system

Abstract

The system according to the embodiment aims to efficiently collect and analyze multiple pieces of information in supplier screening operations and make appropriate decisions. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently collect and analyze multiple pieces of information and make appropriate judgments during supplier screening work.

[0005] The system according to the embodiment aims to efficiently collect and analyze multiple pieces of information in supplier screening operations and make appropriate decisions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and analyze multiple pieces of information in supplier screening operations and make appropriate decisions. [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 supplier screening system according to an embodiment of the present invention is a system that collects, analyzes, and provides information. The supplier screening system uses a generation AI to streamline supplier screening operations. For example, the supplier screening system identifies a supplier. The generation AI collects necessary information through chat and identifies the supplier. Next, the supplier screening system verifies corporate information and licenses. The generation AI collects necessary information through chat and verifies the validity of the corporate information and licenses. Furthermore, the supplier screening system references past screening history. The generation AI searches past screening history through chat and provides relevant information. Finally, the supplier screening system checks the credit status and submits necessary internal applications. The generation AI checks the credit status through chat and automatically generates the necessary application documents. This allows even inexperienced personnel to efficiently perform supplier screening operations with the support of the generation AI. This allows the supplier screening system to streamline supplier screening operations and reduce the burden on personnel. For example, the generation AI provides appropriate instructions through chat, allowing personnel to proceed with their work without hesitation. In addition, the generating AI automatically collects and verifies information, which improves work efficiency.

[0029] A business partner screening system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information. For example, the collection unit acquires information from a database. The collection unit can also collect information using a sensor. The collection unit can also collect information through chat using a generation AI. For example, the collection unit collects information necessary for the generation AI to identify business partners through chat. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit performs statistical analysis. The analysis unit can also analyze the information by applying a machine learning algorithm. The analysis unit can also use the generation AI to confirm corporate information and license validity based on the collected information. For example, the analysis unit analyzes information collected by the generation AI through chat and confirms corporate information and license validity. The provision unit provides the analysis results obtained by the analysis unit. For example, the provision unit displays the analysis results through a user interface. The provision unit can also provide the analysis results through an API. The provision unit can also use the generation AI to check credit status and automatically generate necessary application documents. For example, the providing unit allows the generation AI to check the credit status through chat and automatically generate the necessary application documents. This allows the business partner screening system according to the embodiment to efficiently collect, analyze, and provide information.

[0030] The collection unit can collect information necessary to identify a business partner. Information necessary to identify a business partner includes, but is not limited to, for example, corporate name, address, and contact information. The collection unit, for example, acquires information such as corporate name, address, and contact information from a database. The collection unit can also use the generation AI to collect information necessary to identify a business partner through chat. For example, the collection unit causes the generation AI to collect information such as corporate name, address, and contact information through chat. Furthermore, the collection unit can also use a sensor to collect information necessary to identify a business partner. For example, the collection unit collects address information using a GPS sensor. This allows the information necessary to identify a business partner to be collected efficiently.

[0031] The analysis unit can confirm the validity of corporate information or licenses based on the collected information. Corporate information includes, but is not limited to, corporate name, date of establishment, and representative name. To confirm the validity of a license, information such as license number and expiration date is required. The analysis unit can confirm corporate information based on collected information such as corporate name, date of establishment, and representative name. The analysis unit can also confirm the validity of a license based on collected information such as license number and expiration date. Furthermore, the analysis unit can use the generation AI to confirm the validity of corporate information or licenses based on the collected information. For example, the analysis unit analyzes information collected by the generation AI through chat and confirms the validity of corporate information or licenses. This allows the validity of corporate information or licenses to be confirmed efficiently.

[0032] The analysis unit can search past examination history and provide specific information. Past examination history includes, for example, examination date, examination result, examiner's comments, etc., but is not limited to these examples. Specific information includes, for example, a summary of the examination result, related legal information, etc., but is not limited to these examples. For example, the analysis unit searches past examination history from a database and obtains information such as the examination date, examination result, and examiner's comments. The analysis unit can also use the generation AI to search past examination history and provide specific information. For example, the analysis unit allows the generation AI to search past examination history through chat and provide a summary of the examination result and related legal information. This makes it possible to efficiently search past examination history and provide related information.

[0033] The providing unit can check the credit status and automatically generate the required application documents. The credit status includes, for example, but is not limited to, a credit score and past transaction history. The application documents include, for example, but are not limited to, a contract and an application form. For example, the providing unit can check the credit status from a database and obtain information such as the credit score and past transaction history. The providing unit can also check the credit status and automatically generate the required application documents using a generation AI. For example, the providing unit has the generation AI check the credit status through chat and automatically generate a contract and application form based on information such as the credit score and past transaction history. This makes it possible to efficiently check the credit status and automatically generate the required application documents.

