System

Abstract

An object of the system according to the embodiment is to efficiently acquire necessary information from tools and guide pages scattered in a company and recommend an optimal method.SOLUTION: A system according to an embodiment includes a search unit and a recommendation unit. The search unit searches across the in-house tool and the guide page. The recommendation unit recommends an optimum method from the information acquired by the search unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently obtain necessary information from tools and guide pages scattered throughout the company.

[0005] The system according to the embodiment aims to efficiently obtain necessary information from tools and guide pages scattered throughout the company and recommend the most suitable method. [Means for solving the problem]

[0006] The system according to the embodiment includes a search unit and a recommendation unit. The search unit performs a cross-sectional search across internal tools and guide pages. The recommendation unit recommends the optimal method based on the information acquired by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently obtain necessary information from tools and guide pages scattered throughout the company and recommend the most suitable method. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The recommendation system according to the embodiment of the present invention is a system that cross-searches various tools and guide pages scattered throughout the company and instantly recommends the optimal method for the information that employees want to know or the content that they want to apply for. As a result, the recommendation system can significantly reduce the time that employees spend on activities other than their main work.

[0029] A recommendation system according to an embodiment includes a search unit and a recommendation unit. The search unit performs a cross-sectional search across internal tools and guide pages. For example, the search unit searches an internal project management tool or document management tool to acquire relevant information. The search unit can also search operation manuals or FAQ pages to collect necessary information. The search unit efficiently searches for information using a full-text search or a metadata search. For example, the search unit searches task information in a project management tool to acquire relevant documents. The recommendation unit recommends an optimal method from the information acquired by the search unit. For example, the recommendation unit selects the most efficient and easy-to-understand method based on the acquired information and presents it to employees. The recommendation unit can also evaluate and recommend an optimal method based on a user's past behavioral history and work content. For example, the recommendation unit presents an optimal procedure based on a history of past business trip applications. This allows the recommendation system according to an embodiment to quickly and accurately respond to the information employees want to know or the content of their applications. For example, if an employee wants to know how to apply for a business trip, the recommendation system can present the optimal procedure and quickly complete the application process.In addition, since the recommendation system makes recommendations based on the latest information, it can always provide accurate information.

[0030] The search unit can analyze user input in real time and instantly update search results. For example, the search unit uses a generation AI to analyze user input in real time and instantly update search results. For example, the moment a user types "business trip application," the latest related guide pages and tools are displayed. The search unit can also analyze user input in real time using streaming data analysis. For example, the search unit uses an in-memory database to quickly update search results. This allows for a rapid response to user input. For example, even if a user changes their input, the latest search results can be provided instantly.

[0031] The search unit can analyze the metadata of internal tools and guide pages to improve the accuracy of search results. For example, the search unit uses a generation AI to analyze the metadata of internal tools and guide pages to improve the accuracy of search results. For example, it prioritizes displaying highly relevant information based on the keywords and tags of each page. The search unit can also analyze tag information and attribute data to improve the accuracy of search results. For example, the search unit filters related information based on metadata to provide highly accurate search results. This improves the accuracy of search results. For example, users can find the information they need quickly.

[0032] The search unit can provide search results through a voice assistant, allowing the user to obtain information hands-free. For example, the search unit uses a generation AI to provide search results through a voice assistant, allowing the user to obtain information hands-free. For example, if the user asks, "How do I apply for a business trip?", the voice will guide the user through the steps. The search unit can also provide search results using a voice assistant such as Amazon Alexa or Google Assistant. For example, the search unit can analyze the user's question using voice recognition technology and provide appropriate information. This allows the user to obtain information hands-free. For example, the user can obtain information by voice even if their hands are full.

[0033] The search unit can visualize search results to enable users to intuitively understand them. For example, the search unit uses a generation AI to visualize search results to enable users to intuitively understand them. For example, the steps for applying for a business trip can be displayed as a flowchart or diagram. The search unit can also visualize search results using graph displays or dashboards. For example, the search unit can display search results as graphs or charts to enable users to visually understand the information. This allows users to intuitively understand the information. For example, visually displaying complex steps can make them easier to understand.

