System

Abstract

An object of a system according to an embodiment is to appropriately evaluate a usage status of a business messaging platform of a small-to-medium-sized company and provide the evaluation result to a financial institution or the like.SOLUTION: A system according to an embodiment includes a data collection unit, an analysis unit, a scoring model construction unit, and an evaluation provision unit. The data collector collects usage data of the business messaging platform. The analysis unit analyzes the data collected by the data collection unit. The scoring model construction unit constructs a unique scoring model based on the data analyzed by the analysis unit. The evaluation providing unit provides an evaluation result to a financial institution or the like based on the scoring model constructed by the scoring model construction 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] Conventional technologies have had the problem of not adequately evaluating the usage of business messaging platforms by small and medium-sized enterprises and providing the results to financial institutions and other organizations.

[0005] The system according to the embodiment aims to appropriately evaluate the usage of business messaging platforms by small and medium-sized enterprises and provide the evaluation results to financial institutions and the like. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a scoring model construction unit, and an evaluation provision unit. The data collection unit collects usage data of the business messaging platform. The analysis unit analyzes the data collected by the data collection unit. The scoring model construction unit constructs a unique scoring model based on the data analyzed by the analysis unit. The evaluation provision unit provides evaluation results to financial institutions and the like based on the scoring model constructed by the scoring model construction unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately evaluate the usage of business messaging platforms by small and medium-sized enterprises and provide the evaluation results to financial institutions and the like. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The scoring system according to an embodiment of the present invention uses a generation AI to analyze the usage status of business messaging platforms used by small and medium-sized enterprises, and builds a unique scoring model based on the results. This allows the scoring system to evaluate the business performance of small and medium-sized enterprises and provide the evaluation results to financial institutions, etc.

[0029] The scoring system according to the embodiment includes a data collection unit, an analysis unit, a scoring model construction unit, and a rating provision unit. The data collection unit collects usage data for a business messaging platform. For example, the data collection unit collects the number of messages sent and received and the frequency of interactions with customers. The data collection unit can also collect campaign implementation status and customer response data. For example, the data collection unit records the number of messages sent and received and measures the frequency of interactions with customers. The campaign implementation status is recorded by recording the start date, end date, number of participants, etc. The customer response data is collected by collecting the response rate and click rate for messages. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes data patterns and trends to evaluate business performance. The analysis unit can also evaluate customer engagement levels and relationship quality. For example, the analysis unit analyzes data patterns and assigns higher ratings to companies that actively interact with customers. The customer engagement level is evaluated based on message response rates and response times. The quality of the relationship is evaluated by analyzing the content and tone of the message and evaluating the depth of the relationship with the customer. The scoring model construction unit constructs a unique scoring model based on the data analyzed by the analysis unit. For example, the scoring model construction unit constructs a scoring model using a machine learning algorithm. The scoring model construction unit can also set evaluation indicators and evaluation criteria. For example, the scoring model construction unit uses a machine learning algorithm to generate a score based on the frequency and content of interactions with the customer. The evaluation indicators are set based on the customer engagement level and the quality of the relationship. The evaluation criteria set the score range and evaluation weighting. The evaluation providing unit provides evaluation results to financial institutions, etc. based on the scoring model constructed by the scoring model construction unit. For example, the evaluation providing unit provides the evaluation results in the form of a report. The evaluation providing unit can also update the evaluation results in real time and provide evaluations based on the latest data. For example, the evaluation providing unit provides the evaluation results in the form of a report to financial institutions, providing information for determining the creditworthiness of a company and whether or not to grant a loan.The evaluation results include the score range and evaluation details. Real-time updates are performed by continuously collecting and analyzing data to provide the latest evaluation results. This allows the scoring system according to the embodiment to evaluate the business performance of SMEs and provide the evaluation results to financial institutions, etc. For example, financial institutions can evaluate the creditworthiness of SMEs based on the evaluation results and provide appropriate loans. In addition, the service provider (SB) can earn income by receiving a fee for providing the evaluation results.

[0030] The data collection unit can analyze customer response times and response patterns to evaluate customer engagement levels. For example, the data collection unit uses generation AI to analyze the time it takes for customers to respond to messages and evaluate the engagement level. For example, a company with many quick responses may determine that its customers are highly interested. The data collection unit also analyzes customer response patterns to evaluate the engagement level. For example, a company with many customers who open messages regularly may determine that it has high engagement. The data collection unit also uses generation AI to comprehensively analyze customer response times and response patterns to evaluate the engagement level. For example, a company with many customers who respond quickly and frequently may determine that it has very high engagement. In this way, by evaluating customer engagement levels, the depth of a company's customer relationships can be understood.