[0034] The collection unit can analyze the trading partner's past transaction history and select an appropriate information collection method. The trading partner's past transaction history includes, but is not limited to, for example, transaction date, transaction content, and transaction amount. The collection unit, for example, selects the most effective information collection method for the generation AI based on the trading partner's past transaction history. In addition, if specific information is missing from the trading partner's transaction history, the collection unit can also have the generation AI prioritize collection of that information. Furthermore, the collection unit can analyze the trading partner's transaction history, and the generation AI can adjust the information collection method based on past trends. This allows the optimal information collection method to be selected based on the trading partner's past transaction history.

[0035] When collecting information, the collection unit can filter the information based on the business partner's industry and size. Examples of the business partner's industry and size include, but are not limited to, manufacturing, retail, number of employees, and sales. The collection unit collects only information relevant to the generation AI based on, for example, the business partner's industry. The collection unit can also adjust the level of detail of the information required by the generation AI depending on the size of the business partner. Furthermore, the collection unit can combine the business partner's industry and size to set an optimal information collection filter for the generation AI. This allows information to be filtered based on the business partner's industry and size.

[0036] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit can have the generation AI collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also have the generation AI collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can have the generation AI collect information using image recognition technology. This allows the optimal collection means to be selected depending on the user's input method.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the business partner. Examples of the geographical location information of the business partner include, but are not limited to, GPS data and address information. For example, the collection unit allows the generation AI to prioritize collecting region-specific information based on the location of the business partner. The collection unit can also allow the generation AI to prioritize collecting information about nearby related companies by taking into account the geographical location information of the business partner. Furthermore, the collection unit can allow the generation AI to prioritize collecting information about the economic situation of the region based on the geographical location information of the business partner. This allows highly relevant information to be prioritized by taking into account the geographical location information of the business partner.

[0038] When collecting information, the collection unit can analyze the social media activities of the business partner and collect relevant information. The social media activities of the business partner include, but are not limited to, for example, the content of posts, the number of followers, and the engagement rate. For example, the collection unit analyzes the content of posts made by the business partner on social media, and the generation AI collects relevant information. The collection unit can also enable the generation AI to prioritize collection of the most recent information based on the frequency of the business partner's social media activity. Furthermore, the collection unit can also enable the generation AI to select influential information by taking into account the number of followers of the business partner on social media. This allows the social media activities of the business partner to be analyzed and relevant information to be collected.

[0039] When collecting information, the collection unit can customize the collection method by reflecting past feedback from business partners. Past feedback from business partners includes, but is not limited to, for example, survey results and review comments. The collection unit can adjust the information collection method of the generation AI based on past feedback from business partners, for example. The collection unit can also change the type of information that the generation AI collects by reflecting past feedback from business partners. Furthermore, the collection unit can also adjust the frequency of information collection by the generation AI by taking into account past feedback from business partners. This allows the collection method to be customized by reflecting past feedback from business partners.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, business impact, urgency, and the like. In the analysis unit, for example, the generation AI can perform a detailed analysis of important information. In addition, the analysis unit can also have the generation AI perform a simplified analysis of information with low importance. Furthermore, the analysis unit can also have the generation AI dynamically adjust the level of detail of the analysis depending on the importance of the information. This allows the level of detail of the analysis to be adjusted based on the importance of the information.

[0041] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of information. Information categories include, but are not limited to, for example, product information, customer information, and transaction information. Analysis algorithms include, but are not limited to, for example, regression analysis and clustering. In the analysis unit, for example, the generation AI applies a specific analysis algorithm to corporate information. In addition, the analysis unit can also apply a different analysis algorithm to license information. Furthermore, the analysis unit can also select the optimal analysis algorithm depending on the category of information. This allows the optimal analysis algorithm to be applied depending on the category of information.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The user's past analysis results include, but are not limited to, past reports and analysis logs. In the analysis unit, for example, the generation AI improves the accuracy of the analysis based on the user's past analysis results. The analysis unit can also cause the generation AI to adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results, and the generation AI can select the optimal analysis method. This improves the accuracy of the analysis by referring to the user's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The time of submission of information includes, but is not limited to, for example, the submission date and the submission deadline. In the analysis unit, for example, the generation AI can prioritize analysis of the most recent information. In addition, the analysis unit can also have the generation AI postpone analysis of information that was submitted earlier. Furthermore, the analysis unit can also have the generation AI dynamically adjust the priority of analysis based on the time of submission of information. This allows the priority of analysis to be determined based on the time of submission of information.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The relevance of the information includes, but is not limited to, related topics, common keywords, and the like. In the analysis unit, for example, the generation AI can prioritize analysis of highly relevant information. In addition, the analysis unit can also postpone analysis of less relevant information by the generation AI. Furthermore, the analysis unit can also dynamically adjust the order of analysis by the generation AI based on the relevance of the information. This allows the order of analysis to be adjusted based on the relevance of the information.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user's level of expertise is high, the analysis unit can cause the generation AI to provide analysis results that use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can cause the generation AI to provide analysis results in simpler terms. Furthermore, the analysis unit can also cause the generation AI to dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise.