[0034] The recommendation unit can learn past recommendation history and make recommendations that match the user's preferences. For example, the recommendation unit uses a generation AI to learn past recommendation history and make recommendations that match the user's preferences. For example, it presents the optimal procedure based on the history of past business trip applications. The recommendation unit can also make recommendations based on past recommendation results and user feedback. For example, the recommendation unit prioritizes recommending related information based on the user's preferences. This makes it possible to make recommendations that match the user's preferences. For example, it can prioritize providing information that the user prefers.

[0035] The recommendation unit can provide information at the optimal timing, taking into account the user's work situation. For example, the generation AI analyzes the user's work situation and provides information at the optimal timing. For example, if the user is preparing for a business trip, the recommendation unit can recommend the procedure for applying for a business trip. The recommendation unit can also make recommendations based on the progress of tasks and the priority of work. For example, the recommendation unit evaluates the user's work situation and provides information at the appropriate timing. This makes it possible to provide information at a timing that suits the user's work situation. For example, the user can obtain the information they need at the appropriate time.

[0036] The recommendation unit can automatically add recommended content to a calendar according to the user's schedule. For example, the recommendation unit uses a generation AI to automatically add recommended content to the user's schedule. For example, it can add the steps for applying for a business trip to a calendar and set a reminder. The recommendation unit can also use a calendar app or task management tool to reflect the recommended content in the schedule. For example, the recommendation unit can analyze the user's schedule and add recommended content at the appropriate time. This makes it possible to make recommendations that match the user's schedule. For example, the user can remember to complete a procedure.

[0037] The Recommendation Department can share its recommendations with other employees to find the optimal method for the entire team. For example, the Generative AI can share its recommendations with other employees to find the optimal method for the entire team. For example, the procedure for applying for a business trip can be shared with team members to gather their opinions. The Recommendation Department can also share its recommendations within a project team, department team, etc. For example, the Recommendation Department can share its recommendations using shared documents or collaboration tools. This makes it possible to find the optimal method for the entire team. For example, the optimal procedure can be determined based on the opinions of team members.

[0038] The search unit can automatically detect updates to internal tools and guide pages and notify users. For example, the search unit uses a generation AI to automatically detect updates to internal tools and guide pages and notify users. For example, if a procedure for applying for a business trip is changed, a notification is sent immediately. The search unit can also detect updates such as software upgrades and document revisions. For example, the search unit can send notifications to users based on the update information. This allows users to be provided with the latest information. For example, users can always act in accordance with the latest procedures.

[0039] The search unit can evaluate the importance of update information and prioritize notifications of only important updates. For example, the generation AI can evaluate the importance of update information and prioritize notifications of only important updates. For example, if there is a major change in the procedures for business trip applications, that information will be notified immediately. The search unit can also evaluate the importance of update information based on the impact on business operations and urgency. For example, the search unit prioritizes notifications of information with a high level of importance. This allows important update information to be notified preferentially. For example, users can respond without missing important information.

[0040] The search department can automatically post updated information on an internal digital bulletin board to share with all employees. For example, the generation AI of the search department can automatically post updated information on an internal digital bulletin board to share with all employees. For example, it posts changes to procedures for business trip applications on the bulletin board. The search department can also share updated information using an internal portal site or bulletin board app. For example, the search department can post updated information on a digital bulletin board and send notifications to all employees. This allows the latest updated information to be shared with all employees. For example, all employees can act in accordance with the latest procedures.

[0041] The search unit can customize the update information to match the user's work content and provide only highly relevant information. For example, the generation AI can customize the update information to match the user's work content and provide only highly relevant information. For example, information about business trip applications can be notified preferentially to employees who travel frequently. The search unit can also customize update information based on work content such as project management or sales activities. For example, the search unit can analyze the user's work content and provide highly relevant information. This makes it possible to provide only information related to the user's work content. For example, the user can quickly obtain the information they need.

[0042] The recommendation unit can analyze employee usage history in detail and provide the most suitable information to each individual user. For example, the recommendation unit uses a generation AI to analyze employee usage history in detail and provide the most suitable information to each individual user. For example, it can present the most suitable procedure based on past business trip application history. The recommendation unit can also analyze usage history based on access logs and operation history. For example, the recommendation unit can prioritize recommending related information based on the user's usage history. This makes it possible to provide the most suitable information to each individual user. For example, the user can quickly obtain the information they need.