[0031] The data collection unit can simultaneously collect usage information from social media platforms other than the business messaging platform and perform a comprehensive evaluation. The data collection unit, for example, uses a generation AI to collect usage information from social media platforms other than LINE (registered trademark) for Business and perform a comprehensive evaluation. For example, it analyzes posting frequency and engagement on Facebook (registered trademark) and Twitter (registered trademark). The data collection unit also integrates data from LINE (registered trademark) for Business and other social media platforms to evaluate the company's overall online presence. For example, it analyzes customer responses on multiple platforms. The data collection unit also uses a generation AI to simultaneously collect data from LINE (registered trademark) for Business and other social media platforms and perform a comprehensive evaluation. For example, it compares customer engagement on each platform. This allows for a comprehensive evaluation of the usage information from multiple social media platforms, enabling a more accurate understanding of the company's online presence.

[0032] The data collection unit also collects traffic data from the company's website or online store, allowing for a comprehensive evaluation of customer behavior patterns. The data collection unit, for example, uses generation AI to collect traffic data from the company's website or online store, and comprehensively evaluates customer behavior patterns. For example, it analyzes the number of visitors and length of stay. The data collection unit also analyzes traffic data from the website or online store, and evaluates customer behavior patterns. For example, it analyzes the frequency of viewing specific pages and purchase history. The data collection unit also uses generation AI to collect traffic data from the company's website or online store, and comprehensively evaluates customer behavior patterns. For example, it analyzes visitor repeat rates and conversion rates. In this way, by collecting traffic data from the website or online store, it is possible to comprehensively evaluate customer behavior patterns.

[0033] The analysis unit can compare past data to analyze trends and evaluate a company's growth potential. For example, using generation AI, the analysis unit compares past data with current data to evaluate a company's growth potential. For example, it analyzes trends in increasing sales and number of customers. The analysis unit also performs trend analysis to evaluate a company's growth potential based on past data. For example, it evaluates trends in increasing customer engagement. The analysis unit also uses generation AI to compare past data with current data and evaluate a company's growth potential. For example, it analyzes the effectiveness of a marketing campaign. In this way, the growth potential of a company can be evaluated by comparing past data and analyzing trends.

[0034] The analysis unit can relatively evaluate a company's performance by comparing it with industry-specific benchmark data. The analysis unit, for example, uses generation AI to compare a company's data with industry-specific benchmark data and relatively evaluate performance. For example, it compares sales and number of customers with other companies in the same industry. The analysis unit also performs a relative evaluation to evaluate a company's performance based on industry-specific benchmark data. For example, it evaluates companies that show high performance compared to the industry average. The analysis unit also uses generation AI to compare a company's data with industry-specific benchmark data and relatively evaluate performance. For example, it analyzes fluctuations in market share within the industry. This makes it possible to relatively evaluate a company's performance by comparing it with industry-specific benchmark data.

[0035] The analysis unit can also analyze a company's financial data and market data to evaluate its overall business performance. The analysis unit, for example, uses generative AI to analyze a company's financial data and market data to evaluate its overall business performance. For example, it analyzes sales and profit margins. The analysis unit also comprehensively evaluates a company's business performance based on the financial data and market data. For example, it evaluates financial soundness and market share. The analysis unit also uses generative AI to analyze a company's financial data and market data to evaluate its overall business performance. For example, it evaluates growth rate and competitiveness. In this way, a company's business performance can be comprehensively evaluated by analyzing financial data and market data.

[0036] The analysis unit can analyze a company's supply chain data and evaluate efficiency or risk. The analysis unit, for example, uses generative AI to analyze the company's supply chain data and evaluate efficiency and risk. For example, it analyzes supply chain delays and inventory management efficiency. The analysis unit also evaluates a company's efficiency and risk based on the supply chain data. For example, it identifies bottlenecks and risk factors in the supply chain. The analysis unit also uses generative AI to analyze a company's supply chain data and evaluate efficiency and risk. For example, it evaluates supply chain flexibility and the effectiveness of risk management. In this way, a company's efficiency and risk can be evaluated by analyzing the supply chain data.

[0037] The evaluation providing unit can update the evaluation results in real time and provide evaluations based on the latest data. The evaluation providing unit, for example, uses a generation AI to update the evaluation results in real time and provide evaluations based on the latest data. For example, by updating data daily, the latest performance of the company is reflected. The evaluation providing unit also builds a system that collects data in real time and immediately updates the evaluation results. For example, customer reactions and sales data are reflected in real time. The evaluation providing unit also uses a generation AI to update the evaluation results in real time and provide evaluations based on the latest data. For example, the effectiveness of a new campaign is immediately reflected in the evaluation. In this way, by updating the evaluation results in real time, evaluations based on the latest data can be provided.

