system

Abstract

The system according to the embodiment aims to provide quick and efficient answers to inquiries within a company. [Solution] A system according to an embodiment includes a collection unit, a response unit, and a provision unit. The collection unit collects information within a company. The response unit provides responses in chat format based on the information collected by the collection unit. The provision unit manages employee information and provides information in a push format.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that responses to inquiries within a company can be delayed, making it difficult to provide information efficiently.

[0005] The system according to the embodiment aims to provide quick and efficient answers to inquiries within a company. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a response unit, and a provision unit. The collection unit collects information within the company. The response unit provides responses in chat format based on the information collected by the collection unit. The provision unit manages employee information and provides information in a push format. [Effects of the Invention]

[0007] The system according to the embodiment can provide quick and efficient answers to inquiries within a company. [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) An information provision system according to an embodiment of the present invention inputs various corporate information into a generation AI and provides chat-style answers when employees ask for help. This information provision system collects corporate information and inputs it into the generation AI, allowing employees to quickly obtain the information they need. For example, the system collects information such as business procedures, FAQs, company regulations, and manuals and inputs it into the generation AI. Then, when an employee asks for help, the generation AI provides a chat-style answer. For example, if an employee asks, "How do I apply for vacation?", the generation AI immediately provides an answer. Furthermore, by inputting employee information into the generation AI, the generation AI can proactively provide necessary information in a push-style manner. For example, when a new employee joins the company, the generation AI automatically provides the necessary procedures and information. This allows employees to quickly obtain the information they need, improving work efficiency. This system eliminates the need for employees to consult with a contact point or person in charge and allows them to receive answers quickly. This reduces the burden on the person being consulted, allowing them to focus on other tasks. Furthermore, the system can quickly respond to infrequent inquiries, eliminating the need for time-consuming responses. As a specific example, when an employee asks, "How do I go about going on a business trip?", the generating AI will respond, "Submit a business trip application form and get approval from your supervisor." Similarly, when an employee asks, "How do I reserve a conference room within the company?", the generating AI will respond, "Access the conference room reservation system and select the desired date and time." In this way, by utilizing generating AI, employees can quickly obtain the information they need, improving work efficiency. As a result, the information provision system allows employees to quickly obtain the information they need, improving work efficiency.

[0029] An information provision system according to an embodiment includes a collection unit, a response unit, and a provision unit. The collection unit collects information within a company. The collection unit collects information such as business procedures, FAQs, company regulations, and manuals. When collecting business procedures, the collection unit may collect daily business procedures and procedures for specific projects. When collecting FAQs, the collection unit may collect general questions and technical questions. When collecting company regulations, the collection unit may collect information such as working hours, vacation policies, and compliance regulations. When collecting manuals, the collection unit may collect information such as operation manuals and troubleshooting guides. The response unit provides answers to employees' questions in a chat format based on the collected information. The response unit may provide answers using, for example, text-based chat. The response unit may also provide answers using voice chat or video chat. For example, if an employee asks, "How do I apply for vacation?", the response unit may respond, "Submit a vacation application and get approval from your supervisor." Furthermore, when an employee asks, "How do I reserve an in-house conference room?" the answering unit replies, "Access the conference room reservation system and select the desired date and time." The providing unit manages employee information and provides information in a push-based manner. The providing unit can provide appropriate information in a push-based manner, for example, depending on the employee's position or department. The providing unit can automatically provide necessary procedures and information, for example, when a new employee joins the company. The providing unit can also provide information when a new employee is transferred or at a specific event (e.g., during year-end tax adjustments). For example, the providing unit can automatically provide necessary procedures and information during year-end tax adjustments. As a result, the information providing system according to the embodiment allows employees to quickly obtain the information they need, thereby improving work efficiency.

[0030] The collection department can collect information on business procedures, FAQs, company regulations, and manuals. For example, when collecting business procedures, the collection department can collect daily work procedures and procedures for specific projects. When collecting FAQs, the collection department can collect general questions and technical questions. When collecting company regulations, the collection department can collect information such as working hours, vacation systems, and compliance regulations. When collecting manuals, the collection department can collect information such as operation manuals and troubleshooting guides. By collecting a variety of information within the company, the collection department can provide appropriate answers to employee questions.