[0043] The recommendation unit can learn the user's work patterns based on usage history and make predictive recommendations. For example, the recommendation unit uses a generation AI to learn work patterns based on employee usage history and make predictive recommendations. For example, the recommendation unit predicts the timing of the next business trip application based on the frequency of business trip applications and recommends procedures. The recommendation unit can also learn work patterns using machine learning algorithms and data mining. For example, the recommendation unit analyzes the user's work patterns and makes recommendations at the appropriate time. This makes it possible to make predictive recommendations based on the user's work patterns. For example, the user can obtain the information they need in advance.

[0044] The recommendation department can share usage history with other employees and find the optimal method for the entire team. For example, the generative AI can share an employee's usage history with other employees and find the optimal method for the entire team. For example, the procedure for applying for a business trip can be shared with team members and opinions can be gathered. The recommendation department can also share usage history within a project team, department team, etc. For example, the recommendation department can share usage history using shared documents and collaboration tools. This makes it possible to find the optimal method for the entire team. For example, the optimal procedure can be determined based on the opinions of team members.

[0045] The recommendation unit can provide information that is useful for improving a user's skills based on usage history. For example, the recommendation unit uses a generation AI to provide information that is useful for improving skills based on an employee's usage history. For example, it can recommend related training based on the content of training that has been taken in the past. The recommendation unit can also provide information on improving skills such as training programs and obtaining qualifications. For example, the recommendation unit analyzes information that is useful for improving a user's skills and makes recommendations at the appropriate time. This makes it possible to provide information that is useful for improving a user's skills. For example, the user can efficiently acquire the skills they need.

[0046] The recommendation department can analyze internal company communication history and suggest the most appropriate communication method. For example, the recommendation department uses a generation AI to analyze internal company communication history and suggest the most appropriate communication method. For example, it can recommend effective communication methods based on past success stories. The recommendation department can also analyze communication history based on email history and chat logs. For example, the recommendation department can suggest appropriate communication methods based on communication history. This makes it possible to suggest the most appropriate communication method. For example, it can support smooth communication between employees.

[0047] The Recommendation Department can utilize internal communications to share information between different departments, strengthening collaboration between them. For example, the Recommendation Department uses generative AI to analyze internal communications and promote information sharing between different departments. For example, it shares the progress of a project with other departments. The Recommendation Department can also share information between departments using shared documents and collaboration tools. For example, the Recommendation Department can share project progress information and strengthen collaboration between departments. This can strengthen collaboration between departments. For example, different departments can work together to smoothly progress a project.

[0048] The recommendation unit can automatically reflect the content of communication in the project management tool and visualize the progress of tasks. For example, the generation AI in the recommendation unit automatically reflects the content of communication in the project management tool and visualizes the progress of tasks. For example, it reflects meeting minutes in the project management tool. The recommendation unit can also visualize the progress of tasks using project management tools such as Trello and JIRA. For example, the recommendation unit updates the progress of tasks based on the content of communication. This makes it possible to visualize the progress of tasks. For example, it is possible to grasp the progress of a project in real time.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The recommendation system can further include a health management unit that monitors the user's health condition. For example, the health management unit measures the user's heart rate and stress level and adjusts the recommendations based on the user's health condition. If the user feels tired, the health management unit can recommend taking a break. The health management unit can also analyze the user's exercise history and food records to provide recommendations that support healthy lifestyle habits. This makes it possible to make recommendations based on the user's health condition. For example, the health management unit can provide information to help the user maintain a healthy lifestyle.

[0051] The recommendation system can further include a learning management unit that analyzes the user's learning history. For example, the learning management unit can analyze the training and learning content that the user has taken in the past and recommend new related learning opportunities. If the user wants to improve a specific skill, it can suggest training and courses related to that skill. The learning management unit can also monitor the user's learning progress and provide learning resources at the appropriate time. This makes it possible to make recommendations according to the user's learning needs. For example, it can provide information to help the user acquire skills efficiently.

[0052] The recommendation system can further include a project management section that supports the user's project management. For example, the project management section analyzes the user's project progress and recommends the next task to be performed. If the user is having difficulty progressing with the project, it can suggest appropriate resources and support. The project management section can also support efficient task management by taking into account project priorities and deadlines. This can support the user's project management to proceed smoothly. For example, it can provide information to help the user progress with the project efficiently.