[0038] The evaluation providing unit can collect feedback on the evaluation results and continuously improve the scoring model. The evaluation providing unit, for example, uses a generation AI to collect feedback on the evaluation results and continuously improve the scoring model. For example, it adjusts the model based on feedback from financial institutions. The evaluation providing unit also collects feedback on the evaluation results and builds a system that improves the accuracy of the scoring model. For example, it improves the model by reflecting user opinions. The evaluation providing unit also uses a generation AI to collect feedback on the evaluation results in real time and continuously improve the scoring model. For example, it immediately reflects feedback to improve the accuracy of the evaluation results. In this way, the scoring model can be continuously improved by collecting feedback.

[0039] The evaluation provision unit can share the evaluation results among different financial institutions to promote joint evaluation and lending. The evaluation provision unit, for example, uses generation AI to share the evaluation results among different financial institutions to build a system to promote joint evaluation and lending. For example, multiple financial institutions provide loans based on the same evaluation results. The evaluation provision unit also shares the evaluation results among different financial institutions to promote joint evaluation and lending. For example, risk is distributed through information sharing among financial institutions. The evaluation provision unit also uses generation AI to share the evaluation results among different financial institutions in real time to promote joint evaluation and lending. For example, a system is built to jointly provide loans based on the evaluation results. In this way, by sharing evaluation results, joint evaluation and lending among financial institutions can be promoted.

[0040] The evaluation providing department can also provide the evaluation results to the company itself and provide feedback for self-improvement. The evaluation providing department, for example, uses a generation AI to build a system that provides the evaluation results to the company itself and provides feedback for self-improvement. For example, a company identifies areas for improvement based on its own evaluation results. The evaluation providing department also provides the evaluation results to the company itself and provides feedback for self-improvement. For example, the company improves its operations based on the evaluation results. The evaluation providing department also uses a generation AI to provide the evaluation results to the company itself in real time and provide feedback for self-improvement. For example, the company immediately implements improvement measures based on the evaluation results. In this way, by providing the evaluation results to the company itself, feedback for self-improvement can be provided.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The data collection department can also collect information on the company's employees' use of messaging platforms to evaluate employee communication patterns. For example, the number of messages sent and received between employees and their response times can be analyzed to evaluate the team's level of cooperation and communication efficiency. The data collection department can also analyze the content of employees' messages to evaluate their motivation and stress levels at work. For example, a team with many positive messages can be determined to have high motivation. In this way, by evaluating employees' communication patterns, the quality of a company's internal communication can be understood.

[0043] The Data Collection Department can also collect data from a company's customer support chats to evaluate customer satisfaction. For example, the response time and resolution rate of customer support chats can be analyzed to evaluate customer satisfaction. The content of customer support chats can also be analyzed to evaluate the speed at which customers' problems are resolved and the quality of the response. For example, a company that frequently provides prompt and courteous responses can be judged to have high customer satisfaction. This allows for a more accurate evaluation of customer satisfaction by collecting customer support chat data.

[0044] The data collection department can collect mental health data from a company's employees and evaluate their health status. For example, it can analyze employees' stress levels and fatigue levels to evaluate their health status. It can also identify areas for improvement in the company's working environment based on employee mental health data. For example, if there are many employees with high stress levels, it can determine that the working environment needs to be improved. In this way, by collecting employee mental health data, it is possible to evaluate the quality of a company's working environment.

[0045] The data collection department can collect customer purchase history data to evaluate the effectiveness of a company's marketing campaigns. For example, the effectiveness of a campaign can be evaluated by analyzing sales and the increase in the number of new customers during the campaign period. Customer purchase history data can also be used to evaluate the response of the campaign's target demographic. For example, if a particular product is purchased in large numbers, it can be determined that that product is the key to the campaign's success. In this way, by collecting customer purchase history data, the effectiveness of a marketing campaign can be accurately evaluated.