[0046] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. The importance of the information includes, but is not limited to, business impact, urgency, and the like. For example, the providing unit allows the generation AI to provide a detailed explanation for important information. The providing unit can also allow the generation AI to provide a simplified explanation for less important information. Furthermore, the providing unit can allow the generation AI to dynamically adjust the level of detail of the information provided based on the importance of the information. This allows the level of detail of the information provided to be adjusted based on the importance of the information.

[0047] The providing unit can apply different providing algorithms depending on the category of information when providing the information. Examples of information categories include, but are not limited to, corporate information, license information, and transaction information. Examples of providing algorithms include, but are not limited to, recommendation algorithms and filtering algorithms. For example, the providing unit allows the generation AI to apply a specific providing algorithm to corporate information. Furthermore, the providing unit can also allow the generation AI to apply a different providing algorithm to license information. Furthermore, the providing unit can also allow the generation AI to select the optimal providing algorithm depending on the category of information. This makes it possible to apply the optimal providing algorithm depending on the category of information.

[0048] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. The user's past provision results include, but are not limited to, past reports, feedback, etc. The providing unit, for example, causes the generation AI to improve the accuracy of the provision based on the user's past provision results. The providing unit can also cause the generation AI to adjust the provision algorithm by referring to the user's past provision results. Furthermore, the providing unit can analyze the user's past provision results, and the generation AI can select the optimal provision method. This improves the accuracy of the provision by referring to the user's past provision results.

[0049] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. The time of submission of information includes, for example, the submission date, the submission deadline, etc., but is not limited to these examples. For example, the providing unit can cause the generation AI to provide the most recent information first. The providing unit can also cause the generation AI to provide information that was submitted earlier later. Furthermore, the providing unit can cause the generation AI to dynamically adjust the priority of provision based on the time of submission of information. This allows the priority of provision to be determined based on the time of submission of information.

[0050] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. The relevance of the information includes, for example, related topics, common keywords, etc., but is not limited to such examples. For example, the providing unit allows the generation AI to provide highly relevant information preferentially. The providing unit can also allow the generation AI to provide less relevant information later. Furthermore, the providing unit can also allow the generation AI to dynamically adjust the order of provision based on the relevance of the information. This makes it possible to adjust the order of provision based on the relevance of the information.

[0051] The providing unit can adjust the use of technical terms provided during provision according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user's level of expertise is high, the providing unit can cause the generation AI to provide information using a lot of technical terms. Furthermore, if the user's level of expertise is low, the providing unit can cause the generation AI to provide information in simpler terms. Furthermore, the providing unit can dynamically adjust the use of technical terms provided by the generation AI according to the user's level of expertise. This allows the use of technical terms provided to be adjusted according to the user's level of expertise.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection unit can collect information necessary to identify a trading partner. For example, the collection unit can analyze the trading partner's past transaction history and select an appropriate information collection method. The trading partner's past transaction history includes, but is not limited to, transaction dates, transaction details, transaction amounts, etc. The collection unit selects the most effective information collection method for the generation AI based on the trading partner's past transaction history. In addition, if specific information is missing from the trading partner's transaction history, the collection unit can also have the generation AI prioritize collection of that information. Furthermore, the collection unit can analyze the trading partner's transaction history, and the generation AI can adjust the information collection method based on past trends. This allows the optimal information collection method to be selected based on the trading partner's past transaction history.

[0054] The analysis unit can search past review history and provide specific information. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, business impact, urgency, etc. The analysis unit allows the generation AI to perform a detailed analysis of important information. The analysis unit can also allow the generation AI to perform a simplified analysis of less important information. Furthermore, the analysis unit can allow the generation AI to dynamically adjust the level of detail of the analysis depending on the importance of the information. This allows the level of detail of the analysis to be adjusted based on the importance of the information.