[0053] Recommendation systems can also analyze a user's communication history to suggest optimal communication methods. For example, they can recommend effective communication methods based on past success stories based on communication history. If a user is having difficulty communicating in a particular project, they can suggest an appropriate approach. Furthermore, by analyzing communication history, they can make recommendations that match the user's communication style. This can support users so that their communication goes smoothly. For example, they can provide information to help users communicate effectively.

[0054] Recommendation systems can also analyze a user's schedule and make recommendations at the optimal time. For example, they can analyze a user's calendar and adjust the recommendations to coincide with important meetings or deadlines. When a user is busy, they can recommend tasks that are easy and quick to perform. Based on the schedule analysis, they can also make recommendations to improve the user's work efficiency. This makes it possible to make recommendations based on the user's schedule. For example, they can provide information to help users work more efficiently.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The search unit performs a cross-sectional search across internal tools and guide pages. For example, the search unit searches internal project management tools and document management tools to obtain relevant information. The search unit can also search operation manuals and FAQ pages to collect necessary information. Furthermore, the search unit uses full-text search and metadata search to efficiently search for information. For example, the search unit searches for task information in a project management tool to obtain related documents. Step 2: The recommendation unit recommends the optimal method from the information acquired by the search unit. For example, the recommendation unit selects the most efficient and easy-to-understand method based on the acquired information and presents it to the employee. The recommendation unit can also evaluate and recommend the optimal method based on the user's past behavioral history and work content. For example, the recommendation unit presents the optimal procedure based on the history of past business trip applications.

[0057] (Example 2) The recommendation system according to the embodiment of the present invention is a system that cross-searches various tools and guide pages scattered throughout the company and instantly recommends the optimal method for the information that employees want to know or the content that they want to apply for. As a result, the recommendation system can significantly reduce the time that employees spend on activities other than their main work.

[0058] A recommendation system according to an embodiment includes a search unit and a recommendation unit. The search unit performs a cross-sectional search across internal tools and guide pages. For example, the search unit searches an internal project management tool or document management tool to acquire relevant information. The search unit can also search operation manuals or FAQ pages to collect necessary information. The search unit efficiently searches for information using a full-text search or a metadata search. For example, the search unit searches task information in a project management tool to acquire relevant documents. The recommendation unit recommends an optimal method from the information acquired by the search unit. For example, the recommendation unit selects the most efficient and easy-to-understand method based on the acquired information and presents it to employees. The recommendation unit can also evaluate and recommend an optimal method based on a user's past behavioral history and work content. For example, the recommendation unit presents an optimal procedure based on a history of past business trip applications. This allows the recommendation system according to an embodiment to quickly and accurately respond to the information employees want to know or the content of their applications. For example, if an employee wants to know how to apply for a business trip, the recommendation system can present the optimal procedure and quickly complete the application process.In addition, since the recommendation system makes recommendations based on the latest information, it can always provide accurate information.

[0059] The search unit can analyze user input in real time and instantly update search results. For example, the search unit uses a generation AI to analyze user input in real time and instantly update search results. For example, the moment a user types "business trip application," the latest related guide pages and tools are displayed. The search unit can also analyze user input in real time using streaming data analysis. For example, the search unit uses an in-memory database to quickly update search results. This allows for a rapid response to user input. For example, even if a user changes their input, the latest search results can be provided instantly.

[0060] The search unit can analyze the metadata of internal tools and guide pages to improve the accuracy of search results. For example, the search unit uses a generation AI to analyze the metadata of internal tools and guide pages to improve the accuracy of search results. For example, it prioritizes displaying highly relevant information based on the keywords and tags of each page. The search unit can also analyze tag information and attribute data to improve the accuracy of search results. For example, the search unit filters related information based on metadata to provide highly accurate search results. This improves the accuracy of search results. For example, users can find the information they need quickly.

[0061] The search unit can use an emotion estimation function to estimate the user's emotions and customize search results based on those emotions. For example, the search unit uses a generation AI to estimate the user's emotions and customize search results based on those emotions. For example, if the user is feeling stressed, simple and easy-to-understand information can be displayed preferentially. The search unit can also estimate the user's emotions using facial expression recognition and voice analysis. For example, the search unit can capture the user's facial expression with a camera and analyze the emotion using an emotion estimation algorithm. This allows the search unit to provide search results that correspond to the user's emotions. For example, if the user is relaxed, detailed information can be provided.

