system

Abstract

The system according to the embodiment aims at automatic generation of a new customer list and efficient management of company information.SOLUTION: A system according to an embodiment includes a company information acquisition unit, a list generation unit, a scoring unit, an analysis unit, an information management unit, and a chat response unit. The company information acquisition unit collects basic information of a company using a company information API. The list generation part generates a new customer list by using a generation AI on the basis of the company information collected by the company information acquiring part. The scoring unit scores the goodness of fit of each company in the new customer list generated by the list generation unit. The analysis unit performs a company analysis based on the company information scored by the scoring unit. The information management unit manages freshness of the company information analyzed by the analysis unit. The chat response unit provides a Web chat function based on the company information managed by the information management unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating new customer lists and managing corporate information was often done manually, which was inefficient.

[0005] The system according to the embodiment aims to automatically generate a new customer list and efficiently manage company information. [Means for solving the problem]

[0006] The system according to the embodiment includes a company information acquisition unit, a list generation unit, a scoring unit, an analysis unit, an information management unit, and a chat response unit. The company information acquisition unit collects basic company information using a company information API. The list generation unit generates a new customer list using a generation AI based on the company information collected by the company information acquisition unit. The scoring unit scores the suitability of each company in the new customer list generated by the list generation unit. The analysis unit performs company analysis based on the company information scored by the scoring unit. The information management unit manages the freshness of the company information analyzed by the analysis unit. The chat response unit provides a web chat function based on the company information managed by the information management unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a new customer list and efficiently manage company information. [Brief explanation of the drawings]

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

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

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

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

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

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The automatic sales list creation system according to an embodiment of the present invention utilizes a company information API and generation AI to automatically create new customer lists, and provides compatibility scoring, automated company analysis, information freshness management, and web chat functions. As a result, the automatic sales list creation system can reduce the man-hours of sales representatives and improve sales efficiency through the creation of highly accurate lists.

[0029] The automatic sales list creation system according to the embodiment includes a company information acquisition unit, a list generation unit, a scoring unit, an analysis unit, an information management unit, and a chat response unit. The company information acquisition unit collects basic company information using a company information API. For example, the company information includes company name, industry, location, and number of employees. The list generation unit generates a new customer list using a generation AI based on the collected company information. For example, the generation AI generates the new customer list based on prompts entered by a sales representative. The scoring unit scores the suitability of each company in the generated new customer list. For example, the suitability score is calculated based on criteria such as industry, company size, and location. The analysis unit performs company analysis based on the scored company information. For example, the analysis unit analyzes the company's financial status, comparison with competitors, growth forecast, and so on. The information management unit manages the freshness of company information and provides the latest information. For example, the information management unit automatically collects the latest information, such as changes in company address and executive transfers, and updates the list. The chat response unit provides a web chat function to support communication between sales representatives and customers. For example, the chat response unit automatically responds to customer inquiries and provides necessary information. As a result, the automatic sales list creation system according to the embodiment can reduce the man-hours of sales representatives and improve sales efficiency through the creation of highly accurate lists.

[0030] The list generation unit can analyze a company's social media activity and generate a new customer list based on the company's latest trends and interests. For example, the list generation unit uses a generation AI to analyze a company's social media accounts such as Twitter and LinkedIn, and identify the company's interests based on the latest posts and hashtags. For example, companies that post frequently about a specific technology or market can be added to a list. This makes it possible to generate a new customer list based on the company's latest trends and interests.

[0031] The list generation unit can analyze a company's past purchasing history and transaction history, predict future purchasing intent, and generate a new customer list. For example, the list generation unit uses generation AI to analyze a company's past purchasing history and identify purchasing patterns for specific products and services. For example, companies that regularly purchase the same products are added to the list. This makes it possible to predict future purchasing intent based on a company's past purchasing history and transaction history and generate a new customer list.

[0032] The list generation unit can cross-reference company information from different industries and generate a new customer list to discover new market opportunities. For example, the list generation unit uses a generation AI to analyze company information from different industries and add companies with common needs or challenges to a list. For example, it cross-references companies from the IT industry and the medical industry and selects companies with common technology needs. This makes it possible to cross-reference company information from different industries and generate a new customer list to discover new market opportunities.