[0055] When collecting information, the collection unit can filter based on the business partner's industry and size. Examples of business partner's industry and size include, but are not limited to, manufacturing, retail, number of employees, and sales. The collection unit allows the generation AI to collect only relevant information based on the business partner's industry. The collection unit can also adjust the level of detail of information required by the generation AI depending on the size of the business partner. Furthermore, the collection unit can combine the business partner's industry and size to set an optimal information collection filter for the generation AI. This allows information to be filtered based on the business partner's industry and size.

[0056] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the business partner. The geographical location information of the business partner includes, but is not limited to, GPS data, address information, etc. The collection unit allows the generation AI to prioritize collecting region-specific information based on the location of the business partner. The collection unit can also allow the generation AI to prioritize collecting information on nearby related companies by taking into account the geographical location information of the business partner. Furthermore, the collection unit can also allow the generation AI to prioritize collecting information on the economic situation of the region based on the geographical location information of the business partner. This allows highly relevant information to be prioritized by taking into account the geographical location information of the business partner.

[0057] When collecting information, the collection unit can analyze the social media activities of the client and collect relevant information. The client's social media activities include, but are not limited to, the content of posts, the number of followers, and the engagement rate. The collection unit analyzes the content of the client's social media posts, and the generation AI collects relevant information. The collection unit can also enable the generation AI to prioritize collection of the most recent information based on the frequency of the client's social media activity. Furthermore, the collection unit can also enable the generation AI to select influential information by taking into account the number of followers the client has on social media. This allows the social media activities of the client to be analyzed and relevant information to be collected.

[0058] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. The time of submission of information includes, but is not limited to, the submission date, the submission deadline, etc. The providing unit allows the generation AI to provide the most recent information preferentially. The providing unit can also allow the generation AI to provide information that was submitted earlier later. Furthermore, the providing unit can also allow the generation AI to dynamically adjust the priority of provision based on the time of submission of information. This allows the priority of provision to be determined based on the time of submission of information.

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

[0060] Step 1: The collection unit collects information. For example, the collection unit obtains information from a database. The collection unit can also collect information using a sensor. Furthermore, the collection unit can also collect information through chat using the generation AI. For example, the collection unit collects information necessary for the generation AI to identify business partners through chat. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit may, for example, perform statistical analysis. The analysis unit may also analyze the information by applying a machine learning algorithm. Furthermore, the analysis unit may use the generation AI to confirm the validity of corporate information and licenses based on the collected information. For example, the analysis unit analyzes the information collected by the generation AI through chat and confirms the validity of corporate information and licenses. Step 3: The providing unit provides the analysis results obtained by the analysis unit. For example, the providing unit displays the analysis results through a user interface. The providing unit can also provide the analysis results through an API. Furthermore, the providing unit can use the generation AI to check the credit status and automatically generate the necessary application documents. For example, the providing unit has the generation AI check the credit status through chat and automatically generate the necessary application documents.

[0061] (Example 2) A supplier screening system according to an embodiment of the present invention is a system that collects, analyzes, and provides information. The supplier screening system uses a generation AI to streamline supplier screening operations. For example, the supplier screening system identifies a supplier. The generation AI collects necessary information through chat and identifies the supplier. Next, the supplier screening system verifies corporate information and licenses. The generation AI collects necessary information through chat and verifies the validity of the corporate information and licenses. Furthermore, the supplier screening system references past screening history. The generation AI searches past screening history through chat and provides relevant information. Finally, the supplier screening system checks the credit status and submits necessary internal applications. The generation AI checks the credit status through chat and automatically generates the necessary application documents. This allows even inexperienced personnel to efficiently perform supplier screening operations with the support of the generation AI. This allows the supplier screening system to streamline supplier screening operations and reduce the burden on personnel. For example, the generation AI provides appropriate instructions through chat, allowing personnel to proceed with their work without hesitation. In addition, the generating AI automatically collects and verifies information, which improves work efficiency.

[0062] A business partner screening system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information. For example, the collection unit acquires information from a database. The collection unit can also collect information using a sensor. The collection unit can also collect information through chat using a generation AI. For example, the collection unit collects information necessary for the generation AI to identify business partners through chat. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit performs statistical analysis. The analysis unit can also analyze the information by applying a machine learning algorithm. The analysis unit can also use the generation AI to confirm corporate information and license validity based on the collected information. For example, the analysis unit analyzes information collected by the generation AI through chat and confirms corporate information and license validity. The provision unit provides the analysis results obtained by the analysis unit. For example, the provision unit displays the analysis results through a user interface. The provision unit can also provide the analysis results through an API. The provision unit can also use the generation AI to check credit status and automatically generate necessary application documents. For example, the providing unit allows the generation AI to check the credit status through chat and automatically generate the necessary application documents. This allows the business partner screening system according to the embodiment to efficiently collect, analyze, and provide information.