[0062] The search unit can provide search results through a voice assistant, allowing the user to obtain information hands-free. For example, the search unit uses a generation AI to provide search results through a voice assistant, allowing the user to obtain information hands-free. For example, if the user asks, "How do I apply for a business trip?", the voice will guide the user through the steps. The search unit can also provide search results using a voice assistant such as Amazon Alexa or Google Assistant. For example, the search unit can analyze the user's question using voice recognition technology and provide appropriate information. This allows the user to obtain information hands-free. For example, the user can obtain information by voice even if their hands are full.

[0063] The search unit can visualize search results to enable users to intuitively understand them. For example, the search unit uses a generation AI to visualize search results to enable users to intuitively understand them. For example, the steps for applying for a business trip can be displayed as a flowchart or diagram. The search unit can also visualize search results using graph displays or dashboards. For example, the search unit can display search results as graphs or charts to enable users to visually understand the information. This allows users to intuitively understand the information. For example, visually displaying complex steps can make them easier to understand.

[0064] The search unit uses an emotion estimation function to estimate the user's emotions, detect frustration felt during the search, and provide appropriate support. For example, the search unit uses a generation AI to estimate the user's emotions and detect frustration felt during the search. For example, if the user is frustrated, the search unit will prioritize displaying simple steps. The search unit can also detect frustration based on the user's operation log and survey results. For example, the search unit can analyze the user's operation history to determine whether the user is feeling frustrated. This can reduce the user's frustration. For example, if the user is in trouble, the search unit can provide appropriate support.

[0065] The recommendation unit can learn past recommendation history and make recommendations that match the user's preferences. For example, the recommendation unit uses a generation AI to learn past recommendation history and make recommendations that match the user's preferences. For example, it presents the optimal procedure based on the history of past business trip applications. The recommendation unit can also make recommendations based on past recommendation results and user feedback. For example, the recommendation unit prioritizes recommending related information based on the user's preferences. This makes it possible to make recommendations that match the user's preferences. For example, it can prioritize providing information that the user prefers.

[0066] The recommendation unit can provide information at the optimal timing, taking into account the user's work situation. For example, the generation AI analyzes the user's work situation and provides information at the optimal timing. For example, if the user is preparing for a business trip, the recommendation unit can recommend the procedure for applying for a business trip. The recommendation unit can also make recommendations based on the progress of tasks and the priority of work. For example, the recommendation unit evaluates the user's work situation and provides information at the appropriate timing. This makes it possible to provide information at a timing that suits the user's work situation. For example, the user can obtain the information they need at the appropriate time.

[0067] The recommendation unit can estimate the user's emotions using an emotion estimation function and adjust the recommendation content based on those emotions. For example, the recommendation unit uses a generation AI to estimate the user's emotions and adjust the recommendation content based on those emotions. For example, if the user is feeling stressed, the recommendation unit can recommend simple and easy-to-understand information. The recommendation unit can also estimate the user's emotions using facial expression recognition and voice analysis. For example, the recommendation unit can capture the user's facial expression with a camera and analyze the emotion using an emotion estimation algorithm. This makes it possible to make recommendations based on the user's emotions. For example, if the user is relaxed, detailed information can be recommended.

[0068] The recommendation unit can automatically add recommended content to a calendar according to the user's schedule. For example, the recommendation unit uses a generation AI to automatically add recommended content to the user's schedule. For example, it can add the steps for applying for a business trip to a calendar and set a reminder. The recommendation unit can also use a calendar app or task management tool to reflect the recommended content in the schedule. For example, the recommendation unit can analyze the user's schedule and add recommended content at the appropriate time. This makes it possible to make recommendations that match the user's schedule. For example, the user can remember to complete a procedure.

[0069] The Recommendation Department can share its recommendations with other employees to find the optimal method for the entire team. For example, the Generative AI can share its recommendations with other employees to find the optimal method for the entire team. For example, the procedure for applying for a business trip can be shared with team members to gather their opinions. The Recommendation Department can also share its recommendations within a project team, department team, etc. For example, the Recommendation Department can share its recommendations using shared documents or collaboration tools. This makes it possible to find the optimal method for the entire team. For example, the optimal procedure can be determined based on the opinions of team members.