[0033] The list generation unit analyzes information about a company's environmental and social responsibility, and can add companies that are interested in sustainability to a new customer list. For example, the list generation unit uses generation AI to analyze a company's CSR (corporate social responsibility) report and add companies that are working on sustainability to a list. For example, it selects companies that are engaged in environmental protection activities and social contribution activities. This allows companies that are interested in sustainability to be added to a new customer list.

[0034] The scoring unit can analyze a company's financial data and calculate a suitability score based on financial soundness. For example, the scoring unit uses a generation AI to analyze a company's financial statements and evaluate financial soundness based on profitability and debt ratios. For example, a company with a high profit margin and a low debt ratio will receive a high score. This allows the suitability score to be calculated based on the company's financial soundness.

[0035] The scoring unit can analyze a company's technology implementation status and calculate a score based on technical compatibility. For example, the scoring unit uses a generation AI to analyze a company's technology implementation status and assigns a high score to companies that have implemented the latest technology. For example, it selects companies that have implemented AI and IoT technology. This makes it possible to calculate a compatibility score based on a company's technology implementation status.

[0036] The scoring unit can analyze a company's market share and calculate a relevance score based on its market influence. For example, the scoring unit uses a generation AI to analyze a company's market share data and assign a high score to companies with a large market influence. For example, it selects companies with a large share in a specific market. This makes it possible to calculate a relevance score based on a company's market share.

[0037] The scoring unit can analyze the skill sets of a company's employees and calculate a compatibility score based on skill compatibility. For example, the scoring unit uses a generative AI to analyze the LinkedIn profiles of company employees and evaluate their skill sets. For example, a company with many employees with specific technical skills will be given a high score. This allows a compatibility score to be calculated based on the skill sets of the company's employees.

[0038] The scoring unit analyzes a company's customer satisfaction and can assign a high compatibility score to companies with high customer satisfaction. For example, the scoring unit uses a generation AI to analyze data from a company's customer review site and assign a high score to companies with high customer satisfaction. For example, it selects companies that have received high ratings in reviews on Glassdoor and Yelp. This allows it to assign a high compatibility score to companies with high customer satisfaction.

[0039] The analysis department can analyze a company's patent data and perform company analysis based on the degree of technological innovation. For example, the analysis department uses generative AI to analyze a company's patent database and evaluate the degree of technological innovation based on the number and quality of patents. For example, companies with a large number of patents and technologically innovative patents are given a high rating. This makes it possible to evaluate the degree of technological innovation and perform company analysis based on the company's patent data.

[0040] The analysis unit can analyze a company's supply chain data, evaluate supply risk, and conduct company analysis. For example, the analysis unit uses a generative AI to analyze a company's supply chain data and evaluate the diversity and stability of its suppliers. For example, it can give a high rating to companies with multiple suppliers and stable supply systems. This makes it possible to evaluate supply risk and conduct company analysis based on the company's supply chain data.

[0041] The analysis unit can analyze a company's brand value and perform company analysis based on the strength of the brand. For example, the analysis unit uses a generation AI to analyze a company's brand value assessment report and give a high rating to companies with high brand value. For example, it selects companies that have received high scores in assessments by Interbrand or Brand Finance. This makes it possible to evaluate brand strength based on a company's brand value and perform company analysis.

[0042] The information management unit can analyze a company's real-time news feed and automatically update the latest company information. For example, the information management unit uses a generation AI to analyze a company's real-time news feed, automatically collect the latest company information, and update the list. For example, the list is updated based on a company's new product announcements and performance reports. This makes it possible to automatically update the latest company information based on the company's real-time news feed.

[0043] The information management department can monitor a company's official social media accounts and update information based on the latest posted content. For example, the generation AI monitors a company's official Twitter account and updates the list based on the latest posted content. For example, the list is updated based on new product announcements and event information. This makes it possible to reflect the latest posted content in the information based on the company's official social media accounts.

[0044] The information management department can analyze a company's industry reports and update the information based on the latest industry trends. For example, the generation AI analyzes a company's industry reports and updates the list based on the latest industry trends. For example, the list is updated based on information about the introduction of new technologies and market fluctuations. This makes it possible to reflect the latest trends in the information based on the company's industry reports.