[0031] The answering unit can provide answers to employees' questions in chat format based on the collected information. The answering unit can provide answers using, for example, text-based chat. The answering unit can also provide answers using voice chat or video chat. For example, if an employee asks, "How do I apply for vacation?" the answering unit can respond, "Submit a vacation application form and get approval from your supervisor." Furthermore, if an employee asks, "How do I reserve a conference room within the company?" the answering unit can respond, "Access the conference room reservation system and select the desired date and time." This allows answers to employees' questions to be provided quickly in chat format.

[0032] The provision unit can provide appropriate information in a push format according to the employee's position or department. The provision unit can provide appropriate information in a push format according to, for example, the employee's position or department. The provision unit can automatically provide the necessary procedures and information, for example, when a new employee joins the company. The provision unit can also provide information when an employee is transferred or at a specific event (e.g., around the time of year-end tax adjustment). For example, the provision unit can automatically provide the necessary procedures and information around the time of year-end tax adjustment. This makes it possible to improve work efficiency by providing appropriate information according to the employee's position or department.

[0033] The provision unit can provide information for specific events such as when a new employee joins the company, when they are transferred, or at the time of year-end tax adjustments. For example, the provision unit can automatically provide the necessary procedures and information when a new employee joins the company. The provision unit can also provide information when a new employee is transferred or at specific events (e.g., at the time of year-end tax adjustments). For example, the provision unit can automatically provide the necessary procedures and information at the time of year-end tax adjustments. In this way, by providing information according to specific events, employees can quickly obtain the information they need.

[0034] The collection unit can periodically update the information and reflect it in the generation AI. The collection unit can, for example, periodically update the information and reflect it in the generation AI. Periodically, for example, this can be done daily, weekly, monthly, etc. By periodically updating the information, it is possible to provide answers based on the latest information.

[0035] The collection unit can analyze past information collection history and select the most appropriate collection method. For example, the collection unit can identify and apply the most efficient collection method from the past information collection history. The collection unit can also optimize the collection frequency based on the past information collection history. Furthermore, the collection unit can analyze the past information collection history and determine the priority of collection targets. As a result, by analyzing the past information collection history, the optimal collection method can be selected and information can be collected efficiently.

[0036] When collecting information, the collection unit can filter the information based on the employee's current project or area of ​​interest. For example, the collection unit can prioritize the collection of information related to the project the employee is currently working on. The collection unit can also filter and collect related information based on the employee's area of ​​interest. Furthermore, the collection unit can filter and collect necessary information according to the employee's position or department. This makes it possible to efficiently collect necessary information by filtering information based on the employee's current project or area of ​​interest.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information based on the employee's geographical location information. For example, if an employee is on a business trip, the collection unit can prioritize collecting information related to the business trip destination. Also, if an employee is working remotely, the collection unit can prioritize collecting information around the employee's home. Furthermore, if an employee is in a specific office, the collection unit can prioritize collecting information related to that office. In this way, by taking the employee's geographical location information into consideration, highly relevant information can be prioritized.

[0038] When collecting information, the collection unit can analyze employees' social media activities and collect related information. For example, the collection unit can collect related information based on information shared by employees on social media. The collection unit can also analyze employees' interests on social media and collect related information. Furthermore, the collection unit can collect related information based on information on accounts followed by employees. This makes it possible to efficiently collect related information by analyzing employees' social media activities.

[0039] When generating an answer, the answering unit can adjust the level of detail of the answer based on the importance of the question. For example, the answering unit can provide a detailed answer to a question of high importance. Also, the answering unit can provide a concise answer to a question of low importance. Furthermore, the answering unit can gradually adjust the level of detail of the answer depending on the importance of the question. In this way, by adjusting the level of detail of the answer depending on the importance of the question, it is possible to provide an appropriate answer.

[0040] When generating an answer, the answering unit can apply different answering algorithms depending on the category of the question. For example, the answering unit can apply a specialized algorithm to technical questions to generate an answer. The answering unit can also apply a simple algorithm to general questions to generate an answer. Furthermore, the answering unit can apply an algorithm specialized for customer support to generate an answer. This makes it possible to provide an accurate answer by applying an appropriate answering algorithm depending on the category of the question.

