system

Abstract

The system according to the embodiment aims to efficiently identify and resolve issues that employees are having trouble with or prone to making mistakes about when they have questions or concerns that arise in the course of work. [Solution] The system according to the embodiment includes an accumulation unit, an analysis unit, and a provision unit. The accumulation unit accumulates text data and voice data generated daily. The analysis unit analyzes the data accumulated by the accumulation unit and identifies areas where employees are having trouble, are prone to making mistakes, and are unclear. The provision unit provides solutions to the problems identified by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently identify and resolve issues that employees are having trouble with or are prone to making mistakes in response to questions or inquiries that arise in the course of work.

[0005] The system according to the embodiment aims to efficiently identify and resolve issues that employees are having trouble with or prone to making mistakes about when they have questions or concerns that arise in the course of work. [Means for solving the problem]

[0006] The system according to the embodiment includes an accumulation unit, an analysis unit, and a provision unit. The accumulation unit accumulates text data and voice data generated daily. The analysis unit analyzes the data accumulated by the accumulation unit and identifies areas where employees are having trouble, are prone to making mistakes, and are unclear. The provision unit provides solutions to the problems identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently identify and resolve issues that employees are having trouble with or prone to making mistakes about when they have questions or concerns that arise in the course of work. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses AI to resolve all work-related questions and inquiries. This system collects text and voice data generated daily, analyzes the data using AI, identifies issues employees are struggling with, are prone to make mistakes, and are unsure of, and then provides solutions in a unified manner. For example, the system collects text data from emails and chats, and voice data from telephone and online conference systems. Next, the system uses AI to analyze the collected data and identify issues employees are struggling with, are prone to make mistakes, and are unsure of. Finally, the system presents appropriate solutions to the identified problems. For example, if there are many questions about a specific work procedure, the system provides a guide that clearly explains the procedure. It also provides alerts for points where employees are prone to make mistakes. This allows the system to quickly and efficiently resolve work-related questions and inquiries. For example, employees can quickly resolve their work-related questions and inquiries, improving work efficiency and reducing employee stress. Furthermore, early detection of work-related problems and the ability to take appropriate measures improves overall work quality.

[0029] The business support system according to the embodiment includes an accumulation unit, an analysis unit, and a provision unit. The accumulation unit accumulates text data and voice data generated daily. For example, the accumulation unit collects text data from emails and chats. The accumulation unit can also collect voice data from telephones and online conference systems. The accumulation unit can set the data collection frequency and collection method. For example, the accumulation unit collects data at a fixed time every day. The accumulation unit can also collect data when a specific event occurs. The analysis unit analyzes the data accumulated by the accumulation unit to identify issues that employees are having trouble with, are prone to making mistakes, or are unsure about. For example, the analysis unit uses text mining technology to analyze the collected text data. The analysis unit can also analyze the collected voice data using speech recognition technology. The analysis unit can also use AI to identify issues that employees are having trouble with, are prone to making mistakes, or are unsure about. For example, if there are many questions about a specific work procedure, the analysis unit can identify that the procedure is difficult for employees. The provision unit provides solutions to the problems identified by the analysis unit. For example, if there are many questions about a specific work procedure, the providing unit provides a guide that clearly explains the procedure. The providing unit can also alert employees to points where they are likely to make mistakes. Furthermore, the providing unit can use AI to present appropriate solutions. For example, if there are many questions about a specific work procedure, the providing unit provides the guide using an AI model that generates a guide that clearly explains the procedure. This allows the work support system according to the embodiment to efficiently resolve work-related questions and inquiries.

[0030] The collection unit can collect text data from emails or chats, and audio data from telephones or online conference systems. Examples of text data from emails or chats include, but are not limited to, emails and instant messages. For example, the collection unit automatically collects email content and stores it in a database. The collection unit can also collect instant message content in real time and store it for analysis. Examples of audio data from telephones or online conference systems include, but are not limited to, call recordings and meeting recordings. For example, the collection unit records telephone conversation content and stores it as audio data. The collection unit can also record meeting content from online conference systems and store it as audio data. This makes it possible to collect information from a variety of data sources. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input email content into AI and have the AI ​​collect the text data.