[0063] The collection unit can collect information necessary to identify a business partner. Information necessary to identify a business partner includes, but is not limited to, for example, corporate name, address, and contact information. The collection unit, for example, acquires information such as corporate name, address, and contact information from a database. The collection unit can also use the generation AI to collect information necessary to identify a business partner through chat. For example, the collection unit causes the generation AI to collect information such as corporate name, address, and contact information through chat. Furthermore, the collection unit can also use a sensor to collect information necessary to identify a business partner. For example, the collection unit collects address information using a GPS sensor. This allows the information necessary to identify a business partner to be collected efficiently.

[0064] The analysis unit can confirm the validity of corporate information or licenses based on the collected information. Corporate information includes, but is not limited to, corporate name, date of establishment, and representative name. To confirm the validity of a license, information such as license number and expiration date is required. The analysis unit can confirm corporate information based on collected information such as corporate name, date of establishment, and representative name. The analysis unit can also confirm the validity of a license based on collected information such as license number and expiration date. Furthermore, the analysis unit can use the generation AI to confirm the validity of corporate information or licenses based on the collected information. For example, the analysis unit analyzes information collected by the generation AI through chat and confirms the validity of corporate information or licenses. This allows the validity of corporate information or licenses to be confirmed efficiently.

[0065] The analysis unit can search past examination history and provide specific information. Past examination history includes, for example, examination date, examination result, examiner's comments, etc., but is not limited to these examples. Specific information includes, for example, a summary of the examination result, related legal information, etc., but is not limited to these examples. For example, the analysis unit searches past examination history from a database and obtains information such as the examination date, examination result, and examiner's comments. The analysis unit can also use the generation AI to search past examination history and provide specific information. For example, the analysis unit allows the generation AI to search past examination history through chat and provide a summary of the examination result and related legal information. This makes it possible to efficiently search past examination history and provide related information.

[0066] The providing unit can check the credit status and automatically generate the required application documents. The credit status includes, for example, but is not limited to, a credit score and past transaction history. The application documents include, for example, but are not limited to, a contract and an application form. For example, the providing unit can check the credit status from a database and obtain information such as the credit score and past transaction history. The providing unit can also check the credit status and automatically generate the required application documents using a generation AI. For example, the providing unit has the generation AI check the credit status through chat and automatically generate a contract and application form based on information such as the credit score and past transaction history. This makes it possible to efficiently check the credit status and automatically generate the required application documents.

[0067] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit causes the generation AI to reduce the frequency of information collection, thereby reducing the user's burden. Furthermore, if the user is relaxed, the collection unit can also cause the generation AI to increase the frequency of information collection and collect more detailed information. Furthermore, if the user is in a hurry, the collection unit can also cause the generation AI to quickly collect information and provide necessary information preferentially. This allows the timing of information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0068] The collection unit can analyze the trading partner's past transaction history and select an appropriate information collection method. The trading partner's past transaction history includes, but is not limited to, for example, transaction date, transaction content, and transaction amount. The collection unit, for example, selects the most effective information collection method for the generation AI based on the trading partner's past transaction history. In addition, if specific information is missing from the trading partner's transaction history, the collection unit can also have the generation AI prioritize collection of that information. Furthermore, the collection unit can analyze the trading partner's transaction history, and the generation AI can adjust the information collection method based on past trends. This allows the optimal information collection method to be selected based on the trading partner's past transaction history.

[0069] When collecting information, the collection unit can filter the information based on the business partner's industry and size. Examples of the business partner's industry and size include, but are not limited to, manufacturing, retail, number of employees, and sales. The collection unit collects only information relevant to the generation AI based on, for example, the business partner's industry. The collection unit can also adjust the level of detail of the information required by the generation AI depending on the size of the business partner. Furthermore, the collection unit can combine the business partner's industry and size to set an optimal information collection filter for the generation AI. This allows information to be filtered based on the business partner's industry and size.

[0070] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit can have the generation AI collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also have the generation AI collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can have the generation AI collect information using image recognition technology. This allows the optimal collection means to be selected depending on the user's input method.

[0071] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit causes the generation AI to prioritize collecting important information and postpone other information. The collection unit can also cause the generation AI to prioritize collecting detailed information when the user is relaxed. Furthermore, if the user is in a hurry, the collection unit can also prioritize collecting information that the generation AI can collect quickly. This makes it possible to determine the priority of information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0072] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the business partner. Examples of the geographical location information of the business partner include, but are not limited to, GPS data and address information. For example, the collection unit allows the generation AI to prioritize collecting region-specific information based on the location of the business partner. The collection unit can also allow the generation AI to prioritize collecting information about nearby related companies by taking into account the geographical location information of the business partner. Furthermore, the collection unit can allow the generation AI to prioritize collecting information about the economic situation of the region based on the geographical location information of the business partner. This allows highly relevant information to be prioritized by taking into account the geographical location information of the business partner.