[0041] When generating an answer, the answering unit can determine the priority of the answer based on the time of submission of the question. The answering unit can determine the priority of the answer based on, for example, the time of day when the question was submitted. The answering unit can also determine the priority of the answer based on the day of the week when the question was submitted. Furthermore, the answering unit can determine the priority of the answer based on the month when the question was submitted. In this way, by determining the priority of the answer based on the time of submission of the question, answers can be provided quickly.

[0042] The answering unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, if the relevance of a question is high, the answering unit can provide the answer preferentially. Also, if the relevance of a question is low, the answering unit can provide the answer later. Furthermore, the answering unit can gradually adjust the order of answers according to the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, it is possible to give priority to answers to highly relevant questions.

[0043] When providing information, the provision unit can analyze the employee's past behavioral history and select the optimal information provision method. The provision unit can, for example, identify and apply the optimal information provision method from the employee's past behavioral history. The provision unit can also optimize the frequency of information provision based on the employee's past behavioral history. Furthermore, the provision unit can analyze the employee's past behavioral history and adjust the timing of information provision. In this way, by analyzing the employee's past behavioral history, the optimal information provision method can be selected and information can be provided efficiently.

[0044] When providing information, the providing unit can customize the means of providing information based on the employee's current position or department. The providing unit can select an appropriate means of providing information, for example, depending on the employee's position. The providing unit can also provide relevant information depending on the employee's department. Furthermore, the providing unit can customize the method of providing information based on the employee's position or department. This makes it possible to provide appropriate information by customizing the means of providing information depending on the employee's position or department.

[0045] When providing information, the providing unit can select the optimal information providing method based on the employee's geographical location information. For example, if an employee is on a business trip, the providing unit can prioritize providing information related to the business trip destination. Also, if an employee is working remotely, the providing unit can prioritize providing information around the employee's home. Furthermore, if an employee is in a specific office, the providing unit can prioritize providing information related to that office. In this way, highly relevant information can be provided by taking the employee's geographical location information into consideration.

[0046] When providing information, the provision unit can analyze the employee's social media activity and suggest a means of providing the information. For example, the provision unit can provide related information based on information shared by the employee on social media. The provision unit can also analyze the employee's interests on social media and provide related information. Furthermore, the provision unit can provide related information based on information on accounts the employee follows. In this way, by analyzing the employee's social media activity, related information can be provided efficiently.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The information provision system can further include a learning unit. The learning unit can train the generation AI based on collected information to improve the accuracy of answers. For example, the learning unit can analyze past questions and their answers to optimize the generation AI's answering algorithm. The learning unit can also update the generation AI's knowledge base when new information is collected and provide answers based on the latest information. Furthermore, the learning unit can evaluate the quality of the generation AI's answers based on employee feedback and adjust the learning data as necessary. This allows the information provision system to continuously learn and provide more accurate and appropriate answers.

[0049] The information provision system may further include a notification unit. The notification unit can provide notifications at appropriate times so that employees do not miss important information. For example, the notification unit can send a reminder when an employee is approaching a deadline for performing a specific procedure. The notification unit can also notify employees when they receive updated information on new company regulations or manuals. Furthermore, the notification unit can send a reminder when an employee is about to attend an important meeting or event. This allows the information provision system to ensure that employees do not miss important information and take action at appropriate times.

[0050] The information provision system may further include a customization unit. The customization unit may customize the method and content of information provision according to the individual needs and preferences of each employee. For example, the customization unit may select the information provision format (text, audio, video, etc.) preferred by the employee. The customization unit may also provide necessary information preferentially according to the employee's position or department. Furthermore, the customization unit may suggest related information based on the employee's past question history. This allows the information provision system to provide information according to the individual needs of each employee and provide more effective support.