[0031] The analysis unit analyzes the collected data and can identify what employees are struggling with, what they are prone to make mistakes on, and what they do not understand. The analysis of the collected data can be performed using, for example, text mining technology or voice recognition technology, but is not limited to these examples. The analysis unit can analyze the collected text data using, for example, text mining technology. For example, the analysis unit can analyze the frequency of occurrence of specific keywords or phrases to identify what employees are struggling with. The analysis unit can also analyze the collected voice data using voice recognition technology. For example, the analysis unit can convert the voice data into text data and analyze it using text mining technology. Furthermore, the analysis unit can use AI to identify what employees are struggling with, what they are prone to make mistakes on, and what they do not understand. For example, the analysis unit can use an AI model to analyze the collected data and identify what employees are struggling with. This can clearly identify the employee's problems. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0032] When there are many questions about a specific work procedure, the providing unit can provide a guide that explains the procedure in detail. When there are many questions about a specific work procedure, the providing unit can provide a guide that explains the procedure in detail. For example, the providing unit can create a step-by-step procedure manual and provide it to employees. The providing unit can also create a video tutorial and provide it to employees. Furthermore, when there are many questions about a specific work procedure, the providing unit can use AI to generate a guide that clearly explains the procedure. For example, when there are many questions about a specific work procedure, the providing unit can use an AI model to generate a guide that clearly explains the procedure. This allows questions about work procedures to be resolved efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, when there are many questions about a specific work procedure, the providing unit can input the information into AI that generates a guide that clearly explains the procedure, and have the AI ​​generate the guide.

[0033] The providing unit can issue warnings about points where employees are likely to make mistakes. The providing unit issues warnings about points where employees are likely to make mistakes. For example, the providing unit can point out points where employees are likely to make mistakes with regard to a specific operating procedure and issue warnings. The providing unit can also point out points where employees are likely to make mistakes with regard to a specific setting item and issue warnings. Furthermore, the providing unit can use AI to issue warnings about points where employees are likely to make mistakes. For example, the providing unit can use an AI model to identify points where employees are likely to make mistakes and issue warnings. This can reduce employee mistakes. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input points where employees are likely to make mistakes into AI and have the AI ​​issue warnings.

[0034] When collecting data, the accumulation unit can filter the data based on the employee's job title or job content. For example, the accumulation unit may prioritize collecting data from managerial employees because this data is useful for improving the entire business. The accumulation unit may also collect data regarding specific job content only from employees related to that job. Furthermore, the accumulation unit may specially filter and collect data on new employees because this data is useful for improving education and training. This makes it possible to collect data based on job title and job content. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit may input data on employee's job title and job content into AI and have the AI ​​perform the filtering.

[0035] When collecting data, the accumulation unit can select data to be collected based on the employee's past question history. For example, the accumulation unit may preferentially collect data from employees who have asked many questions in the past. The accumulation unit can also intensively collect data from employees who frequently ask specific questions to identify problems. Furthermore, the accumulation unit can analyze the past question history and collect data from employees who exhibit specific patterns. This allows for efficient data collection based on the past question history. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input the employee's past question history into AI and have the AI ​​select the data to be collected.

[0036] When collecting data, the accumulation unit can select an appropriate collection method depending on the employee's input method. For example, when collecting voice data, the accumulation unit converts the voice data into text using voice recognition technology. When collecting text data, the accumulation unit can also analyze the data using natural language processing technology. Furthermore, when collecting image data, the accumulation unit can also analyze the content using image recognition technology. This enables optimal data collection depending on the input method. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input the employee's input data into AI and have the AI ​​select the collection method.

[0037] When collecting data, the aggregation unit can prioritize collecting highly relevant data based on employee geographic location information. For example, the aggregation unit can prioritize collecting data from specific offices or branches to identify regional issues. The aggregation unit can also prioritize collecting data from employees working remotely to identify issues in remote environments. Furthermore, the aggregation unit can prioritize collecting data from overseas bases to identify international issues. This makes it possible to collect highly relevant data based on geographic location information. Some or all of the above-described processing in the aggregation unit may be performed using, or without, AI. For example, the aggregation unit can input employee geographic location information into AI and have the AI ​​collect highly relevant data.

[0038] During data collection, the accumulation unit can collect relevant data based on employees' social media activities. For example, the accumulation unit collects work-related posts shared by employees on social media. The accumulation unit can also collect employee feedback on social media and use the data to improve operations. Furthermore, the accumulation unit can analyze employees' activities on social media and collect relevant data. This makes it possible to collect relevant data based on social media activities. Some or all of the above-described processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input employees' social media activity data into AI and have the AI ​​collect the relevant data.