[0073] When collecting information, the collection unit can analyze the social media activities of the business partner and collect relevant information. The social media activities of the business partner include, but are not limited to, for example, the content of posts, the number of followers, and the engagement rate. For example, the collection unit analyzes the content of posts made by the business partner on social media, and the generation AI collects relevant information. The collection unit can also enable the generation AI to prioritize collection of the most recent information based on the frequency of the business partner's social media activity. Furthermore, the collection unit can also enable the generation AI to select influential information by taking into account the number of followers of the business partner on social media. This allows the social media activities of the business partner to be analyzed and relevant information to be collected.

[0074] When collecting information, the collection unit can customize the collection method by reflecting past feedback from business partners. Past feedback from business partners includes, but is not limited to, for example, survey results and review comments. The collection unit can adjust the information collection method of the generation AI based on past feedback from business partners, for example. The collection unit can also change the type of information that the generation AI collects by reflecting past feedback from business partners. Furthermore, the collection unit can also adjust the frequency of information collection by the generation AI by taking into account past feedback from business partners. This allows the collection method to be customized by reflecting past feedback from business partners.

[0075] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the generation AI can also provide an analysis result that focuses on the main points. This allows the way the analysis is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, business impact, urgency, and the like. In the analysis unit, for example, the generation AI can perform a detailed analysis of important information. In addition, the analysis unit can also have the generation AI perform a simplified analysis of information with low importance. Furthermore, the analysis unit can also have the generation AI dynamically adjust the level of detail of the analysis depending on the importance of the information. This allows the level of detail of the analysis to be adjusted based on the importance of the information.

[0077] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of information. Information categories include, but are not limited to, for example, product information, customer information, and transaction information. Analysis algorithms include, but are not limited to, for example, regression analysis and clustering. In the analysis unit, for example, the generation AI applies a specific analysis algorithm to corporate information. In addition, the analysis unit can also apply a different analysis algorithm to license information. Furthermore, the analysis unit can also select the optimal analysis algorithm depending on the category of information. This allows the optimal analysis algorithm to be applied depending on the category of information.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The user's past analysis results include, but are not limited to, past reports and analysis logs. In the analysis unit, for example, the generation AI improves the accuracy of the analysis based on the user's past analysis results. The analysis unit can also cause the generation AI to adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results, and the generation AI can select the optimal analysis method. This improves the accuracy of the analysis by referring to the user's past analysis results.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can cause the generation AI to provide a short, concise analysis result. If the user is relaxed, the analysis unit can also cause the generation AI to provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can cause the generation AI to provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0080] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The time of submission of information includes, but is not limited to, for example, the submission date and the submission deadline. In the analysis unit, for example, the generation AI can prioritize analysis of the most recent information. In addition, the analysis unit can also have the generation AI postpone analysis of information that was submitted earlier. Furthermore, the analysis unit can also have the generation AI dynamically adjust the priority of analysis based on the time of submission of information. This allows the priority of analysis to be determined based on the time of submission of information.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The relevance of the information includes, but is not limited to, related topics, common keywords, and the like. In the analysis unit, for example, the generation AI can prioritize analysis of highly relevant information. In addition, the analysis unit can also postpone analysis of less relevant information by the generation AI. Furthermore, the analysis unit can also dynamically adjust the order of analysis by the generation AI based on the relevance of the information. This allows the order of analysis to be adjusted based on the relevance of the information.

[0082] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user's level of expertise is high, the analysis unit can cause the generation AI to provide analysis results that use a lot of technical terms. Furthermore, if the user's level of expertise is low, the analysis unit can cause the generation AI to provide analysis results in simpler terms. Furthermore, the analysis unit can also cause the generation AI to dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows the use of technical terms in the analysis to be adjusted according to the user's level of expertise.

[0083] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, when the user is nervous, the generating AI can provide a simple, highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the generating AI can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the information to be provided according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generating AI. The generating AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. The importance of the information includes, but is not limited to, business impact, urgency, and the like. For example, the providing unit allows the generation AI to provide a detailed explanation for important information. The providing unit can also allow the generation AI to provide a simplified explanation for less important information. Furthermore, the providing unit can allow the generation AI to dynamically adjust the level of detail of the information provided based on the importance of the information. This allows the level of detail of the information provided to be adjusted based on the importance of the information.