[0070] The search unit can automatically detect updates to internal tools and guide pages and notify users. For example, the search unit uses a generation AI to automatically detect updates to internal tools and guide pages and notify users. For example, if a procedure for applying for a business trip is changed, a notification is sent immediately. The search unit can also detect updates such as software upgrades and document revisions. For example, the search unit can send notifications to users based on the update information. This allows users to be provided with the latest information. For example, users can always act in accordance with the latest procedures.

[0071] The search unit can evaluate the importance of update information and prioritize notifications of only important updates. For example, the generation AI can evaluate the importance of update information and prioritize notifications of only important updates. For example, if there is a major change in the procedures for business trip applications, that information will be notified immediately. The search unit can also evaluate the importance of update information based on the impact on business operations and urgency. For example, the search unit prioritizes notifications of information with a high level of importance. This allows important update information to be notified preferentially. For example, users can respond without missing important information.

[0072] The search unit can use an emotion estimation function to estimate the user's emotions and provide support to reduce anxiety about the update information. For example, the search unit uses a generation AI to estimate the user's emotions and provide support to reduce anxiety about the update information. For example, it provides a detailed explanation of a procedural change. The search unit can also detect anxiety based on the user's operation log or survey results. For example, the search unit analyzes the user's operation history and determines whether the user is feeling anxious. This makes it possible to provide support to reduce the user's anxiety. For example, the user can perform the procedure with peace of mind.

[0073] The search department can automatically post updated information on an internal digital bulletin board to share with all employees. For example, the generation AI of the search department can automatically post updated information on an internal digital bulletin board to share with all employees. For example, it posts changes to procedures for business trip applications on the bulletin board. The search department can also share updated information using an internal portal site or bulletin board app. For example, the search department can post updated information on a digital bulletin board and send notifications to all employees. This allows the latest updated information to be shared with all employees. For example, all employees can act in accordance with the latest procedures.

[0074] The search unit can customize the update information to match the user's work content and provide only highly relevant information. For example, the generation AI can customize the update information to match the user's work content and provide only highly relevant information. For example, information about business trip applications can be notified preferentially to employees who travel frequently. The search unit can also customize update information based on work content such as project management or sales activities. For example, the search unit can analyze the user's work content and provide highly relevant information. This makes it possible to provide only information related to the user's work content. For example, the user can quickly obtain the information they need.

[0075] The search unit can analyze the user's emotional reactions and improve the notification method based on that data. For example, the search unit uses a generation AI to analyze the user's emotional reactions to update information and improves the notification method based on that data. For example, it prioritizes notification methods with a high number of positive reactions. The search unit can also collect the user's emotional reactions using facial recognition and voice analysis. For example, the search unit can capture the user's facial expressions with a camera and analyze their emotions using an emotion estimation algorithm. This makes it possible to improve the notification method based on the user's emotional reactions. For example, it can prioritize the notification method that the user prefers.

[0076] The recommendation unit can analyze employee usage history in detail and provide the most suitable information to each individual user. For example, the recommendation unit uses a generation AI to analyze employee usage history in detail and provide the most suitable information to each individual user. For example, it can present the most suitable procedure based on past business trip application history. The recommendation unit can also analyze usage history based on access logs and operation history. For example, the recommendation unit can prioritize recommending related information based on the user's usage history. This makes it possible to provide the most suitable information to each individual user. For example, the user can quickly obtain the information they need.

[0077] The recommendation unit can learn the user's work patterns based on usage history and make predictive recommendations. For example, the recommendation unit uses a generation AI to learn work patterns based on employee usage history and make predictive recommendations. For example, the recommendation unit predicts the timing of the next business trip application based on the frequency of business trip applications and recommends procedures. The recommendation unit can also learn work patterns using machine learning algorithms and data mining. For example, the recommendation unit analyzes the user's work patterns and makes recommendations at the appropriate time. This makes it possible to make predictive recommendations based on the user's work patterns. For example, the user can obtain the information they need in advance.

[0078] The recommendation unit can estimate the user's emotions using an emotion estimation function and provide personalized information based on those emotions. For example, the recommendation unit uses a generation AI to estimate the user's emotions and provides personalized information based on those emotions. For example, if the user is feeling stressed, the recommendation unit can recommend simple and easy-to-understand information. The recommendation unit can also estimate the user's emotions using facial expression recognition and voice analysis. For example, the recommendation unit can capture the user's facial expression with a camera and analyze the emotion using an emotion estimation algorithm. This makes it possible to provide personalized information according to the user's emotions. For example, if the user is relaxed, detailed information can be recommended.