[0045] The information management department can analyze the LinkedIn profiles of company employees and update the list based on the latest information on job title changes. For example, the information management department uses a generation AI to analyze the LinkedIn profiles of company employees and update the list based on the latest information on job title changes. For example, the list is updated based on information on people who have been newly appointed to executive positions. This makes it possible to reflect the latest information on job title changes in the list based on the LinkedIn profiles of company employees.

[0046] The information management unit can monitor a company's customer review site and update the information based on the latest customer feedback. For example, the information management unit uses a generation AI to analyze data from a company's customer review site and update the list based on the latest customer feedback. For example, the information management unit adds companies with recent high-rated reviews to the list. This allows the latest customer feedback to be reflected in the information based on the company's customer review site.

[0047] The chat response unit can analyze a customer's past chat history and generate a customized response according to individual needs. For example, the chat response unit uses a generation AI to analyze a customer's past chat history and generate a customized response according to specific needs or problems. For example, a response is generated based on information about products that have been inquired about in the past. This makes it possible to generate a customized response according to individual needs based on the customer's past chat history.

[0048] The chat response unit can analyze a customer's purchasing history and generate a response that suggests related products and services. For example, the generation AI in the chat response unit analyzes a customer's past purchasing history and generates a response that suggests related products and services. For example, it suggests accessories or additional services related to products purchased in the past. This makes it possible to generate a response that suggests related products and services based on the customer's purchasing history.

[0049] The chat response unit can automatically translate chat responses in different languages, enabling international customer service. For example, the chat response unit uses a generation AI to translate customer chat messages in real time and generate responses in different languages. For example, it can translate a Japanese message into English and respond. This allows chat responses in different languages ​​to be automatically translated, enabling international customer service.

[0050] The chat response unit can analyze customer feedback and continuously improve the quality of chat responses. In the chat response unit, for example, the generation AI analyzes customer chat feedback and evaluates the quality of the response. For example, it identifies areas for improvement in the response based on customer satisfaction scores. This makes it possible to continuously improve the quality of chat responses based on customer feedback.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The list generation unit analyzes the skill sets of a company's employees and can add companies with specific skills to a new customer list. For example, the generation AI analyzes the LinkedIn profiles of a company's employees and adds companies with many employees with specific technical skills to a list. This allows companies with specific skill sets to be added to a new customer list.

[0053] The list generation unit can analyze a company's patent data and generate a new customer list based on the company's level of technological innovation. For example, the generation AI can analyze a company's patent database and evaluate the company's level of technological innovation based on the number and quality of its patents. This allows companies with a high level of technological innovation to be added to the new customer list.

[0054] The list generation unit can analyze a company's market share and generate a new customer list based on its market influence. For example, the generation AI analyzes a company's market share data and adds companies with a large market influence to the list. This allows companies with a large market influence to be added to the new customer list.

[0055] The list generation unit analyzes the customer satisfaction of companies and can add companies with high customer satisfaction to a new customer list. For example, the generation AI analyzes data from a company's customer review site and adds companies with high customer satisfaction to a list. This allows companies with high customer satisfaction to be added to a new customer list.

[0056] The list generation unit can analyze a company's brand value and generate a new customer list based on the strength of the brand. For example, the generation AI can analyze a company's brand value assessment report and add companies with high brand value to the list. This allows companies with high brand value to be added to the new customer list.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The company information acquisition unit uses the company information API to collect basic company information, such as company name, industry, location, and number of employees. Step 2: The list generation unit generates a new customer list using generation AI based on the collected company information. For example, the generation AI generates a new customer list based on prompts entered by a sales representative. Step 3: The scoring unit scores the suitability of each company on the generated new customer list. For example, it calculates the suitability score based on criteria such as industry, company size, and location. Step 4: The analysis department conducts company analysis based on the scored company information, such as analyzing the company's financial situation, comparison with competitors, and growth forecasts. Step 5: The information management department manages the freshness of company information and provides the latest information. For example, it automatically collects the latest information such as changes in company addresses and transfers of executives, and updates the list. Step 6: The chat response section provides a web chat function to support communication between sales representatives and customers. For example, it automatically responds to customer inquiries and provides necessary information.