[0039] When collecting data, the accumulation unit can customize the collection method based on the employee's past feedback. For example, the accumulation unit can prioritize the use of a collection method preferred by the employee based on the past feedback. The accumulation unit can also reflect the feedback and improve the collection method to collect data efficiently. Furthermore, the accumulation unit can customize the collection method based on the employee's feedback to collect data more effectively. This makes it possible to optimize the collection method based on the past feedback. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input the employee's past feedback data into AI and have the AI ​​customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This enables detailed analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data into AI and have the AI ​​adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply a voice recognition algorithm to audio data. Furthermore, the analysis unit can also apply an image recognition algorithm to image data. This enables optimal analysis depending on the type of data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the type of data into AI and have the AI ​​select the analysis algorithm to apply.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on past analysis results of the employee. For example, the analysis unit improves the accuracy of the current analysis based on past analysis results. The analysis unit can also refer to past analysis results to perform analyses on similar problems. Furthermore, the analysis unit can utilize past analysis results to optimize the analysis algorithm. This makes it possible to improve the accuracy of the current analysis based on past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results of the employee into AI and have the AI ​​improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the submission date and time of the data. For example, the analysis unit can prioritize analysis of the most recent data and respond quickly. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the submission date. This allows for efficient data analysis based on the submission date. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission date and time of data into AI and have the AI ​​determine the priority of analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data to achieve efficient problem solving. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0045] During the analysis, the analysis unit can adjust the use of technical terminology in the analysis based on the employee's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terminology to employees with high levels of expertise. The analysis unit can also provide analysis results in easy-to-understand language to employees with low levels of expertise. Furthermore, the analysis unit can dynamically adjust the way in which the analysis results are presented depending on the employee's level of expertise. This makes it possible to provide optimal analysis results according to the employee's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's level of expertise into AI and have the AI ​​execute the use of technical terminology.

[0046] When providing a solution, the providing unit can adjust the level of detail of the solution based on the importance of the problem. For example, the providing unit provides a detailed solution for a problem of high importance. The providing unit can also provide a concise solution for a problem of low importance. Furthermore, the providing unit can dynamically adjust the level of detail of the solution according to the importance of the problem. This makes it possible to provide a detailed solution according to the importance of the problem. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the problem to AI and cause the AI ​​to adjust the level of detail of the solution.

[0047] When providing a solution, the providing unit can apply different solution algorithms depending on the type of problem. For example, the providing unit applies a natural language processing algorithm to a problem related to text data. The providing unit can also apply a voice recognition algorithm to a problem related to audio data. Furthermore, the providing unit can also apply an image recognition algorithm to a problem related to image data. This makes it possible to provide an optimal solution depending on the type of problem. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the type of problem into AI and have the AI ​​select a solution algorithm to apply.

[0048] When providing a solution, the providing unit can improve the accuracy of the solution based on the employee's past solution results. The providing unit, for example, improves the accuracy of the current solution based on the past solution results. The providing unit can also refer to the past solution results to provide solutions to similar problems. Furthermore, the providing unit can also utilize the past solution results to optimize the solution algorithm. This makes it possible to improve the accuracy of the current solution based on the past solution results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the employee's past solution results into AI and have the AI ​​improve the accuracy of the solution.

[0049] When providing a solution, the providing unit can determine the priority of the solution based on the date and time when the problem occurred. For example, the providing unit can quickly provide a solution for the most recent problem. The providing unit can also postpone providing a solution for an older problem. Furthermore, the providing unit can dynamically adjust the priority of the solution based on the date and time when the problem occurred. This makes it possible to efficiently provide a solution based on the date and time when the problem occurred. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the date and time when the problem occurred into AI and have the AI ​​determine the priority of the solution.

[0050] The providing unit can adjust the order of solutions based on the relevance of the problems when providing solutions. For example, the providing unit can provide solutions preferentially to highly relevant problems. The providing unit can also provide solutions to less relevant problems at a later date. Furthermore, the providing unit can dynamically adjust the order of solutions based on the relevance of the problems. This makes it possible to efficiently provide solutions based on the relevance of the problems. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the problems to AI and have the AI ​​adjust the order of solutions.

[0051] When providing a solution, the providing unit can adjust the use of technical terminology in the solution based on the employee's level of expertise. For example, the providing unit can provide a solution using detailed technical terminology to an employee with high technical expertise. The providing unit can also provide a solution in easy-to-understand language to an employee with low technical expertise. Furthermore, the providing unit can dynamically adjust the way the solution is expressed depending on the employee's level of expertise. This makes it possible to provide an optimal solution according to the employee's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the employee's level of expertise into AI and have the AI ​​execute the use of technical terminology.

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

[0053] The accumulation unit can collect employee work performance data and provide information useful for improving performance. For example, the accumulation unit can collect data on employee work speed and accuracy and provide feedback useful for improving performance. The accumulation unit can also suggest training programs to improve employee work efficiency. Furthermore, the accumulation unit can analyze employee performance data and identify individual areas for improvement. This can provide specific actions to improve employee work performance. Some or all of the above-mentioned processing in the accumulation unit can be performed using, for example, AI, or can be performed without using AI. For example, the accumulation unit can input employee performance data into AI and have the AI ​​provide feedback.

[0054] The providing unit can propose an optimal solution based on the employee's past solution history. For example, if there is a history of solving a similar problem in the past, that solution can be reused. The providing unit can also analyze the past solution history and propose the most effective solution. Furthermore, the providing unit can improve the accuracy of the solution based on the past solution history. This makes it possible to provide an optimal solution that makes use of the employee's past experience. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the employee's past solution history into AI and have the AI ​​execute the proposal of an optimal solution.