[0079] The recommendation department can share usage history with other employees and find the optimal method for the entire team. For example, the generative AI can share an employee's usage history with other employees and find the optimal method for the entire team. For example, the procedure for applying for a business trip can be shared with team members and opinions can be gathered. The recommendation department can also share usage history within a project team, department team, etc. For example, the recommendation department can share usage history using shared documents and collaboration tools. This makes it possible to find the optimal method for the entire team. For example, the optimal procedure can be determined based on the opinions of team members.

[0080] The recommendation unit can provide information that is useful for improving a user's skills based on usage history. For example, the recommendation unit uses a generation AI to provide information that is useful for improving skills based on an employee's usage history. For example, it can recommend related training based on the content of training that has been taken in the past. The recommendation unit can also provide information on improving skills such as training programs and obtaining qualifications. For example, the recommendation unit analyzes information that is useful for improving a user's skills and makes recommendations at the appropriate time. This makes it possible to provide information that is useful for improving a user's skills. For example, the user can efficiently acquire the skills they need.

[0081] The recommendation unit can collect users' emotional reactions and improve the algorithm based on that data. For example, the recommendation unit collects users' emotional reactions to personalized information generated by the generation AI based on usage history, and improves the algorithm based on that data. For example, it prioritizes information with a high number of positive reactions. The recommendation unit can also collect users' emotional reactions using facial recognition and voice analysis. For example, the recommendation unit can capture users' facial expressions with a camera and analyze their emotions using an emotion estimation algorithm. This allows the algorithm to be improved based on the users' emotional reactions. For example, it can prioritize recommending information that the user prefers.

[0082] The recommendation department can analyze internal company communication history and suggest the most appropriate communication method. For example, the recommendation department uses a generation AI to analyze internal company communication history and suggest the most appropriate communication method. For example, it can recommend effective communication methods based on past success stories. The recommendation department can also analyze communication history based on email history and chat logs. For example, the recommendation department can suggest appropriate communication methods based on communication history. This makes it possible to suggest the most appropriate communication method. For example, it can support smooth communication between employees.

[0083] The recommendation unit can use an emotion estimation function to analyze emotions during communication and provide appropriate feedback. For example, the recommendation unit uses a generation AI to analyze emotions during communication and provide appropriate feedback. For example, if the user is feeling stressed, the recommendation unit can provide advice on how to relax. The recommendation unit can also analyze emotions during communication using facial expression recognition and voice analysis. For example, the recommendation unit can capture the user's facial expression with a camera and analyze the emotion using an emotion estimation algorithm. This makes it possible to provide appropriate feedback. For example, the user can communicate in a relaxed manner.

[0084] The Recommendation Department can utilize internal communications to share information between different departments, strengthening collaboration between them. For example, the Recommendation Department uses generative AI to analyze internal communications and promote information sharing between different departments. For example, it shares the progress of a project with other departments. The Recommendation Department can also share information between departments using shared documents and collaboration tools. For example, the Recommendation Department can share project progress information and strengthen collaboration between departments. This can strengthen collaboration between departments. For example, different departments can work together to smoothly progress a project.

[0085] The recommendation unit can automatically reflect the content of communication in the project management tool and visualize the progress of tasks. For example, the generation AI in the recommendation unit automatically reflects the content of communication in the project management tool and visualizes the progress of tasks. For example, it reflects meeting minutes in the project management tool. The recommendation unit can also visualize the progress of tasks using project management tools such as Trello and JIRA. For example, the recommendation unit updates the progress of tasks based on the content of communication. This makes it possible to visualize the progress of tasks. For example, it is possible to grasp the progress of a project in real time.

[0086] The recommendation unit can use an emotion estimation function to monitor emotions during communication in real time and provide appropriate support. For example, the recommendation unit uses a generation AI to monitor emotions during communication in real time and provide appropriate support. For example, if a user is feeling stressed, it can provide advice on how to relax. The recommendation unit can also monitor emotions using streaming data analysis and a real-time dashboard. For example, the recommendation unit can capture the user's facial expressions with a camera and analyze emotions in real time using an emotion estimation algorithm. This makes it possible to monitor emotions in real time and provide appropriate support. For example, the user can communicate in a relaxed manner.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The recommendation system can further include a health management unit that monitors the user's health condition. For example, the health management unit measures the user's heart rate and stress level and adjusts the recommendations based on the user's health condition. If the user feels tired, the health management unit can recommend taking a break. The health management unit can also analyze the user's exercise history and food records to provide recommendations that support healthy lifestyle habits. This makes it possible to make recommendations based on the user's health condition. For example, the health management unit can provide information to help the user maintain a healthy lifestyle.