[0059] (Example 2) The automatic sales list creation system according to an embodiment of the present invention utilizes a company information API and generation AI to automatically create new customer lists, and provides compatibility scoring, automated company analysis, information freshness management, and web chat functions. As a result, the automatic sales list creation system can reduce the man-hours of sales representatives and improve sales efficiency through the creation of highly accurate lists.

[0060] The automatic sales list creation system according to the embodiment includes a company information acquisition unit, a list generation unit, a scoring unit, an analysis unit, an information management unit, and a chat response unit. The company information acquisition unit collects basic company information using a company information API. For example, the company information includes company name, industry, location, and number of employees. The list generation unit generates a new customer list using a generation AI based on the collected company information. For example, the generation AI generates the new customer list based on prompts entered by a sales representative. The scoring unit scores the suitability of each company in the generated new customer list. For example, the suitability score is calculated based on criteria such as industry, company size, and location. The analysis unit performs company analysis based on the scored company information. For example, the analysis unit analyzes the company's financial status, comparison with competitors, growth forecast, and so on. The information management unit manages the freshness of company information and provides the latest information. For example, the information management unit automatically collects the latest information, such as changes in company address and executive transfers, and updates the list. The chat response unit provides a web chat function to support communication between sales representatives and customers. For example, the chat response unit automatically responds to customer inquiries and provides necessary information. As a result, the automatic sales list creation system according to the embodiment can reduce the man-hours of sales representatives and improve sales efficiency through the creation of highly accurate lists.

[0061] The list generation unit can analyze a company's social media activity and generate a new customer list based on the company's latest trends and interests. For example, the list generation unit uses a generation AI to analyze a company's social media accounts such as Twitter and LinkedIn, and identify the company's interests based on the latest posts and hashtags. For example, companies that post frequently about a specific technology or market can be added to a list. This makes it possible to generate a new customer list based on the company's latest trends and interests.

[0062] The list generation unit can analyze a company's past purchasing history and transaction history, predict future purchasing intent, and generate a new customer list. For example, the list generation unit uses generation AI to analyze a company's past purchasing history and identify purchasing patterns for specific products and services. For example, companies that regularly purchase the same products are added to the list. This makes it possible to predict future purchasing intent based on a company's past purchasing history and transaction history and generate a new customer list.

[0063] The list generation unit uses the emotion estimation function to analyze the emotions of company representatives and key decision makers, and can prioritize companies with positive emotions to add to the new customer list. For example, the list generation unit uses generation AI to analyze the social media posts of company representatives and add companies with posts containing a lot of positive emotions to the list. For example, it selects companies that frequently post success stories and positive news. This allows companies with positive emotions to be prioritized for addition to the new customer list.

[0064] The list generation unit can cross-reference company information from different industries and generate a new customer list to discover new market opportunities. For example, the list generation unit uses a generation AI to analyze company information from different industries and add companies with common needs or challenges to a list. For example, it cross-references companies from the IT industry and the medical industry and selects companies with common technology needs. This makes it possible to cross-reference company information from different industries and generate a new customer list to discover new market opportunities.

[0065] The list generation unit analyzes information about a company's environmental and social responsibility, and can add companies that are interested in sustainability to a new customer list. For example, the list generation unit uses generation AI to analyze a company's CSR (corporate social responsibility) report and add companies that are working on sustainability to a list. For example, it selects companies that are engaged in environmental protection activities and social contribution activities. This allows companies that are interested in sustainability to be added to a new customer list.

[0066] The list generation unit can analyze the emotions of company employees and add companies with high employee satisfaction to the new customer list. For example, the list generation unit uses a generation AI to analyze social media posts from company employees and add companies with posts that contain a lot of positive emotions to the list. For example, it selects companies whose employees post about their satisfaction with their company. This allows companies with high employee satisfaction to be added to the new customer list.

[0067] The scoring unit can analyze a company's financial data and calculate a suitability score based on financial soundness. For example, the scoring unit uses a generation AI to analyze a company's financial statements and evaluate financial soundness based on profitability and debt ratios. For example, a company with a high profit margin and a low debt ratio will receive a high score. This allows the suitability score to be calculated based on the company's financial soundness.

[0068] The scoring unit can analyze a company's technology implementation status and calculate a score based on technical compatibility. For example, the scoring unit uses a generation AI to analyze a company's technology implementation status and assigns a high score to companies that have implemented the latest technology. For example, it selects companies that have implemented AI and IoT technology. This makes it possible to calculate a compatibility score based on a company's technology implementation status.