[0051] The information provision system may further include an analysis unit. The analysis unit analyzes the collected information and employee question history to identify trends and problems throughout the company. For example, if a particular question is frequently asked, the analysis unit may suggest that there may be a lack of information in that field. The analysis unit may also identify problems in a particular department or project based on the employee question history. Furthermore, the analysis unit may make suggestions to improve business efficiency throughout the company based on the collected information. This allows the information provision system to grasp trends and problems throughout the company and take appropriate measures.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The collection department collects information within the company. The collection department collects information such as business procedures, FAQs, internal regulations, and manuals. For example, they collect information on daily work procedures, procedures for specific projects, general questions, technical questions, working hours, vacation policies, compliance regulations, operation manuals, and troubleshooting guides. Step 2: The answering department uses the collected information to provide a chat-style answer to the employee's question. The answering department can provide answers using text-based chat, voice chat, or video chat. For example, if an employee asks, "How do I apply for vacation?", the answering department will respond, "Submit a vacation application and get your manager's approval." Or, if an employee asks, "How do I reserve a conference room within the company?", the answering department will respond, "Access the conference room reservation system and select the desired date and time." Step 3: The data provisioning unit manages employee information and provides it in a push format. The data provisioning unit can provide appropriate information in a push format depending on the employee's position or department. For example, it automatically provides the procedures and information required when a new employee joins the company, or when a transfer or specific events (e.g., during year-end tax adjustments) are handled.

[0054] (Example 2) An information provision system according to an embodiment of the present invention inputs various corporate information into a generation AI and provides chat-style answers when employees ask for help. This information provision system collects corporate information and inputs it into the generation AI, allowing employees to quickly obtain the information they need. For example, the system collects information such as business procedures, FAQs, company regulations, and manuals and inputs it into the generation AI. Then, when an employee asks for help, the generation AI provides a chat-style answer. For example, if an employee asks, "How do I apply for vacation?", the generation AI immediately provides an answer. Furthermore, by inputting employee information into the generation AI, the generation AI can proactively provide necessary information in a push-style manner. For example, when a new employee joins the company, the generation AI automatically provides the necessary procedures and information. This allows employees to quickly obtain the information they need, improving work efficiency. This system eliminates the need for employees to consult with a contact point or person in charge and allows them to receive answers quickly. This reduces the burden on the person being consulted, allowing them to focus on other tasks. Furthermore, the system can quickly respond to infrequent inquiries, eliminating the need for time-consuming responses. As a specific example, when an employee asks, "How do I go about going on a business trip?", the generating AI will respond, "Submit a business trip application form and get approval from your supervisor." Similarly, when an employee asks, "How do I reserve a conference room within the company?", the generating AI will respond, "Access the conference room reservation system and select the desired date and time." In this way, by utilizing generating AI, employees can quickly obtain the information they need, improving work efficiency. As a result, the information provision system allows employees to quickly obtain the information they need, improving work efficiency.

[0055] An information provision system according to an embodiment includes a collection unit, a response unit, and a provision unit. The collection unit collects information within a company. The collection unit collects information such as business procedures, FAQs, company regulations, and manuals. When collecting business procedures, the collection unit may collect daily business procedures and procedures for specific projects. When collecting FAQs, the collection unit may collect general questions and technical questions. When collecting company regulations, the collection unit may collect information such as working hours, vacation policies, and compliance regulations. When collecting manuals, the collection unit may collect information such as operation manuals and troubleshooting guides. The response unit provides answers to employees' questions in a chat format based on the collected information. The response unit may provide answers using, for example, text-based chat. The response unit may also provide answers using voice chat or video chat. For example, if an employee asks, "How do I apply for vacation?", the response unit may respond, "Submit a vacation application and get approval from your supervisor." Furthermore, when an employee asks, "How do I reserve an in-house conference room?" the answering unit replies, "Access the conference room reservation system and select the desired date and time." The providing unit manages employee information and provides information in a push-based manner. The providing unit can provide appropriate information in a push-based manner, for example, depending on the employee's position or department. The providing unit can automatically provide necessary procedures and information, for example, when a new employee joins the company. The providing unit can also provide information when a new employee is transferred or at a specific event (e.g., during year-end tax adjustments). For example, the providing unit can automatically provide necessary procedures and information during year-end tax adjustments. As a result, the information providing system according to the embodiment allows employees to quickly obtain the information they need, thereby improving work efficiency.

[0056] The collection department can collect information on business procedures, FAQs, company regulations, and manuals. For example, when collecting business procedures, the collection department can collect daily work procedures and procedures for specific projects. When collecting FAQs, the collection department can collect general questions and technical questions. When collecting company regulations, the collection department can collect information such as working hours, vacation systems, and compliance regulations. When collecting manuals, the collection department can collect information such as operation manuals and troubleshooting guides. By collecting a variety of information within the company, the collection department can provide appropriate answers to employee questions.