[0055] The solution provision unit can customize the solution provision method based on the employee's job title and job content. For example, strategic solutions can be provided to managerial employees, while solutions showing specific procedures can be provided to general employees. Solutions related to specific job content can also be provided only to employees related to that job. Furthermore, solutions can be provided to new employees as part of their education and training. This makes it possible to provide optimal solutions according to job titles and job content. Some or all of the above-mentioned processing in the solution provision unit can be performed using, or without, AI, for example. For example, the solution provision unit can input data on employee job titles and job content into AI and have the AI ​​customize the solution provision method.

[0056] The accumulation unit can customize the data collection method based on the employee's past feedback. For example, the accumulation unit can prioritize the use of a collection method preferred by the employee based on the past feedback. The accumulation unit can also reflect the feedback and improve the collection method to collect data efficiently. Furthermore, the accumulation unit can customize the collection method based on the employee's feedback to collect data more effectively. This makes it possible to optimize the collection method based on the past feedback. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input the employee's past feedback data into AI and have the AI ​​customize the collection method.

[0057] The analysis unit can adjust the way in which the analysis results are presented based on the employee's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terms to employees with high levels of expertise. On the other hand, the analysis results can be provided in easy-to-understand language to employees with low levels of expertise. Furthermore, the way in which the analysis results are presented can be dynamically adjusted according to the employee's level of expertise. This makes it possible to provide optimal analysis results according to the employee's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's level of expertise into AI and have the AI ​​adjust the way in which the analysis results are presented.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects text data and audio data generated daily. For example, it collects text data from emails and chats, and audio data from telephone calls and online conference systems. The collection unit can also set the frequency and means of data collection, and can collect data at a fixed time each day or when a specific event occurs. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the areas where employees are having trouble, making mistakes, and not understanding. For example, text data can be analyzed using text mining technology, and voice data can be analyzed using voice recognition technology. Furthermore, AI can be used to identify the areas where employees are having trouble, making mistakes, and not understanding. Step 3: The provision department provides solutions to the problems identified by the analysis department. For example, if there are many questions about a specific work procedure, the provision department can provide a guide that clearly explains the procedure and warn employees about points where they are likely to make mistakes. In addition, appropriate solutions can be presented using AI.

[0060] (Example 2) A system according to an embodiment of the present invention uses AI to resolve all work-related questions and inquiries. This system collects text and voice data generated daily, analyzes the data using AI, identifies issues employees are struggling with, are prone to make mistakes, and are unsure of, and then provides solutions in a unified manner. For example, the system collects text data from emails and chats, and voice data from telephone and online conference systems. Next, the system uses AI to analyze the collected data and identify issues employees are struggling with, are prone to make mistakes, and are unsure of. Finally, the system presents appropriate solutions to the identified problems. For example, if there are many questions about a specific work procedure, the system provides a guide that clearly explains the procedure. It also provides alerts for points where employees are prone to make mistakes. This allows the system to quickly and efficiently resolve work-related questions and inquiries. For example, employees can quickly resolve their work-related questions and inquiries, improving work efficiency and reducing employee stress. Furthermore, early detection of work-related problems and the ability to take appropriate measures improves overall work quality.

[0061] The business support system according to the embodiment includes an accumulation unit, an analysis unit, and a provision unit. The accumulation unit accumulates text data and voice data generated daily. For example, the accumulation unit collects text data from emails and chats. The accumulation unit can also collect voice data from telephones and online conference systems. The accumulation unit can set the data collection frequency and collection method. For example, the accumulation unit collects data at a fixed time every day. The accumulation unit can also collect data when a specific event occurs. The analysis unit analyzes the data accumulated by the accumulation unit to identify issues that employees are having trouble with, are prone to making mistakes, or are unsure about. For example, the analysis unit uses text mining technology to analyze the collected text data. The analysis unit can also analyze the collected voice data using speech recognition technology. The analysis unit can also use AI to identify issues that employees are having trouble with, are prone to making mistakes, or are unsure about. For example, if there are many questions about a specific work procedure, the analysis unit can identify that the procedure is difficult for employees. The provision unit provides solutions to the problems identified by the analysis unit. For example, if there are many questions about a specific work procedure, the providing unit provides a guide that clearly explains the procedure. The providing unit can also alert employees to points where they are likely to make mistakes. Furthermore, the providing unit can use AI to present appropriate solutions. For example, if there are many questions about a specific work procedure, the providing unit provides the guide using an AI model that generates a guide that clearly explains the procedure. This allows the work support system according to the embodiment to efficiently resolve work-related questions and inquiries.