[0069] The scoring unit uses the emotion estimation function to analyze the emotions of company representatives and can assign a high compatibility score to companies with positive emotions. For example, the scoring unit uses a generative AI to analyze the social media posts of company representatives and assign a high score to companies whose posts contain a lot of positive emotions. For example, it selects companies that frequently post success stories and positive news. This allows it to assign a high compatibility score to companies with positive emotions.

[0070] The scoring unit can analyze a company's market share and calculate a relevance score based on its market influence. For example, the scoring unit uses a generation AI to analyze a company's market share data and assign a high score to companies with a large market influence. For example, it selects companies with a large share in a specific market. This makes it possible to calculate a relevance score based on a company's market share.

[0071] The scoring unit can analyze the skill sets of a company's employees and calculate a compatibility score based on skill compatibility. For example, the scoring unit uses a generative AI to analyze the LinkedIn profiles of company employees and evaluate their skill sets. For example, a company with many employees with specific technical skills will be given a high score. This allows a compatibility score to be calculated based on the skill sets of the company's employees.

[0072] The scoring unit analyzes a company's customer satisfaction and can assign a high compatibility score to companies with high customer satisfaction. For example, the scoring unit uses a generation AI to analyze data from a company's customer review site and assign a high score to companies with high customer satisfaction. For example, it selects companies that have received high ratings in reviews on Glassdoor and Yelp. This allows it to assign a high compatibility score to companies with high customer satisfaction.

[0073] The analysis department can analyze a company's patent data and perform company analysis based on the degree of technological innovation. For example, the analysis department uses generative AI to analyze a company's patent database and evaluate the degree of technological innovation based on the number and quality of patents. For example, companies with a large number of patents and technologically innovative patents are given a high rating. This makes it possible to evaluate the degree of technological innovation and perform company analysis based on the company's patent data.

[0074] The analysis unit can analyze a company's supply chain data, evaluate supply risk, and conduct company analysis. For example, the analysis unit uses a generative AI to analyze a company's supply chain data and evaluate the diversity and stability of its suppliers. For example, it can give a high rating to companies with multiple suppliers and stable supply systems. This makes it possible to evaluate supply risk and conduct company analysis based on the company's supply chain data.

[0075] The analysis unit uses the emotion estimation function to analyze the emotions of company employees and can analyze companies based on employee motivation and satisfaction. For example, the analysis unit uses a generation AI to analyze the social media posts of company employees and highly evaluate companies that post a lot of positive emotions. For example, it selects companies whose employees post comments expressing satisfaction with their company. This makes it possible to analyze companies based on employee motivation and satisfaction.

[0076] The analysis unit can analyze a company's brand value and perform company analysis based on the strength of the brand. For example, the analysis unit uses a generation AI to analyze a company's brand value assessment report and give a high rating to companies with high brand value. For example, it selects companies that have received high scores in assessments by Interbrand or Brand Finance. This makes it possible to evaluate brand strength based on a company's brand value and perform company analysis.

[0077] The analysis unit can analyze a company's customer reviews and perform company analysis based on customer sentiment. For example, the analysis unit uses a generation AI to analyze data from a company's customer review site and highly rate companies with reviews that contain a lot of positive sentiment. For example, it selects companies that have received high ratings in reviews on Glassdoor and Yelp. This makes it possible to evaluate customer sentiment based on a company's customer reviews and perform company analysis.

[0078] The information management unit can analyze a company's real-time news feed and automatically update the latest company information. For example, the information management unit uses a generation AI to analyze a company's real-time news feed, automatically collect the latest company information, and update the list. For example, the list is updated based on a company's new product announcements and performance reports. This makes it possible to automatically update the latest company information based on the company's real-time news feed.

[0079] The information management department can monitor a company's official social media accounts and update information based on the latest posted content. For example, the generation AI monitors a company's official Twitter account and updates the list based on the latest posted content. For example, the list is updated based on new product announcements and event information. This makes it possible to reflect the latest posted content in the information based on the company's official social media accounts.