[0057] The answering unit can provide answers to employees' questions in chat format based on the collected information. The answering unit can provide answers using, for example, text-based chat. The answering unit can also provide answers using voice chat or video chat. For example, if an employee asks, "How do I apply for vacation?" the answering unit can respond, "Submit a vacation application form and get approval from your supervisor." Furthermore, if an employee asks, "How do I reserve a conference room within the company?" the answering unit can respond, "Access the conference room reservation system and select the desired date and time." This allows answers to employees' questions to be provided quickly in chat format.

[0058] The provision unit can provide appropriate information in a push format according to the employee's position or department. The provision unit can provide appropriate information in a push format according to, for example, the employee's position or department. The provision unit can automatically provide the necessary procedures and information, for example, when a new employee joins the company. The provision unit can also provide information when an employee is transferred or at a specific event (e.g., around the time of year-end tax adjustment). For example, the provision unit can automatically provide the necessary procedures and information around the time of year-end tax adjustment. This makes it possible to improve work efficiency by providing appropriate information according to the employee's position or department.

[0059] The provision unit can provide information for specific events such as when a new employee joins the company, when they are transferred, or at the time of year-end tax adjustments. For example, the provision unit can automatically provide the necessary procedures and information when a new employee joins the company. The provision unit can also provide information when a new employee is transferred or at specific events (e.g., at the time of year-end tax adjustments). For example, the provision unit can automatically provide the necessary procedures and information at the time of year-end tax adjustments. In this way, by providing information according to specific events, employees can quickly obtain the information they need.

[0060] The collection unit can periodically update the information and reflect it in the generation AI. The collection unit can, for example, periodically update the information and reflect it in the generation AI. Periodically, for example, this can be done daily, weekly, monthly, etc. By periodically updating the information, it is possible to provide answers based on the latest information.

[0061] The collection unit can estimate the employee's emotions and adjust the timing of information collection based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit can reduce the frequency of information collection to reduce the employee's burden. Furthermore, if the employee is relaxed, the collection unit can increase the frequency of information collection to collect more detailed information. Furthermore, if the employee is busy, the collection unit can adjust the timing of information collection to between work tasks. This allows the employee's burden to be reduced by adjusting the timing of information collection according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0062] The collection unit can analyze past information collection history and select the most appropriate collection method. For example, the collection unit can identify and apply the most efficient collection method from the past information collection history. The collection unit can also optimize the collection frequency based on the past information collection history. Furthermore, the collection unit can analyze the past information collection history and determine the priority of collection targets. As a result, by analyzing the past information collection history, the optimal collection method can be selected and information can be collected efficiently.

[0063] When collecting information, the collection unit can filter the information based on the employee's current project or area of ​​interest. For example, the collection unit can prioritize the collection of information related to the project the employee is currently working on. The collection unit can also filter and collect related information based on the employee's area of ​​interest. Furthermore, the collection unit can filter and collect necessary information according to the employee's position or department. This makes it possible to efficiently collect necessary information by filtering information based on the employee's current project or area of ​​interest.

[0064] The collection unit can estimate the employee's emotions and determine the priority of information to be collected based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit can prioritize collecting information of high importance. Furthermore, if the employee is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, if the employee is busy, the collection unit can prioritize collecting information directly related to work. In this way, by determining the priority of information to be collected according to the employee's emotions, important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0065] When collecting information, the collection unit can prioritize collecting highly relevant information based on the employee's geographical location information. For example, if an employee is on a business trip, the collection unit can prioritize collecting information related to the business trip destination. Also, if an employee is working remotely, the collection unit can prioritize collecting information around the employee's home. Furthermore, if an employee is in a specific office, the collection unit can prioritize collecting information related to that office. In this way, by taking the employee's geographical location information into consideration, highly relevant information can be prioritized.

[0066] When collecting information, the collection unit can analyze employees' social media activities and collect related information. For example, the collection unit can collect related information based on information shared by employees on social media. The collection unit can also analyze employees' interests on social media and collect related information. Furthermore, the collection unit can collect related information based on information on accounts followed by employees. This makes it possible to efficiently collect related information by analyzing employees' social media activities.