[0046] The Data Collection Department can collect skill data from a company's employees and perform skill matching. For example, it can analyze employees' skill sets and experience and assign them to appropriate projects and tasks. It can also identify skill gaps and suggest training programs based on employee skill data. For example, if a specific skill is lacking, it can suggest training to improve that skill. In this way, collecting employee skill data makes it possible to perform skill matching and suggest ways to improve skills.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The data collection department collects usage data for the business messaging platform, such as the number of messages sent and received, frequency of interactions with customers, campaign implementation status, and customer response data (reply rate and click rate). Step 2: The analytics department analyzes the data collected by the data collection department, for example, analyzing the data for patterns and trends to assess business performance, customer engagement levels, and relationship quality. Step 3: The scoring model construction unit constructs a unique scoring model based on the data analyzed by the analysis unit. For example, it constructs a scoring model using a machine learning algorithm and sets evaluation indicators and evaluation criteria. Step 4: The evaluation providing unit provides the evaluation results to financial institutions, etc. based on the scoring model constructed by the scoring model construction unit. For example, the evaluation results can be provided in the form of a report and updated in real time.

[0049] (Example 2) The scoring system according to an embodiment of the present invention uses a generation AI to analyze the usage status of business messaging platforms used by small and medium-sized enterprises, and builds a unique scoring model based on the results. This allows the scoring system to evaluate the business performance of small and medium-sized enterprises and provide the evaluation results to financial institutions, etc.

[0050] The scoring system according to the embodiment includes a data collection unit, an analysis unit, a scoring model construction unit, and a rating provision unit. The data collection unit collects usage data for a business messaging platform. For example, the data collection unit collects the number of messages sent and received and the frequency of interactions with customers. The data collection unit can also collect campaign implementation status and customer response data. For example, the data collection unit records the number of messages sent and received and measures the frequency of interactions with customers. The campaign implementation status is recorded by recording the start date, end date, number of participants, etc. The customer response data is collected by collecting the response rate and click rate for messages. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes data patterns and trends to evaluate business performance. The analysis unit can also evaluate customer engagement levels and relationship quality. For example, the analysis unit analyzes data patterns and assigns higher ratings to companies that actively interact with customers. The customer engagement level is evaluated based on message response rates and response times. The quality of the relationship is evaluated by analyzing the content and tone of the message and evaluating the depth of the relationship with the customer. The scoring model construction unit constructs a unique scoring model based on the data analyzed by the analysis unit. For example, the scoring model construction unit constructs a scoring model using a machine learning algorithm. The scoring model construction unit can also set evaluation indicators and evaluation criteria. For example, the scoring model construction unit uses a machine learning algorithm to generate a score based on the frequency and content of interactions with the customer. The evaluation indicators are set based on the customer engagement level and the quality of the relationship. The evaluation criteria set the score range and evaluation weighting. The evaluation providing unit provides evaluation results to financial institutions, etc. based on the scoring model constructed by the scoring model construction unit. For example, the evaluation providing unit provides the evaluation results in the form of a report. The evaluation providing unit can also update the evaluation results in real time and provide evaluations based on the latest data. For example, the evaluation providing unit provides the evaluation results in the form of a report to financial institutions, providing information for determining the creditworthiness of a company and whether or not to grant a loan.The evaluation results include the score range and evaluation details. Real-time updates are performed by continuously collecting and analyzing data to provide the latest evaluation results. This allows the scoring system according to the embodiment to evaluate the business performance of SMEs and provide the evaluation results to financial institutions, etc. For example, financial institutions can evaluate the creditworthiness of SMEs based on the evaluation results and provide appropriate loans. In addition, the service provider (SB) can earn income by receiving a fee for providing the evaluation results.

[0051] The data collection unit can analyze the content and tone of messages to evaluate the quality of relationships with customers. For example, the data collection unit uses a generation AI to analyze the content of LINE (registered trademark) for Business messages and evaluate the tone and emotions of interactions with customers. For example, a company that sends many messages with a positive tone may determine that it has good relationships with customers. The data collection unit also analyzes the content of messages to evaluate the frequency and depth of interactions with customers. For example, a company that sends many detailed questions and feedback may determine that it has deep relationships with customers. The data collection unit also analyzes the tone of messages to evaluate the quality of interactions with customers. For example, a company that sends many messages of gratitude and praise may determine that it has high customer satisfaction. This allows for a more accurate evaluation of a company's business performance by evaluating the quality of relationships with customers.

[0052] The data collection unit can analyze customer response times and response patterns to evaluate customer engagement levels. For example, the data collection unit uses generation AI to analyze the time it takes for customers to respond to messages and evaluate the engagement level. For example, a company with many quick responses may determine that its customers are highly interested. The data collection unit also analyzes customer response patterns to evaluate the engagement level. For example, a company with many customers who open messages regularly may determine that it has high engagement. The data collection unit also uses generation AI to comprehensively analyze customer response times and response patterns to evaluate the engagement level. For example, a company with many customers who respond quickly and frequently may determine that it has very high engagement. In this way, by evaluating customer engagement levels, the depth of a company's customer relationships can be understood.