[0080] The information management department can use the emotion estimation function to analyze the latest emotional trends of companies and update the list to keep the information fresh. For example, the information management department uses the generative AI to analyze the company's social media posts and update the list based on the latest emotional trends. For example, companies that post a lot of positive emotions are added to the list. This allows the list to be updated to keep the information fresh based on the latest emotional trends of companies.

[0081] The information management department can analyze a company's industry reports and update the information based on the latest industry trends. For example, the generation AI analyzes a company's industry reports and updates the list based on the latest industry trends. For example, the list is updated based on information about the introduction of new technologies and market fluctuations. This makes it possible to reflect the latest trends in the information based on the company's industry reports.

[0082] The information management department can analyze the LinkedIn profiles of company employees and update the list based on the latest information on job title changes. For example, the information management department uses a generation AI to analyze the LinkedIn profiles of company employees and update the list based on the latest information on job title changes. For example, the list is updated based on information on people who have been newly appointed to executive positions. This makes it possible to reflect the latest information on job title changes in the list based on the LinkedIn profiles of company employees.

[0083] The information management unit can monitor a company's customer review site and update the information based on the latest customer feedback. For example, the information management unit uses a generation AI to analyze data from a company's customer review site and update the list based on the latest customer feedback. For example, the information management unit adds companies with recent high-rated reviews to the list. This allows the latest customer feedback to be reflected in the information based on the company's customer review site.

[0084] The chat response unit can analyze a customer's past chat history and generate a customized response according to individual needs. For example, the chat response unit uses a generation AI to analyze a customer's past chat history and generate a customized response according to specific needs or problems. For example, a response is generated based on information about products that have been inquired about in the past. This makes it possible to generate a customized response according to individual needs based on the customer's past chat history.

[0085] The chat response unit can analyze a customer's purchasing history and generate a response that suggests related products and services. For example, the generation AI in the chat response unit analyzes a customer's past purchasing history and generates a response that suggests related products and services. For example, it suggests accessories or additional services related to products purchased in the past. This makes it possible to generate a response that suggests related products and services based on the customer's purchasing history.

[0086] The chat response unit can use an emotion estimation function to analyze customer emotions in real time and generate appropriate responses according to those emotions. For example, the chat response unit uses a generation AI to analyze customers' emotions during chat in real time and generate responses that express gratitude to customers who have positive emotions. For example, the response may be something like "Thank you for your patronage." This makes it possible to analyze customer emotions in real time and generate appropriate responses according to those emotions.

[0087] The chat response unit can automatically translate chat responses in different languages, enabling international customer service. For example, the chat response unit uses a generation AI to translate customer chat messages in real time and generate responses in different languages. For example, it can translate a Japanese message into English and respond. This allows chat responses in different languages ​​to be automatically translated, enabling international customer service.

[0088] The chat response unit can analyze customer feedback and continuously improve the quality of chat responses. In the chat response unit, for example, the generation AI analyzes customer chat feedback and evaluates the quality of the response. For example, it identifies areas for improvement in the response based on customer satisfaction scores. This makes it possible to continuously improve the quality of chat responses based on customer feedback.

[0089] The chat response unit can use emotion estimation to analyze customer emotions and propose promotions and campaigns based on those emotions. For example, the chat response unit uses a generation AI to analyze the emotions of customers during chats and propose special promotions to customers with positive emotions. For example, it may propose something like, "You can take advantage of a special discount right now." This makes it possible to propose promotions and campaigns based on customer emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The list generation unit analyzes the skill sets of a company's employees and can add companies with specific skills to a new customer list. For example, the generation AI analyzes the LinkedIn profiles of a company's employees and adds companies with many employees with specific technical skills to a list. This allows companies with specific skill sets to be added to a new customer list.

[0092] The list generation unit can analyze a company's patent data and generate a new customer list based on the company's level of technological innovation. For example, the generation AI can analyze a company's patent database and evaluate the company's level of technological innovation based on the number and quality of its patents. This allows companies with a high level of technological innovation to be added to the new customer list.

[0093] The list generation unit can analyze a company's market share and generate a new customer list based on its market influence. For example, the generation AI analyzes a company's market share data and adds companies with a large market influence to the list. This allows companies with a large market influence to be added to the new customer list.