[0062] The collection unit can collect text data from emails or chats, and audio data from telephones or online conference systems. Examples of text data from emails or chats include, but are not limited to, emails and instant messages. For example, the collection unit automatically collects email content and stores it in a database. The collection unit can also collect instant message content in real time and store it for analysis. Examples of audio data from telephones or online conference systems include, but are not limited to, call recordings and meeting recordings. For example, the collection unit records telephone conversation content and stores it as audio data. The collection unit can also record meeting content from online conference systems and store it as audio data. This makes it possible to collect information from a variety of data sources. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input email content into AI and have the AI ​​collect the text data.

[0063] The analysis unit analyzes the collected data and can identify what employees are struggling with, what they are prone to make mistakes on, and what they do not understand. The analysis of the collected data can be performed using, for example, text mining technology or voice recognition technology, but is not limited to these examples. The analysis unit can analyze the collected text data using, for example, text mining technology. For example, the analysis unit can analyze the frequency of occurrence of specific keywords or phrases to identify what employees are struggling with. The analysis unit can also analyze the collected voice data using voice recognition technology. For example, the analysis unit can convert the voice data into text data and analyze it using text mining technology. Furthermore, the analysis unit can use AI to identify what employees are struggling with, what they are prone to make mistakes on, and what they do not understand. For example, the analysis unit can use an AI model to analyze the collected data and identify what employees are struggling with. This can clearly identify the employee's problems. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0064] When there are many questions about a specific work procedure, the providing unit can provide a guide that explains the procedure in detail. When there are many questions about a specific work procedure, the providing unit can provide a guide that explains the procedure in detail. For example, the providing unit can create a step-by-step procedure manual and provide it to employees. The providing unit can also create a video tutorial and provide it to employees. Furthermore, when there are many questions about a specific work procedure, the providing unit can use AI to generate a guide that clearly explains the procedure. For example, when there are many questions about a specific work procedure, the providing unit can use an AI model to generate a guide that clearly explains the procedure. This allows questions about work procedures to be resolved efficiently. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, when there are many questions about a specific work procedure, the providing unit can input the information into AI that generates a guide that clearly explains the procedure, and have the AI ​​generate the guide.

[0065] The providing unit can issue warnings about points where employees are likely to make mistakes. The providing unit issues warnings about points where employees are likely to make mistakes. For example, the providing unit can point out points where employees are likely to make mistakes with regard to a specific operating procedure and issue warnings. The providing unit can also point out points where employees are likely to make mistakes with regard to a specific setting item and issue warnings. Furthermore, the providing unit can use AI to issue warnings about points where employees are likely to make mistakes. For example, the providing unit can use an AI model to identify points where employees are likely to make mistakes and issue warnings. This can reduce employee mistakes. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input points where employees are likely to make mistakes into AI and have the AI ​​issue warnings.

[0066] The accumulation unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the accumulation unit temporarily delays data collection and resumes collection when the user is relaxed. The accumulation unit can also collect data when the user is concentrating, minimizing interruptions to work. Furthermore, if the user is tired, the accumulation unit can refrain from collecting data and resume collection after the user has taken a break. This allows data to be collected at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the accumulation unit may be performed using, for example, an AI, or without an AI. For example, the accumulation unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0067] When collecting data, the accumulation unit can filter the data based on the employee's job title or job content. For example, the accumulation unit may prioritize collecting data from managerial employees because this data is useful for improving the entire business. The accumulation unit may also collect data regarding specific job content only from employees related to that job. Furthermore, the accumulation unit may specially filter and collect data on new employees because this data is useful for improving education and training. This makes it possible to collect data based on job title and job content. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit may input data on employee's job title and job content into AI and have the AI ​​perform the filtering.

[0068] When collecting data, the accumulation unit can select data to be collected based on the employee's past question history. For example, the accumulation unit may preferentially collect data from employees who have asked many questions in the past. The accumulation unit can also intensively collect data from employees who frequently ask specific questions to identify problems. Furthermore, the accumulation unit can analyze the past question history and collect data from employees who exhibit specific patterns. This allows for efficient data collection based on the past question history. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input the employee's past question history into AI and have the AI ​​select the data to be collected.

[0069] When collecting data, the accumulation unit can select an appropriate collection method depending on the employee's input method. For example, when collecting voice data, the accumulation unit converts the voice data into text using voice recognition technology. When collecting text data, the accumulation unit can also analyze the data using natural language processing technology. Furthermore, when collecting image data, the accumulation unit can also analyze the content using image recognition technology. This enables optimal data collection depending on the input method. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input the employee's input data into AI and have the AI ​​select the collection method.

[0070] The accumulation unit can estimate a user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if a user is feeling stressed, the accumulation unit can prioritize collecting that user's data and quickly resolve the problem. Furthermore, if a user is relaxed, the accumulation unit can prioritize collecting other users' data. Furthermore, if a user is in a hurry, the accumulation unit can quickly collect that user's data and provide immediate support. This allows data prioritization to be determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the accumulation unit can be performed using, for example, an AI, or without an AI. For example, the accumulation unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the data.