[0067] The answering unit can estimate the employee's emotions and adjust the way the answer is expressed based on the estimated employee's emotions. For example, if the employee is stressed, the answering unit can provide a concise and clear answer. Furthermore, if the employee is relaxed, the answering unit can provide an answer that includes detailed explanations. Furthermore, if the employee is in a hurry, the answering unit can provide a quick, to-the-point answer. This allows for an appropriate answer to be provided by adjusting the way the answer is expressed depending on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] When generating an answer, the answering unit can adjust the level of detail of the answer based on the importance of the question. For example, the answering unit can provide a detailed answer to a question of high importance. Also, the answering unit can provide a concise answer to a question of low importance. Furthermore, the answering unit can gradually adjust the level of detail of the answer depending on the importance of the question. In this way, by adjusting the level of detail of the answer depending on the importance of the question, it is possible to provide an appropriate answer.

[0069] When generating an answer, the answering unit can apply different answering algorithms depending on the category of the question. For example, the answering unit can apply a specialized algorithm to technical questions to generate an answer. The answering unit can also apply a simple algorithm to general questions to generate an answer. Furthermore, the answering unit can apply an algorithm specialized for customer support to generate an answer. This makes it possible to provide an accurate answer by applying an appropriate answering algorithm depending on the category of the question.

[0070] The answering unit can estimate the employee's emotions and adjust the length of the answer based on the estimated employee emotions. For example, if the employee is stressed, the answering unit can provide a short, to-the-point answer. If the employee is relaxed, the answering unit can provide a longer answer with detailed explanations. If the employee is in a hurry, the answering unit can provide a short, to-the-point answer quickly. This allows for appropriate answers to be provided by adjusting the length of the answer according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] When generating an answer, the answering unit can determine the priority of the answer based on the time of submission of the question. The answering unit can determine the priority of the answer based on, for example, the time of day when the question was submitted. The answering unit can also determine the priority of the answer based on the day of the week when the question was submitted. Furthermore, the answering unit can determine the priority of the answer based on the month when the question was submitted. In this way, by determining the priority of the answer based on the time of submission of the question, answers can be provided quickly.

[0072] The answering unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, if the relevance of a question is high, the answering unit can provide the answer preferentially. Also, if the relevance of a question is low, the answering unit can provide the answer later. Furthermore, the answering unit can gradually adjust the order of answers according to the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, it is possible to give priority to answers to highly relevant questions.

[0073] The providing unit can estimate the employee's emotions and determine the priority of information to be provided based on the estimated employee emotions. For example, if the employee is feeling stressed, the providing unit can prioritize providing information of high importance. Furthermore, if the employee is relaxed, the providing unit can prioritize providing detailed information. Furthermore, if the employee is busy, the providing unit can prioritize providing information directly related to work. In this way, by determining the priority of information to be provided according to the employee's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] When providing information, the provision unit can analyze the employee's past behavioral history and select the optimal information provision method. The provision unit can, for example, identify and apply the optimal information provision method from the employee's past behavioral history. The provision unit can also optimize the frequency of information provision based on the employee's past behavioral history. Furthermore, the provision unit can analyze the employee's past behavioral history and adjust the timing of information provision. In this way, by analyzing the employee's past behavioral history, the optimal information provision method can be selected and information can be provided efficiently.

[0075] When providing information, the providing unit can customize the means of providing information based on the employee's current position or department. The providing unit can select an appropriate means of providing information, for example, depending on the employee's position. The providing unit can also provide relevant information depending on the employee's department. Furthermore, the providing unit can customize the method of providing information based on the employee's position or department. This makes it possible to provide appropriate information by customizing the means of providing information depending on the employee's position or department.

[0076] The providing unit can estimate the employee's emotions and adjust the display method of the information to be provided based on the estimated employee's emotions. For example, if the employee is nervous, the providing unit can provide a simple, highly visible display method. If the employee is relaxed, the providing unit can provide a display method that includes detailed information. If the employee is in a hurry, the providing unit can provide a display method that focuses on the main points. In this way, by adjusting the information display method according to the employee's emotions, highly visible information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] When providing information, the providing unit can select the optimal information providing method based on the employee's geographical location information. For example, if an employee is on a business trip, the providing unit can prioritize providing information related to the business trip destination. Also, if an employee is working remotely, the providing unit can prioritize providing information around the employee's home. Furthermore, if an employee is in a specific office, the providing unit can prioritize providing information related to that office. In this way, highly relevant information can be provided by taking the employee's geographical location information into consideration.