[0094] The list generation unit analyzes the customer satisfaction of companies and can add companies with high customer satisfaction to a new customer list. For example, the generation AI analyzes data from a company's customer review site and adds companies with high customer satisfaction to a list. This allows companies with high customer satisfaction to be added to a new customer list.

[0095] The list generation unit can analyze a company's brand value and generate a new customer list based on the strength of the brand. For example, the generation AI can analyze a company's brand value assessment report and add companies with high brand value to the list. This allows companies with high brand value to be added to the new customer list.

[0096] The list generation unit uses the emotion estimation function to analyze the emotions of company employees and can add companies with high employee satisfaction to the new customer list. For example, the generation AI can analyze the social media posts of company employees and add companies with posts that contain a lot of positive emotions to the list. This allows companies with high employee satisfaction to be added to the new customer list.

[0097] The list generation unit uses the emotion estimation function to analyze the emotions of company representatives and prioritizes adding companies with positive emotions to the new customer list. For example, the generation AI analyzes the social media posts of company representatives and adds companies with posts that contain a lot of positive emotions to the list. This allows companies with positive emotions to be prioritized for addition to the new customer list.

[0098] The list generation unit uses the emotion estimation function to analyze the emotions of a company's customers and can add companies with customers who have positive emotions to a new customer list. For example, the generation AI analyzes data from a company's customer review site and adds companies with reviews that contain a large number of positive emotions to the list. This allows companies with customers who have positive emotions to be added to a new customer list.

[0099] The list generation unit uses the emotion estimation function to analyze the emotions of a company's employees and can generate a new customer list based on employee motivation and satisfaction. For example, the generation AI can analyze the social media posts of company employees and add companies whose posts contain a lot of positive emotions to the list. This makes it possible to generate a new customer list based on employee motivation and satisfaction.

[0100] The list generation unit uses the emotion estimation function to analyze the emotions of a company's customers and can add companies with high customer satisfaction to a new customer list. For example, the generation AI analyzes data from a company's customer review site and adds companies with reviews containing a large number of positive emotions to the list. This allows companies with high customer satisfaction to be added to a new customer list.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The company information acquisition unit uses the company information API to collect basic company information, such as company name, industry, location, and number of employees. Step 2: The list generation unit generates a new customer list using generation AI based on the collected company information. For example, the generation AI generates a new customer list based on prompts entered by a sales representative. Step 3: The scoring unit scores the suitability of each company on the generated new customer list. For example, it calculates the suitability score based on criteria such as industry, company size, and location. Step 4: The analysis department conducts company analysis based on the scored company information, such as analyzing the company's financial situation, comparison with competitors, and growth forecasts. Step 5: The information management department manages the freshness of company information and provides the latest information. For example, it automatically collects the latest information such as changes in company addresses and transfers of executives, and updates the list. Step 6: The chat response section provides a web chat function to support communication between sales representatives and customers. For example, it automatically responds to customer inquiries and provides necessary information.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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 company information acquisition department that uses the company information API to collect basic information about companies; a list generation unit that generates a new customer list using a generation AI based on the company information collected by the company information acquisition unit; a scoring unit that scores the suitability of each company in the new customer list generated by the list generation unit; an analysis unit that performs company analysis based on the company information scored by the scoring unit; an information management unit that manages the freshness of the company information analyzed by the analysis unit; a chat response unit that provides a web chat function based on the company information managed by the information management unit. A system characterized by:

2. The list generation unit Using a sentiment estimation function, the sentiment of the company's representatives and key decision makers is analyzed, and companies with positive sentiment are preferentially added to the new customer list.

2. The system of claim 1.

3. The list generation unit Cross-reference company information across different industries to generate new customer lists to discover new market opportunities 2. The system of claim 1.

4. The scoring unit Analyze the financial data of the company and calculate a suitability score based on its financial soundness 2. The system of claim 1.

5. The analysis unit Analyze the patent data of the company and analyze the company based on the degree of technological innovation.

2. The system of claim 1.

6. The information management unit Analyze the company's real-time news feed and automatically update the latest company information 2. The system of claim 1.

7. The chat response unit Analyze past chat history of customers to generate customized responses based on their individual needs 2. The system of claim 1.

8. The chat response unit Analyze customer emotions in real time using emotion estimation and generate appropriate responses based on those emotions.

2. The system of claim 1.

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