[0071] When collecting data, the aggregation unit can prioritize collecting highly relevant data based on employee geographic location information. For example, the aggregation unit can prioritize collecting data from specific offices or branches to identify regional issues. The aggregation unit can also prioritize collecting data from employees working remotely to identify issues in remote environments. Furthermore, the aggregation unit can prioritize collecting data from overseas bases to identify international issues. This makes it possible to collect highly relevant data based on geographic location information. Some or all of the above-described processing in the aggregation unit may be performed using, or without, AI. For example, the aggregation unit can input employee geographic location information into AI and have the AI ​​collect highly relevant data.

[0072] During data collection, the accumulation unit can collect relevant data based on employees' social media activities. For example, the accumulation unit collects work-related posts shared by employees on social media. The accumulation unit can also collect employee feedback on social media and use the data to improve operations. Furthermore, the accumulation unit can analyze employees' activities on social media and collect relevant data. This makes it possible to collect relevant data based on social media activities. Some or all of the above-described processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input employees' social media activity data into AI and have the AI ​​collect the relevant data.

[0073] When collecting data, the accumulation unit can customize the collection method based on the employee's past feedback. For example, the accumulation unit can prioritize the use of a collection method preferred by the employee based on the past feedback. The accumulation unit can also reflect the feedback and improve the collection method to collect data efficiently. Furthermore, the accumulation unit can customize the collection method based on the employee's feedback to collect data more effectively. This makes it possible to optimize the collection method based on the past feedback. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input the employee's past feedback data into AI and have the AI ​​customize the collection method.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This allows for optimal analysis results to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the presentation method of the analysis.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This enables detailed analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data into AI and have the AI ​​adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the type of data during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply a voice recognition algorithm to audio data. Furthermore, the analysis unit can also apply an image recognition algorithm to image data. This enables optimal analysis depending on the type of data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the type of data into AI and have the AI ​​select the analysis algorithm to apply.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis based on past analysis results of the employee. For example, the analysis unit improves the accuracy of the current analysis based on past analysis results. The analysis unit can also refer to past analysis results to perform analyses on similar problems. Furthermore, the analysis unit can utilize past analysis results to optimize the analysis algorithm. This makes it possible to improve the accuracy of the current analysis based on past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results of the employee into AI and have the AI ​​improve the accuracy of the analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows for providing an analysis result of optimal length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0079] During analysis, the analysis unit can determine the priority of analysis based on the submission date and time of the data. For example, the analysis unit can prioritize analysis of the most recent data and respond quickly. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the submission date. This allows for efficient data analysis based on the submission date. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission date and time of data into AI and have the AI ​​determine the priority of analysis.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data to achieve efficient problem solving. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI and have the AI ​​adjust the order of analysis.

[0081] During the analysis, the analysis unit can adjust the use of technical terminology in the analysis based on the employee's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terminology to employees with high levels of expertise. The analysis unit can also provide analysis results in easy-to-understand language to employees with low levels of expertise. Furthermore, the analysis unit can dynamically adjust the way in which the analysis results are presented depending on the employee's level of expertise. This makes it possible to provide optimal analysis results according to the employee's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's level of expertise into AI and have the AI ​​execute the use of technical terminology.

[0082] The providing unit can estimate the user's emotions and adjust the way the solution is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible solution. Furthermore, if the user is relaxed, the providing unit can also provide a detailed solution. Furthermore, if the user is in a hurry, the providing unit can also provide a concise solution that focuses on the main points. This makes it possible to provide an optimal solution according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​adjust the way the solution is presented.

[0083] When providing a solution, the providing unit can adjust the level of detail of the solution based on the importance of the problem. For example, the providing unit provides a detailed solution for a problem of high importance. The providing unit can also provide a concise solution for a problem of low importance. Furthermore, the providing unit can dynamically adjust the level of detail of the solution according to the importance of the problem. This makes it possible to provide a detailed solution according to the importance of the problem. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the problem to AI and cause the AI ​​to adjust the level of detail of the solution.

[0084] When providing a solution, the providing unit can apply different solution algorithms depending on the type of problem. For example, the providing unit applies a natural language processing algorithm to a problem related to text data. The providing unit can also apply a voice recognition algorithm to a problem related to audio data. Furthermore, the providing unit can also apply an image recognition algorithm to a problem related to image data. This makes it possible to provide an optimal solution depending on the type of problem. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the type of problem into AI and have the AI ​​select a solution algorithm to apply.

[0085] When providing a solution, the providing unit can improve the accuracy of the solution based on the employee's past solution results. The providing unit, for example, improves the accuracy of the current solution based on the past solution results. The providing unit can also refer to the past solution results to provide solutions to similar problems. Furthermore, the providing unit can also utilize the past solution results to optimize the solution algorithm. This makes it possible to improve the accuracy of the current solution based on the past solution results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the employee's past solution results into AI and have the AI ​​improve the accuracy of the solution.