[0053] The data collection unit can use the emotion estimation function to analyze emotional responses to customers' messages and assign a high score to companies that have many positive responses. The data collection unit, for example, uses the emotion estimation function to analyze emotional responses to customers' messages and assigns a high score to companies that have many positive responses. For example, a company that sends many messages expressing gratitude and joy is given a high rating. The data collection unit also analyzes the emotions contained in customer messages and assigns a high score to companies that have many positive emotions. For example, a company whose customers frequently say "thank you" is given a high rating. The data collection unit also uses the emotion estimation function to analyze emotional responses to customers' messages in real time and assigns a high score to companies that have many positive responses. For example, a company whose customers frequently use smiling emoticons is given a high rating. In this way, by evaluating customers' emotional responses, it is possible to understand a company's customer satisfaction.

[0054] The data collection unit can simultaneously collect usage information from social media platforms other than the business messaging platform and perform a comprehensive evaluation. The data collection unit, for example, uses a generation AI to collect usage information from social media platforms other than LINE (registered trademark) for Business and perform a comprehensive evaluation. For example, it analyzes posting frequency and engagement on Facebook (registered trademark) and Twitter (registered trademark). The data collection unit also integrates data from LINE (registered trademark) for Business and other social media platforms to evaluate the company's overall online presence. For example, it analyzes customer responses on multiple platforms. The data collection unit also uses a generation AI to simultaneously collect data from LINE (registered trademark) for Business and other social media platforms and perform a comprehensive evaluation. For example, it compares customer engagement on each platform. This allows for a comprehensive evaluation of the usage information from multiple social media platforms, enabling a more accurate understanding of the company's online presence.

[0055] The data collection unit also collects traffic data from the company's website or online store, allowing for a comprehensive evaluation of customer behavior patterns. The data collection unit, for example, uses generation AI to collect traffic data from the company's website or online store, and comprehensively evaluates customer behavior patterns. For example, it analyzes the number of visitors and length of stay. The data collection unit also analyzes traffic data from the website or online store, and evaluates customer behavior patterns. For example, it analyzes the frequency of viewing specific pages and purchase history. The data collection unit also uses generation AI to collect traffic data from the company's website or online store, and comprehensively evaluates customer behavior patterns. For example, it analyzes visitor repeat rates and conversion rates. In this way, by collecting traffic data from the website or online store, it is possible to comprehensively evaluate customer behavior patterns.

[0056] The data collection unit can use the emotion estimation function to analyze the emotional responses of company employees to messages and evaluate the motivation or satisfaction of the employees. For example, the data collection unit uses the emotion estimation function to analyze the emotional responses of company employees to messages and evaluate the motivation and satisfaction of the employees. For example, it determines that employees who express a lot of positive emotions have high motivation. The data collection unit also analyzes the emotions contained in the employees' messages and evaluates their motivation and satisfaction. For example, it determines that employees who express a lot of gratitude and joy have high satisfaction. The data collection unit also uses the emotion estimation function to analyze the emotional responses of employees to messages in real time and evaluate their motivation and satisfaction. For example, it determines that employees who frequently use smiling emoticons have high satisfaction. In this way, it is possible to understand the motivation and satisfaction of employees by evaluating their emotional responses.

[0057] The analysis unit can compare past data to analyze trends and evaluate a company's growth potential. For example, using generation AI, the analysis unit compares past data with current data to evaluate a company's growth potential. For example, it analyzes trends in increasing sales and number of customers. The analysis unit also performs trend analysis to evaluate a company's growth potential based on past data. For example, it evaluates trends in increasing customer engagement. The analysis unit also uses generation AI to compare past data with current data and evaluate a company's growth potential. For example, it analyzes the effectiveness of a marketing campaign. In this way, the growth potential of a company can be evaluated by comparing past data and analyzing trends.

[0058] The analysis unit can relatively evaluate a company's performance by comparing it with industry-specific benchmark data. The analysis unit, for example, uses generation AI to compare a company's data with industry-specific benchmark data and relatively evaluate performance. For example, it compares sales and number of customers with other companies in the same industry. The analysis unit also performs a relative evaluation to evaluate a company's performance based on industry-specific benchmark data. For example, it evaluates companies that show high performance compared to the industry average. The analysis unit also uses generation AI to compare a company's data with industry-specific benchmark data and relatively evaluate performance. For example, it analyzes fluctuations in market share within the industry. This makes it possible to relatively evaluate a company's performance by comparing it with industry-specific benchmark data.

