooperation by reflecting the feedback of each partner during cooperation. For example, the cooperation unit improves the method of cooperation based on the feedback of each partner. The cooperation unit can also adjust the timing of cooperation by reflecting the feedback of each partner. Furthermore, the cooperation unit can customize the content of cooperation by referring to the feedback of each partner. In this way, by reflecting the feedback, the method of cooperation can be optimized and efficient cooperation can be achieved. Some or all of the above-mentioned processing in the cooperation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cooperation unit can input the feedback information of each partner into AI, and the AI ​​can automatically improve the method of cooperation.

[0088] The collaboration unit can estimate the emotions of each partner and determine the priority of collaboration based on the estimated emotions. For example, if each partner is stressed, the collaboration unit can postpone less important collaborations. Furthermore, if each partner is relaxed, the collaboration unit can prioritize more important collaborations. Furthermore, if each partner is busy, the collaboration unit can prioritize only more important collaborations. This allows important collaborations to be efficiently performed by determining the priority of collaborations according to each partner's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit can be performed using, for example, AI, or without AI. For example, the collaboration unit can input emotion data of each partner into the generation AI, which can automatically estimate the emotions and determine the priority of collaborations.

[0089] During collaboration, the collaboration unit can select the optimal collaboration method by taking into account the geographical location information of each partner. For example, the collaboration unit prioritizes collaboration with nearby partners based on the geographical location information of each partner. The collaboration unit can also propose a collaboration method specific to a region based on the geographical location information of each partner. Furthermore, the collaboration unit can select a collaboration method that corresponds to regional market trends based on the geographical location information of each partner. This allows for the selection of the optimal collaboration method by taking geographical location information into consideration, enabling efficient collaboration. Some or all of the above-described processing in the collaboration unit may be performed using, or without, AI. For example, the collaboration unit can input the geographical location information of each partner into AI, which can then automatically select the optimal collaboration method.

[0090] During collaboration, the collaboration unit can analyze the social media activities of each partner and suggest means of collaboration. For example, the collaboration unit can analyze the content of each partner's social media posts and suggest relevant means of collaboration. The collaboration unit can also suggest means of collaboration based on the activities of each partner's friends and followers on social media. Furthermore, the collaboration unit can suggest relevant means of collaboration based on each partner's social media check-in information. In this way, by analyzing social media activities, highly relevant means of collaboration can be suggested. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input information on each partner's social media activities into AI, which can then automatically suggest relevant means of collaboration.

[0091] During collaboration, the collaboration unit can customize the collaboration method by reflecting each partner's past feedback. For example, the collaboration unit can improve the collaboration method based on each partner's past feedback. The collaboration unit can also adjust the timing of collaboration by reflecting each partner's past feedback. Furthermore, the collaboration unit can customize the content of collaboration by referring to each partner's past feedback. In this way, by reflecting past feedback, the collaboration method can be optimized and efficient collaboration can be achieved. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input information on each partner's past feedback into AI, and the AI ​​can automatically customize the collaboration method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and collaboration unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect information about each partner using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using generative AI to perform optimal matching. The collaboration unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides specific support for matched partners to efficiently collaborate with each other. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and collaboration unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect information about each partner using the camera 42 or the microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generative AI to perform optimal matching. The collaboration unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides specific support for matched partners to efficiently collaborate with each other. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and collaboration unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect information about each partner using the camera 42 or microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generative AI to perform optimal matching. The collaboration unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides specific support for matched partners to collaborate efficiently. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and collaboration unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect information about each partner using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using generative AI to perform optimal matching. The collaboration unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides specific support for matched partners to efficiently collaborate with each other.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the emotions of each partner and determine the analysis priority based on the estimated emotions. For example, if each partner is stressed, less important analyses can be postponed. Furthermore, if each partner is relaxed, more important analyses can be prioritized. Furthermore, if each partner is busy, only more important analyses can be prioritized. This allows for efficient analysis by determining the analysis priority according to each partner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input each partner's emotion data into the generation AI, which can then automatically estimate the emotions and determine the analysis priority.

[0094] The collection unit can estimate the emotions of each partner and prioritize the information to be collected based on the estimated emotions. For example, if each partner is feeling stressed, less important information can be postponed. Also, if each partner is relaxed, more important information can be collected first. Furthermore, if each partner is busy, only more important information can be collected. This allows important information to be collected efficiently by prioritizing information according to each partner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input each partner's emotion data into a generation AI, which can automatically estimate the emotions and prioritize the information.

[0095] The collaboration unit can estimate the emotions of each partner and adjust the collaboration method based on the estimated emotions. For example, if each partner is stressed, it can suggest a simple and efficient collaboration method. Also, if each partner is relaxed, it can suggest a detailed collaboration method. Furthermore, if each partner is busy, it can suggest a concise and quick collaboration method. This enables more effective collaboration by adjusting the collaboration method according to each partner's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collaboration unit can be performed using, for example, AI, or without AI. For example, the collaboration unit can input each partner's emotion data into the generation AI, which can automatically estimate the emotions and adjust the collaboration method.