[0086] The providing unit can estimate the user's emotions and adjust the length of the solution based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide a short and concise solution. The providing unit can also provide a detailed solution if the user is relaxed. Furthermore, if the user is excited, the providing unit can provide a solution with a visually stimulating effect. This allows a solution of optimal length to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the solution.

[0087] When providing a solution, the providing unit can determine the priority of the solution based on the date and time when the problem occurred. For example, the providing unit can quickly provide a solution for the most recent problem. The providing unit can also postpone providing a solution for an older problem. Furthermore, the providing unit can dynamically adjust the priority of the solution based on the date and time when the problem occurred. This makes it possible to efficiently provide a solution based on the date and time when the problem occurred. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the date and time when the problem occurred into AI and have the AI ​​determine the priority of the solution.

[0088] The providing unit can adjust the order of solutions based on the relevance of the problems when providing solutions. For example, the providing unit can provide solutions preferentially to highly relevant problems. The providing unit can also provide solutions to less relevant problems at a later date. Furthermore, the providing unit can dynamically adjust the order of solutions based on the relevance of the problems. This makes it possible to efficiently provide solutions based on the relevance of the problems. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the problems to AI and have the AI ​​adjust the order of solutions.

[0089] When providing a solution, the providing unit can adjust the use of technical terminology in the solution based on the employee's level of expertise. For example, the providing unit can provide a solution using detailed technical terminology to an employee with high technical expertise. The providing unit can also provide a solution in easy-to-understand language to an employee with low technical expertise. Furthermore, the providing unit can dynamically adjust the way the solution is expressed depending on the employee's level of expertise. This makes it possible to provide an optimal solution according to the employee's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the employee's level of expertise into AI and have the AI ​​execute the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14 and the processor 28 of the data processing device 12, and collects text data from emails and chats, and audio data from telephone calls and online conference systems. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify areas where employees are having trouble, are prone to making mistakes, or are unclear. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12, and presents appropriate solutions to the identified problems. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and the processor 28 of the data processing device 12, and collects text data from emails and chats, and audio data from telephone calls and online conference systems. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify areas where employees are having trouble, are prone to making mistakes, or are unclear. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, and presents appropriate solutions to the identified problems. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset type terminal 314 and the processor 28 of the data processing device 12, and collects text data from emails and chats, and audio data from telephone calls and online conference systems. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify areas where employees are having trouble, are prone to making mistakes, or are unclear. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12, and presents appropriate solutions to the identified problems. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414 and the processor 28 of the data processing device 12, and collects text data from emails and chats, and audio data from telephone calls and online conference systems. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify areas where employees are having trouble, are prone to making mistakes, or are unsure of information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12, and presents appropriate solutions to the identified problems.

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

[0091] The providing unit can estimate the employee's emotions and adjust the solution provision method based on the estimated emotions. For example, if the employee is stressed, the providing unit can provide a simple, highly visible solution. If the employee is relaxed, the providing unit can provide a detailed solution. Furthermore, if the employee is in a hurry, the providing unit can provide a concise solution that focuses on the main points. This allows the optimal solution to be provided according to the employee's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the employee's emotion data into an AI and have the AI ​​adjust the solution provision method.

[0092] The accumulation unit can collect employee work performance data and provide information useful for improving performance. For example, the accumulation unit can collect data on employee work speed and accuracy and provide feedback useful for improving performance. The accumulation unit can also suggest training programs to improve employee work efficiency. Furthermore, the accumulation unit can analyze employee performance data and identify individual areas for improvement. This can provide specific actions to improve employee work performance. Some or all of the above-mentioned processing in the accumulation unit can be performed using, for example, AI, or can be performed without using AI. For example, the accumulation unit can input employee performance data into AI and have the AI ​​provide feedback.

[0093] The analysis unit can estimate the employee's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the employee is stressed, a simple, highly visible analysis result can be provided. If the employee is relaxed, a detailed analysis result can be provided. If the employee is in a hurry, a concise analysis result that focuses on the main points can be provided. This allows the optimal analysis result to be provided according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input employee emotion data into an AI and have the AI ​​adjust the way the analysis results are presented.

[0094] The providing unit can propose an optimal solution based on the employee's past solution history. For example, if there is a history of solving a similar problem in the past, that solution can be reused. The providing unit can also analyze the past solution history and propose the most effective solution. Furthermore, the providing unit can improve the accuracy of the solution based on the past solution history. This makes it possible to provide an optimal solution that makes use of the employee's past experience. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the employee's past solution history into AI and have the AI ​​execute the proposal of an optimal solution.