[0059] The analysis unit can use the emotion estimation function to analyze customer emotion trends and assign a high score to companies where positive emotions are increasing. The analysis unit, for example, uses the emotion estimation function to analyze customer emotion trends and assign a high score to companies where positive emotions are increasing. For example, it rates companies where customer satisfaction is increasing highly. The analysis unit also analyzes customer emotion trends and assigns a high score to companies where positive emotions are increasing. For example, it rates companies where customers are increasingly expressing joy and gratitude highly. The analysis unit also uses the emotion estimation function to analyze customer emotion trends in real time and assign a high score to companies where positive emotions are increasing. For example, it rates companies where customers are increasingly giving positive feedback highly. In this way, it is possible to evaluate a company's customer satisfaction by analyzing customer emotion trends.

[0060] The analysis unit can also analyze a company's financial data and market data to evaluate its overall business performance. The analysis unit, for example, uses generative AI to analyze a company's financial data and market data to evaluate its overall business performance. For example, it analyzes sales and profit margins. The analysis unit also comprehensively evaluates a company's business performance based on the financial data and market data. For example, it evaluates financial soundness and market share. The analysis unit also uses generative AI to analyze a company's financial data and market data to evaluate its overall business performance. For example, it evaluates growth rate and competitiveness. In this way, a company's business performance can be comprehensively evaluated by analyzing financial data and market data.

[0061] The analysis unit can analyze a company's supply chain data and evaluate efficiency or risk. The analysis unit, for example, uses generative AI to analyze the company's supply chain data and evaluate efficiency and risk. For example, it analyzes supply chain delays and inventory management efficiency. The analysis unit also evaluates a company's efficiency and risk based on the supply chain data. For example, it identifies bottlenecks and risk factors in the supply chain. The analysis unit also uses generative AI to analyze a company's supply chain data and evaluate efficiency and risk. For example, it evaluates supply chain flexibility and the effectiveness of risk management. In this way, a company's efficiency and risk can be evaluated by analyzing the supply chain data.

[0062] The analysis unit can use the emotion estimation function to analyze a company's brand image and reputation, and assign a high score to companies with many positive evaluations. The analysis unit, for example, uses the emotion estimation function to analyze a company's brand image and reputation, and assign a high score to companies with many positive evaluations. For example, it assigns a high rating to companies with many favorable customer comments. The analysis unit also analyzes a company's brand image and reputation, and assigns a high score to companies with many positive evaluations. For example, it assigns a high rating to companies with many positive mentions on social media. The analysis unit also uses the emotion estimation function to analyze a company's brand image and reputation in real time, and assigns a high score to companies with many positive evaluations. For example, it assigns a high rating to companies with many positive customer feedback. In this way, by analyzing brand image and reputation, it is possible to understand a company's market evaluation.

[0063] The evaluation providing unit can update the evaluation results in real time and provide evaluations based on the latest data. The evaluation providing unit, for example, uses a generation AI to update the evaluation results in real time and provide evaluations based on the latest data. For example, by updating data daily, the latest performance of the company is reflected. The evaluation providing unit also builds a system that collects data in real time and immediately updates the evaluation results. For example, customer reactions and sales data are reflected in real time. The evaluation providing unit also uses a generation AI to update the evaluation results in real time and provide evaluations based on the latest data. For example, the effectiveness of a new campaign is immediately reflected in the evaluation. In this way, by updating the evaluation results in real time, evaluations based on the latest data can be provided.

[0064] The evaluation providing unit can collect feedback on the evaluation results and continuously improve the scoring model. The evaluation providing unit, for example, uses a generation AI to collect feedback on the evaluation results and continuously improve the scoring model. For example, it adjusts the model based on feedback from financial institutions. The evaluation providing unit also collects feedback on the evaluation results and builds a system that improves the accuracy of the scoring model. For example, it improves the model by reflecting user opinions. The evaluation providing unit also uses a generation AI to collect feedback on the evaluation results in real time and continuously improve the scoring model. For example, it immediately reflects feedback to improve the accuracy of the evaluation results. In this way, the scoring model can be continuously improved by collecting feedback.