[0085] The providing unit can apply different providing algorithms depending on the category of information when providing the information. Examples of information categories include, but are not limited to, corporate information, license information, and transaction information. Examples of providing algorithms include, but are not limited to, recommendation algorithms and filtering algorithms. For example, the providing unit allows the generation AI to apply a specific providing algorithm to corporate information. Furthermore, the providing unit can also allow the generation AI to apply a different providing algorithm to license information. Furthermore, the providing unit can also allow the generation AI to select the optimal providing algorithm depending on the category of information. This makes it possible to apply the optimal providing algorithm depending on the category of information.

[0086] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. The user's past provision results include, but are not limited to, past reports, feedback, etc. The providing unit, for example, causes the generation AI to improve the accuracy of the provision based on the user's past provision results. The providing unit can also cause the generation AI to adjust the provision algorithm by referring to the user's past provision results. Furthermore, the providing unit can analyze the user's past provision results, and the generation AI can select the optimal provision method. This improves the accuracy of the provision by referring to the user's past provision results.

[0087] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit causes the generation AI to provide important information first and postpone other information. The providing unit can also cause the generation AI to provide detailed information first if the user is relaxed. Furthermore, when the user is in a hurry, the providing unit can also provide information that the generation AI can provide quickly first. This makes it possible to determine the priority of information to be provided according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0088] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. The time of submission of information includes, for example, the submission date, the submission deadline, etc., but is not limited to these examples. For example, the providing unit can cause the generation AI to provide the most recent information first. The providing unit can also cause the generation AI to provide information that was submitted earlier later. Furthermore, the providing unit can cause the generation AI to dynamically adjust the priority of provision based on the time of submission of information. This allows the priority of provision to be determined based on the time of submission of information.

[0089] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. The relevance of the information includes, for example, related topics, common keywords, etc., but is not limited to such examples. For example, the providing unit allows the generation AI to provide highly relevant information preferentially. The providing unit can also allow the generation AI to provide less relevant information later. Furthermore, the providing unit can also allow the generation AI to dynamically adjust the order of provision based on the relevance of the information. This makes it possible to adjust the order of provision based on the relevance of the information.

[0090] The providing unit can adjust the use of technical terms provided during provision according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user's level of expertise is high, the providing unit can cause the generation AI to provide information using a lot of technical terms. Furthermore, if the user's level of expertise is low, the providing unit can cause the generation AI to provide information in simpler terms. Furthermore, the providing unit can dynamically adjust the use of technical terms provided by the generation AI according to the user's level of expertise. This allows the use of technical terms provided to be adjusted according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and provision 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 collects information using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A causes the generation AI to collect the information through chat. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to confirm corporate information and license validity. The provision unit displays the analysis results, for example, via the output device 40 of the smart device 14, and the generation AI confirms the credit status through chat and automatically generates the necessary application documents. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision 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 collects information using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A causes the generation AI to collect the information through chat. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and confirms corporate information and license validity. The provision unit displays the analysis results, for example, through the speaker 240 of the smart glasses 214, and the generation AI confirms the credit status through chat and automatically generates the necessary application documents. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A causes the generation AI to collect the information through chat. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and confirms corporate information and license validity. The provision unit displays the analysis results on the display 343 of the headset terminal 314, for example, and the generation AI confirms the credit status through chat and automatically generates the necessary application documents. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and provision 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 collects information using the camera 42 and microphone 238 of the robot 414, and the control unit 46A causes the generation AI to collect the information through chat. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and confirms corporate information and license validity. The provision unit displays the analysis results, for example, through the speaker 240 of the robot 414, and the generation AI confirms the credit status through chat and automatically generates the necessary application documents.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The collection unit can collect information necessary to identify a trading partner. For example, the collection unit can analyze the trading partner's past transaction history and select an appropriate information collection method. The trading partner's past transaction history includes, but is not limited to, transaction dates, transaction details, transaction amounts, etc. The collection unit selects the most effective information collection method for the generation AI based on the trading partner's past transaction history. In addition, if specific information is missing from the trading partner's transaction history, the collection unit can also have the generation AI prioritize collection of that information. Furthermore, the collection unit can analyze the trading partner's transaction history, and the generation AI can adjust the information collection method based on past trends. This allows the optimal information collection method to be selected based on the trading partner's past transaction history.

[0093] The analysis unit can confirm the validity of corporate information or licenses based on the collected information. For example, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. If the user is nervous, the generation AI can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the generation AI can provide a concise analysis result. This allows the way the analysis is presented to be adjusted according to the user's emotions.