[0078] When providing information, the provision unit can analyze the employee's social media activity and suggest a means of providing the information. For example, the provision unit can provide related information based on information shared by the employee on social media. The provision unit can also analyze the employee's interests on social media and provide related information. Furthermore, the provision unit can provide related information based on information on accounts the employee follows. In this way, by analyzing the employee's social media activity, related information can be provided efficiently. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, response unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information within the company using the camera 42 and microphone 38B of the smart device 14, and transmits the collected information to the data processing device 12 by the control unit 46A. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides answers to employee questions in chat format based on the collected information. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and manages employee information and provides information in a push format. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, response unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information within the company using the camera 42 and microphone 238 of the smart glasses 214, and transmits the collected information to the data processing device 12 by the control unit 46A. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides answers to employee questions in a chat format based on the collected information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and manages employee information and provides information in a push format. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, response unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information within the company using the camera 42 and microphone 238 of the headset type terminal 314, and transmits the collected information to the data processing device 12 by the control unit 46A. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides answers to employee questions in a chat format based on the collected information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and manages employee information and provides information in a push format. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, response unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information within the company using the camera 42 and microphone 238 of the robot 414, and transmits the collected information to the data processing device 12 by the control unit 46A. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides answers to employee questions in a chat format based on the collected information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and manages employee information and provides information in a push format.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The information provision system can further include a feedback collection unit. The feedback collection unit can collect how employees felt about the answers provided by the generation AI and use the collected information to improve the system. For example, if an employee is satisfied with an answer, the feedback can be collected to maintain the quality of answers to similar questions. Also, if an employee is dissatisfied with an answer, the content of the answer can be improved based on the feedback. Furthermore, the feedback collection unit can evaluate whether the answer was easy for employees to understand and adjust the way the answer is expressed as necessary. This allows the information provision system to be continuously improved based on employee feedback and provide more appropriate answers.

[0081] The information provision system can further include a learning unit. The learning unit can train the generation AI based on collected information to improve the accuracy of answers. For example, the learning unit can analyze past questions and their answers to optimize the generation AI's answering algorithm. The learning unit can also update the generation AI's knowledge base when new information is collected and provide answers based on the latest information. Furthermore, the learning unit can evaluate the quality of the generation AI's answers based on employee feedback and adjust the learning data as necessary. This allows the information provision system to continuously learn and provide more accurate and appropriate answers.

[0082] The information provision system may further include a notification unit. The notification unit can provide notifications at appropriate times so that employees do not miss important information. For example, the notification unit can send a reminder when an employee is approaching a deadline for performing a specific procedure. The notification unit can also notify employees when they receive updated information on new company regulations or manuals. Furthermore, the notification unit can send a reminder when an employee is about to attend an important meeting or event. This allows the information provision system to ensure that employees do not miss important information and take action at appropriate times.

[0083] The information provision system may further include a customization unit. The customization unit may customize the method and content of information provision according to the individual needs and preferences of each employee. For example, the customization unit may select the information provision format (text, audio, video, etc.) preferred by the employee. The customization unit may also provide necessary information preferentially according to the employee's position or department. Furthermore, the customization unit may suggest related information based on the employee's past question history. This allows the information provision system to provide information according to the individual needs of each employee and provide more effective support.

[0084] The information provision system may further include an analysis unit. The analysis unit analyzes the collected information and employee question history to identify trends and problems throughout the company. For example, if a particular question is frequently asked, the analysis unit may suggest that there may be a lack of information in that field. The analysis unit may also identify problems in a particular department or project based on the employee question history. Furthermore, the analysis unit may make suggestions to improve business efficiency throughout the company based on the collected information. This allows the information provision system to grasp trends and problems throughout the company and take appropriate measures.

[0085] The information provision system may further include a sentiment analysis unit. The sentiment analysis unit may infer the employee's sentiment from the content of the question and feedback and adjust the system's response accordingly. For example, if the employee is feeling stressed, the sentiment analysis unit may provide an answer in a gentle tone. If the employee is relaxed, the sentiment analysis unit may provide an answer with detailed explanations. Furthermore, if the employee is in a hurry, the sentiment analysis unit may provide a quick, concise answer. This allows the information provision system to provide appropriate responses according to the employee's sentiment, thereby achieving a better user experience.