[0065] The evaluation providing unit can use the emotion estimation function to analyze the emotional reactions of financial institution personnel and improve the reliability of the evaluation results. The evaluation providing unit, for example, uses the emotion estimation function to analyze the emotional reactions of financial institution personnel and improve the reliability of the evaluation results. For example, it preferentially provides evaluation results in which the personnel have positive emotions. The evaluation providing unit also analyzes the emotional reactions of financial institution personnel and builds a system to improve the reliability of the evaluation results. For example, it adjusts the evaluation results based on the personnel's emotion score. The evaluation providing unit also uses the emotion estimation function to analyze the emotional reactions of financial institution personnel in real time and improve the reliability of the evaluation results. For example, it promotes proactive lending to companies that the personnel have high ratings for. In this way, analyzing the emotional reactions of financial institution personnel improves the reliability of the evaluation results.

[0066] The evaluation provision unit can share the evaluation results among different financial institutions to promote joint evaluation and lending. The evaluation provision unit, for example, uses generation AI to share the evaluation results among different financial institutions to build a system to promote joint evaluation and lending. For example, multiple financial institutions provide loans based on the same evaluation results. The evaluation provision unit also shares the evaluation results among different financial institutions to promote joint evaluation and lending. For example, risk is distributed through information sharing among financial institutions. The evaluation provision unit also uses generation AI to share the evaluation results among different financial institutions in real time to promote joint evaluation and lending. For example, a system is built to jointly provide loans based on the evaluation results. In this way, by sharing evaluation results, joint evaluation and lending among financial institutions can be promoted.

[0067] The evaluation providing department can also provide the evaluation results to the company itself and provide feedback for self-improvement. The evaluation providing department, for example, uses a generation AI to build a system that provides the evaluation results to the company itself and provides feedback for self-improvement. For example, a company identifies areas for improvement based on its own evaluation results. The evaluation providing department also provides the evaluation results to the company itself and provides feedback for self-improvement. For example, the company improves its operations based on the evaluation results. The evaluation providing department also uses a generation AI to provide the evaluation results to the company itself in real time and provide feedback for self-improvement. For example, the company immediately implements improvement measures based on the evaluation results. In this way, by providing the evaluation results to the company itself, feedback for self-improvement can be provided.

[0068] The evaluation providing unit can use the emotion estimation function to analyze the emotional reaction of a company to the evaluation result and promote positive feedback. The evaluation providing unit, for example, uses the emotion estimation function to analyze the emotional reaction of a company to the evaluation result and promote positive feedback. For example, it analyzes the emotional reaction when a company receives a high evaluation. The evaluation providing unit also analyzes the emotional reaction of a company and builds a system to promote positive feedback. For example, it encourages the company to take positive action in response to the evaluation result. The evaluation providing unit also uses the emotion estimation function to analyze the emotional reaction of a company to the evaluation result in real time and promote positive feedback. For example, it encourages the company to proactively implement improvement measures in response to the evaluation result. In this way, positive feedback can be promoted by analyzing the emotional reaction of a company.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The data collection department can also collect information on the company's employees' use of messaging platforms to evaluate employee communication patterns. For example, the number of messages sent and received between employees and their response times can be analyzed to evaluate the team's level of cooperation and communication efficiency. The data collection department can also analyze the content of employees' messages to evaluate their motivation and stress levels at work. For example, a team with many positive messages can be determined to have high motivation. In this way, by evaluating employees' communication patterns, the quality of a company's internal communication can be understood.

[0071] The Data Collection Department can also collect data from a company's customer support chats to evaluate customer satisfaction. For example, the response time and resolution rate of customer support chats can be analyzed to evaluate customer satisfaction. The content of customer support chats can also be analyzed to evaluate the speed at which customers' problems are resolved and the quality of the response. For example, a company that frequently provides prompt and courteous responses can be judged to have high customer satisfaction. This allows for a more accurate evaluation of customer satisfaction by collecting customer support chat data.

[0072] The data collection department can collect mental health data from a company's employees and evaluate their health status. For example, it can analyze employees' stress levels and fatigue levels to evaluate their health status. It can also identify areas for improvement in the company's working environment based on employee mental health data. For example, if there are many employees with high stress levels, it can determine that the working environment needs to be improved. In this way, by collecting employee mental health data, it is possible to evaluate the quality of a company's working environment.

[0073] The data collection department can collect customer purchase history data to evaluate the effectiveness of a company's marketing campaigns. For example, the effectiveness of a campaign can be evaluated by analyzing sales and the increase in the number of new customers during the campaign period. Customer purchase history data can also be used to evaluate the response of the campaign's target demographic. For example, if a particular product is purchased in large numbers, it can be determined that that product is the key to the campaign's success. In this way, by collecting customer purchase history data, the effectiveness of a marketing campaign can be accurately evaluated.