[0095] The accumulation unit can estimate an employee's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if an employee is stressed, the frequency of data collection can be reduced, and if the employee is relaxed, the frequency of data collection can be increased. Furthermore, if an employee is concentrating, data collection can be scheduled at that time to minimize interruptions to work. Furthermore, if an employee is tired, data collection can be refrained from and resumed after the employee has taken a break. This allows data to be collected at an optimal frequency depending on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Examples of generative AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the accumulation unit may be performed using, for example, AI, or without AI. For example, the accumulation unit can input employee emotion data into an AI and have the AI ​​adjust the data collection frequency.

[0096] The solution provision unit can customize the solution provision method based on the employee's job title and job content. For example, strategic solutions can be provided to managerial employees, while solutions showing specific procedures can be provided to general employees. Solutions related to specific job content can also be provided only to employees related to that job. Furthermore, solutions can be provided to new employees as part of their education and training. This makes it possible to provide optimal solutions according to job titles and job content. Some or all of the above-mentioned processing in the solution provision unit can be performed using, or without, AI, for example. For example, the solution provision unit can input data on employee job titles and job content into AI and have the AI ​​customize the solution provision method.

[0097] The analysis unit can estimate the employee's emotions and adjust the level of analysis detail based on the estimated emotions. For example, if the employee is stressed, a concise and to-the-point analysis result can be provided. Alternatively, if the employee is relaxed, a detailed analysis result can be provided. Furthermore, if the employee is in a hurry, a short and to-the-point analysis result can be provided. This allows for optimal analysis results to be provided according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input employee emotion data into AI and have the AI ​​adjust the level of analysis detail.

[0098] The accumulation unit can customize the data collection method based on the employee's past feedback. For example, the accumulation unit can prioritize the use of a collection method preferred by the employee based on the past feedback. The accumulation unit can also reflect the feedback and improve the collection method to collect data efficiently. Furthermore, the accumulation unit can customize the collection method based on the employee's feedback to collect data more effectively. This makes it possible to optimize the collection method based on the past feedback. Some or all of the above-mentioned processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can input the employee's past feedback data into AI and have the AI ​​customize the collection method.

[0099] The providing unit can estimate the employee's emotions and adjust the length of the solution based on the estimated emotions. For example, if the employee is in a hurry, a short and concise solution can be provided. If the employee is relaxed, a detailed solution can be provided. Furthermore, if the employee is excited, a solution with a visually stimulating effect can be provided. This allows a solution of optimal length to be provided according to the employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the employee's emotion data into an AI and have the AI ​​adjust the length of the solution.

[0100] The analysis unit can adjust the way in which the analysis results are presented based on the employee's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terms to employees with high levels of expertise. On the other hand, the analysis results can be provided in easy-to-understand language to employees with low levels of expertise. Furthermore, the way in which the analysis results are presented can be dynamically adjusted according to the employee's level of expertise. This makes it possible to provide optimal analysis results according to the employee's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's level of expertise into AI and have the AI ​​adjust the way in which the analysis results are presented.

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

[0102] Step 1: The collection unit collects text data and audio data generated daily. For example, it collects text data from emails and chats, and audio data from telephone calls and online conference systems. The collection unit can also set the frequency and means of data collection, and can collect data at a fixed time each day or when a specific event occurs. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the areas where employees are having trouble, making mistakes, and not understanding. For example, text data can be analyzed using text mining technology, and voice data can be analyzed using voice recognition technology. Furthermore, AI can be used to identify the areas where employees are having trouble, making mistakes, and not understanding. Step 3: The provision department provides solutions to the problems identified by the analysis department. For example, if there are many questions about a specific work procedure, the provision department can provide a guide that clearly explains the procedure and warn employees about points where they are likely to make mistakes. In addition, appropriate solutions can be presented using AI.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An accumulation unit that accumulates text data and voice data generated daily; an analysis unit that analyzes the data collected by the collection unit and identifies things that employees are having trouble with, are prone to making mistakes about, and do not understand; a solution providing unit that provides a solution to the problem identified by the analysis unit. A system characterized by:

2. The accumulation section is Collecting text data from emails or chats, and audio data from telephone or online conference systems 2. The system of claim 1.

3. The analysis unit Analyze the collected data to identify areas where employees are struggling, prone to make mistakes, or don't understand 2. The system of claim 1.

4. The providing unit If there are many questions about a particular business procedure, provide a guide that explains that procedure in detail.

2. The system of claim 1.

5. The providing unit Remind employees about points where they are likely to make mistakes 2. The system of claim 1.

6. The accumulation section is Estimate user emotions and adjust data collection timing based on the estimated user emotions.

2. The system of claim 1.

7. The accumulation section is When collecting data, filter based on employee title or job function 2. The system of claim 1.

8. The accumulation section is When collecting data, select what to collect based on employees' past question history 2. The system of claim 1.

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