[0094] The analysis unit can search past review history and provide specific information. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The importance of the information includes, but is not limited to, business impact, urgency, etc. The analysis unit allows the generation AI to perform a detailed analysis of important information. The analysis unit can also allow the generation AI to perform a simplified analysis of less important information. Furthermore, the analysis unit can allow the generation AI to dynamically adjust the level of detail of the analysis depending on the importance of the information. This allows the level of detail of the analysis to be adjusted based on the importance of the information.

[0095] The providing unit can check the credit status and automatically generate the necessary application documents. For example, the providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. When the user is nervous, the generating AI can provide a simple, highly visible display method. When the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the generating AI can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the information to be provided according to the user's emotions.

[0096] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit causes the generation AI to reduce the frequency of information collection, thereby reducing the user's burden. Also, if the user is relaxed, the collection unit can cause the generation AI to increase the frequency of information collection and collect more detailed information. Furthermore, if the user is in a hurry, the collection unit can cause the generation AI to quickly collect information and provide the necessary information preferentially. This makes it possible to adjust the timing of information collection according to the user's emotions.

[0097] When collecting information, the collection unit can filter based on the business partner's industry and size. Examples of business partner's industry and size include, but are not limited to, manufacturing, retail, number of employees, and sales. The collection unit allows the generation AI to collect only relevant information based on the business partner's industry. The collection unit can also adjust the level of detail of information required by the generation AI depending on the size of the business partner. Furthermore, the collection unit can combine the business partner's industry and size to set an optimal information collection filter for the generation AI. This allows information to be filtered based on the business partner's industry and size.

[0098] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the business partner. The geographical location information of the business partner includes, but is not limited to, GPS data, address information, etc. The collection unit allows the generation AI to prioritize collecting region-specific information based on the location of the business partner. The collection unit can also allow the generation AI to prioritize collecting information on nearby related companies by taking into account the geographical location information of the business partner. Furthermore, the collection unit can also allow the generation AI to prioritize collecting information on the economic situation of the region based on the geographical location information of the business partner. This allows highly relevant information to be prioritized by taking into account the geographical location information of the business partner.

[0099] When collecting information, the collection unit can analyze the social media activities of the client and collect relevant information. The client's social media activities include, but are not limited to, the content of posts, the number of followers, and the engagement rate. The collection unit analyzes the content of the client's social media posts, and the generation AI collects relevant information. The collection unit can also enable the generation AI to prioritize collection of the most recent information based on the frequency of the client's social media activity. Furthermore, the collection unit can also enable the generation AI to select influential information by taking into account the number of followers the client has on social media. This allows the social media activities of the client to be analyzed and relevant information to be collected.

[0100] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can have the generation AI provide a short, concise analysis result. If the user is relaxed, the analysis unit can have the generation AI provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can have the generation AI provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions.

[0101] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. The time of submission of information includes, but is not limited to, the submission date, the submission deadline, etc. The providing unit allows the generation AI to provide the most recent information preferentially. The providing unit can also allow the generation AI to provide information that was submitted earlier later. Furthermore, the providing unit can also allow the generation AI to dynamically adjust the priority of provision based on the time of submission of information. This allows the priority of provision to be determined based on the time of submission of information.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects information. For example, the collection unit obtains information from a database. The collection unit can also collect information using a sensor. Furthermore, the collection unit can also collect information through chat using the generation AI. For example, the collection unit collects information necessary for the generation AI to identify business partners through chat. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit may, for example, perform statistical analysis. The analysis unit may also analyze the information by applying a machine learning algorithm. Furthermore, the analysis unit may use the generation AI to confirm the validity of corporate information and licenses based on the collected information. For example, the analysis unit analyzes the information collected by the generation AI through chat and confirms the validity of corporate information and licenses. Step 3: The providing unit provides the analysis results obtained by the analysis unit. For example, the providing unit displays the analysis results through a user interface. The providing unit can also provide the analysis results through an API. Furthermore, the providing unit can use the generation AI to check the credit status and automatically generate the necessary application documents. For example, the providing unit has the generation AI check the credit status through chat and automatically generate the necessary application documents.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the 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.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0155] 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.

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

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides the analysis results obtained by the analyzing unit. A system characterized by:

2. The collecting unit Collect information necessary to identify business partners 2. The system of claim 1.

3. The analysis unit Verify the validity of legal entities or licenses based on the collected information 2. The system of claim 1.

4. The analysis unit Search past audit history and provide specific information 2. The system of claim 1.

5. The providing unit Check credit status and automatically generate necessary application documents 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze the past transaction history of business partners and select the appropriate information gathering method 2. The system of claim 1.

8. The collecting unit When collecting information, filter based on the business type and size of the business partner.

2. The system of claim 1.

Images