[0096] The analysis unit can estimate each partner's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if each partner is stressed, a simple, highly visible analysis result can be provided. If each partner is relaxed, a detailed analysis result can be provided. Furthermore, if each partner is busy, a concise analysis result that focuses on the main points can be provided. By adjusting the way the analysis is presented based on each partner's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input each partner's emotion data into the generation AI, which can then automatically estimate the emotions and adjust the way the analysis is presented.

[0097] The collection unit can estimate the emotions of each partner and adjust the timing of information collection based on the estimated emotions. For example, if each partner is feeling stressed, the collection unit can delay the timing of information collection and have them provide information in a relaxed state. Furthermore, if each partner is relaxed, the collection unit can also accelerate the timing of information collection to efficiently collect information. Furthermore, if each partner is busy, the collection unit can adjust the timing of information collection and have them provide information in between work. This allows for more appropriate information collection by adjusting the timing of information collection according to each partner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input each partner's emotion data into a generation AI, which can automatically estimate the emotions and adjust the timing of information collection.

[0098] The collection unit can analyze the past cooperation history of each partner and select the optimal information collection method. For example, it analyzes the information collection methods (surveys, interviews, etc.) used by each partner in the past and selects the optimal method. The collection unit can also select the most effective information collection method from the past cooperation history of each partner. Furthermore, the collection unit can adjust the frequency and timing of information collection based on the past cooperation history of each partner. This enables efficient information collection by selecting the optimal information collection method based on the past cooperation history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past cooperation history of each partner into AI, and the AI ​​can automatically select the optimal information collection method.

[0099] When collecting information, the collection unit can filter the information based on each partner's current project or area of ​​interest. For example, the collection unit can collect only information related to each partner's current project. The collection unit can also prioritize collecting relevant information based on each partner's area of ​​interest. Furthermore, the collection unit can filter unnecessary information based on each partner's current project or area of ​​interest. This allows for efficient collection of highly relevant information by filtering information based on the current project or area of ​​interest. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input information on each partner's current project or area of ​​interest into AI, and the AI ​​can automatically perform filtering.

[0100] When collecting information, the collection unit can select an appropriate collection means according to each partner's input method. For example, if each partner prefers voice input, the collection unit can collect information by voice. Also, if each partner prefers text input, the collection unit can collect information by text. Furthermore, if each partner prefers image input, the collection unit can collect information by image. This enables efficient information collection by selecting the optimal collection means according to each partner's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information about each partner's input method into AI, and the AI ​​can automatically select the optimal collection means.

[0101] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each partner. For example, the collection unit can prioritize collecting information about nearby business partners based on the geographical location information of each partner. The collection unit can also collect information about business opportunities specific to a region based on the geographical location information of each partner. Furthermore, the collection unit can prioritize collecting information about regional market trends based on the geographical location information of each partner. This allows for efficient collection of highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of each partner into AI, and the AI ​​can automatically prioritize collecting highly relevant information.

[0102] When collecting information, the collection unit can analyze the social media activities of each partner and collect highly relevant information. For example, it can analyze the content of each partner's social media posts to collect related business information. The collection unit can also collect related information by referring to the activities of each partner's friends and followers on social media. Furthermore, the collection unit can also collect related business information based on each partner's check-in information on social media. In this way, highly relevant information can be collected efficiently by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information on each partner's social media activities into AI, which can then automatically collect highly relevant information.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection department collects information about each partner. This information includes information about each partner's business operations, strengths, needs, etc. The collection department collects information by obtaining information from databases, conducting questionnaire surveys, automatically collecting information using AI, and collecting information from the Internet using web scraping technology. Step 2: The analysis unit uses generation AI to analyze the information collected by the collection unit and perform optimal matching. Analysis is performed using methods such as data mining and statistical analysis. The generation AI analyzes the information on each partner and performs optimal matching using similarity measures and specific algorithms. For example, it matches the services provided by a solution partner with the sales channel of a sales partner, and by adding the technical capabilities of an innovative partner, it performs matching that creates new business opportunities. Step 3: The Collaboration Department provides specific support to help the matched partners collaborate efficiently. The Collaboration Department provides specific support such as technical and marketing support. For example, the Sales Partner sells the services offered by the Solution Partner, and the Innovative Partner provides technical support, thereby promoting business success.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

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

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0139] 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.

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

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information on each partner; an analysis unit that analyzes the information collected by the collection unit and performs appropriate matching; a cooperation unit for enabling the partners matched by the analysis unit to cooperate efficiently with each other. A system characterized by:

2. The collecting unit Gather information about each partner's business, strengths, needs, and other information The system of claim 1 .

3. The analysis unit Analyze the collected information and make the best match The system of claim 1 .

4. The Cooperation Department: Provide specific support to help matched partners collaborate effectively The system of claim 1 .

5. The analysis unit The services provided by solution partners are matched with the sales channels of sales partners, and by adding the technological capabilities of innovative partners, new business opportunities are created. The system of claim 1 .

6. The Cooperation Department: Sales partners sell services offered by solution partners, and innovative partners provide technical support to drive business success. The system of claim 1 .

7. The collecting unit Estimate each partner's emotions and adjust the timing of information gathering based on the estimated emotions. The system of claim 1 .

8. The collecting unit Analyze each partner's past cooperation history and select the most appropriate information gathering method The system of claim 1 .

Images