[0086] The information provision system can further include an emotional feedback unit. The emotional feedback unit can collect how employees felt about the answers provided by the generation AI and use the information to improve the system. For example, if an employee is satisfied with an answer, the feedback can be collected to maintain the quality of answers to similar questions. Also, if an employee is dissatisfied with an answer, the content of the answer can be improved based on the feedback. Furthermore, the emotional feedback unit can evaluate whether the answer was easy for the employee to understand and adjust the way the answer is expressed as necessary. This allows the information provision system to be continuously improved based on employees' emotional feedback and provide more appropriate answers.

[0087] The information provision system may further include an emotion monitoring unit. The emotion monitoring unit may monitor the emotional state of employees in real time and provide appropriate support as needed. For example, if an employee is feeling stressed, the emotion monitoring unit may provide advice or resources to help them relax. If the employee is relaxed, the emotion monitoring unit may provide an environment in which the employee can concentrate on their work. Furthermore, if the employee is in a hurry, the emotion monitoring unit may provide resources to respond quickly. This allows the information provision system to provide appropriate support according to the employee's emotional state and improve work efficiency.

[0088] The information provision system may further include an emotion prediction unit. The emotion prediction unit can predict an employee's future emotional state based on the employee's past behavior and question history, and take appropriate measures. For example, if an employee is prone to feeling stressed at a certain time, the emotion prediction unit can provide resources to help the employee relax at that time. If an employee is prone to feeling nervous when working on a particular project, the emotion prediction unit can provide support related to that project. If an employee is prone to feeling anxious when participating in a particular event, the emotion prediction unit can provide information related to the event in advance. In this way, the information provision system can predict an employee's future emotional state and take appropriate measures, thereby improving work efficiency.

[0089] The information provision system may further include an emotion adjustment unit. The emotion adjustment unit may adjust the emotional state of the employee and provide an environment in which the employee can concentrate on their work. For example, if the employee is feeling stressed, the emotion adjustment unit may provide music or resources to help the employee relax. Also, if the employee is relaxed, the emotion adjustment unit may provide an environment in which the employee can concentrate on their work. Furthermore, if the employee is in a hurry, the emotion adjustment unit may provide resources to respond quickly. In this way, the information provision system may adjust the emotional state of the employee and improve work efficiency.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The collection department collects information within the company. The collection department collects information such as business procedures, FAQs, internal regulations, and manuals. For example, they collect information on daily work procedures, procedures for specific projects, general questions, technical questions, working hours, vacation policies, compliance regulations, operation manuals, and troubleshooting guides. Step 2: The answering department uses the collected information to provide a chat-style answer to the employee's question. The answering department can provide answers using text-based chat, voice chat, or video chat. For example, if an employee asks, "How do I apply for vacation?", the answering department will respond, "Submit a vacation application and get your manager's approval." Or, if an employee asks, "How do I reserve a conference room within the company?", the answering department will respond, "Access the conference room reservation system and select the desired date and time." Step 3: The data provisioning unit manages employee information and provides it in a push format. The data provisioning unit can provide appropriate information in a push format depending on the employee's position or department. For example, it automatically provides the procedures and information required when a new employee joins the company, or when a transfer or specific events (e.g., during year-end tax adjustments) are handled.

[0092] 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.

[0093] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0094] 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.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information within the company; a reply unit that provides a reply in a chat format based on the information collected by the collection unit; A providing unit that manages employee information and provides information in a push format. A system characterized by:

2. The collecting unit Collect information on business procedures, FAQs, company regulations, and manuals The system of claim 1 .

3. The answering section Based on the collected information, answers are provided to employees' questions in chat format. The system of claim 1 .

4. The providing unit Providing appropriate information via push notifications based on employee position and department The system of claim 1 .

5. The providing unit Providing information for specific events such as when new employees join the company, when they transfer, and when it comes to year-end tax adjustments The system of claim 1 .

6. The collecting unit Regularly update the information and reflect it in the generated AI The system of claim 1 .

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

8. The collecting unit Analyze past information collection history and select the most appropriate collection method The system of claim 1 .

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