[0074] The Data Collection Department can collect skill data from a company's employees and perform skill matching. For example, it can analyze employees' skill sets and experience and assign them to appropriate projects and tasks. It can also identify skill gaps and suggest training programs based on employee skill data. For example, if a specific skill is lacking, it can suggest training to improve that skill. In this way, collecting employee skill data makes it possible to perform skill matching and suggest ways to improve skills.

[0075] The analysis unit can estimate a user's emotions and evaluate customer loyalty based on the estimated user emotions. For example, it can estimate emotions from the content of a customer's messages and determine that customers with a high level of positive emotions have high loyalty. It can also analyze customer emotion trends and evaluate changes in loyalty. For example, it can determine that customers with increasing positive emotions have improved loyalty. In this way, by evaluating loyalty based on customer emotions, it is possible to understand the depth of customer relationships.

[0076] The analysis unit can estimate user emotions and evaluate the effectiveness of a marketing campaign based on the estimated user emotions. For example, it can analyze customers' emotional responses to a campaign and determine that a campaign with a large number of positive responses is successful. It can also analyze customer emotional trends and evaluate the effectiveness of a campaign in real time. For example, if positive emotions increase during the campaign period, it can determine that the campaign is effective. This allows for more effective campaigns to be implemented by evaluating the effectiveness of a marketing campaign based on customer emotions.

[0077] The analysis unit can estimate user emotions and identify areas for improvement in products and services based on the estimated user emotions. For example, emotions can be estimated from customer feedback, and areas with a high level of negative emotions can be determined to require improvement. The analysis unit can also analyze customer emotion trends and evaluate fluctuations in emotions toward specific products and services. For example, it can determine that products with increasing negative emotions require improvement. This makes it possible to improve customer satisfaction by identifying areas for improvement in products and services based on customer emotions.

[0078] The analysis unit can estimate the user's emotions and evaluate the quality of customer support based on the estimated user emotions. For example, emotions can be estimated from the content of support chats, and support with a high level of positive emotions can be rated highly. The analysis unit can also analyze customer emotion trends and evaluate fluctuations in support quality. For example, if positive emotions are increasing, it can determine that the quality of support is improving. This allows the quality of customer support to be evaluated based on customer emotions, making it possible to provide better support.

[0079] The analysis unit can estimate user emotions and evaluate a company's brand image based on the estimated user emotions. For example, emotions can be estimated from the content of social media posts, and a company with a high percentage of positive emotions can be determined to have a good brand image. It can also analyze customer emotion trends and evaluate changes in brand image. For example, a company with an increasing number of positive emotions can be determined to have an improving brand image. In this way, by evaluating a brand image based on customer emotions, it is possible to understand a company's market evaluation.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The data collection department collects usage data for the business messaging platform, such as the number of messages sent and received, frequency of interactions with customers, campaign implementation status, and customer response data (reply rate and click rate). Step 2: The analytics department analyzes the data collected by the data collection department, for example, analyzing the data for patterns and trends to assess business performance, customer engagement levels, and relationship quality. Step 3: The scoring model construction unit constructs a unique scoring model based on the data analyzed by the analysis unit. For example, it constructs a scoring model using a machine learning algorithm and sets evaluation indicators and evaluation criteria. Step 4: The evaluation providing unit provides the evaluation results to financial institutions, etc. based on the scoring model constructed by the scoring model construction unit. For example, the evaluation results can be provided in the form of a report and updated in real time.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 data collection unit that collects usage data of the business messaging platform; an analysis unit that analyzes the data collected by the data collection unit; a scoring model construction unit that constructs a unique scoring model based on the data analyzed by the analysis unit; an evaluation providing unit that provides an evaluation result to a financial institution or the like based on the scoring model constructed by the scoring model construction unit. A system characterized by:

2. The data collection unit Analyze message content and tone to assess the quality of customer relationships The system of claim 1 .

3. The data collection unit Analyze customer response times and patterns to assess their engagement level The system of claim 1 .

4. The data collection unit Analyzes customer emotional responses to messages and gives a high score to companies with many positive responses The system of claim 1 .

5. The data collection unit The usage status of the above social media platforms other than business messaging platforms will also be collected at the same time to conduct a comprehensive evaluation. The system of claim 1 .

6. The data collection unit We also collect traffic data from your company's website or online store to comprehensively evaluate customer behavior patterns. The system of claim 1 .

7. The data collection unit Analyzing the emotional response of company employees to messages and assessing the motivation or satisfaction of said employees The system of claim 1 .

8. The analysis unit Compare with past data to analyze trends and assess a company's growth potential The system of claim 1 .

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