[0089] The recommendation system can further include a learning management unit that analyzes the user's learning history. For example, the learning management unit can analyze the training and learning content that the user has taken in the past and recommend new related learning opportunities. If the user wants to improve a specific skill, it can suggest training and courses related to that skill. The learning management unit can also monitor the user's learning progress and provide learning resources at the appropriate time. This makes it possible to make recommendations according to the user's learning needs. For example, it can provide information to help the user acquire skills efficiently.

[0090] The recommendation system can further include a project management section that supports the user's project management. For example, the project management section analyzes the user's project progress and recommends the next task to be performed. If the user is having difficulty progressing with the project, it can suggest appropriate resources and support. The project management section can also support efficient task management by taking into account project priorities and deadlines. This can support the user's project management to proceed smoothly. For example, it can provide information to help the user progress with the project efficiently.

[0091] The recommendation system may further include an emotion estimation unit that estimates the user's emotion and adjusts the recommendation content based on the emotion. For example, the emotion estimation unit may analyze the user's facial expression and voice to estimate the emotion. If the user is feeling stressed, the emotion estimation unit may recommend information to help the user relax. The emotion estimation unit may also make recommendations at appropriate times according to the user's emotion. This makes it possible to make recommendations according to the user's emotion. For example, if the user is relaxed, detailed information may be provided.

[0092] The recommendation system can further estimate the user's emotions and adjust the content of communication based on those emotions. For example, the emotion estimation unit analyzes the user's emotions and suggests an appropriate communication method. If the user is irritated, it can recommend communication in a calm tone. The emotion estimation unit can also provide appropriate feedback depending on the user's emotions. This enables communication that is tailored to the user's emotions. For example, if the user is relaxed, it can provide a detailed explanation.

[0093] The recommendation system can further estimate the user's emotions and adjust the learning content based on those emotions. For example, the emotion estimation unit analyzes the user's emotions and suggests appropriate learning resources. If the user is tired, it can recommend content that can be learned in a short time. The emotion estimation unit can also support the user's learning progress according to the user's emotions. This makes it possible to learn according to the user's emotions. For example, if the user is relaxed, it can provide detailed learning resources.

[0094] The recommendation system can further estimate the user's emotions and support health management based on those emotions. For example, the emotion estimation unit analyzes the user's emotions and suggests appropriate health management methods. If the user is feeling stressed, it can recommend exercise or meditation to help them relax. The emotion estimation unit can also provide health management advice based on the user's emotions. This makes it possible to manage health according to the user's emotions. For example, if the user is relaxed, it can provide detailed health management information.

[0095] The recommendation system can further estimate the user's emotions and support project management based on those emotions. For example, the emotion estimation unit analyzes the user's emotions and suggests an appropriate project management method. If the user is frustrated, it can recommend starting with an easy task. The emotion estimation unit can also support the progress of the project according to the user's emotions. This enables project management according to the user's emotions. For example, if the user is relaxed, detailed project management information can be provided.

[0096] Recommendation systems can also analyze a user's communication history to suggest optimal communication methods. For example, they can recommend effective communication methods based on past success stories based on communication history. If a user is having difficulty communicating in a particular project, they can suggest an appropriate approach. Furthermore, by analyzing communication history, they can make recommendations that match the user's communication style. This can support users so that their communication goes smoothly. For example, they can provide information to help users communicate effectively.

[0097] Recommendation systems can also analyze a user's schedule and make recommendations at the optimal time. For example, they can analyze a user's calendar and adjust the recommendations to coincide with important meetings or deadlines. When a user is busy, they can recommend tasks that are easy and quick to perform. Based on the schedule analysis, they can also make recommendations to improve the user's work efficiency. This makes it possible to make recommendations based on the user's schedule. For example, they can provide information to help users work more efficiently.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The search unit performs a cross-sectional search across internal tools and guide pages. For example, the search unit searches internal project management tools and document management tools to obtain relevant information. The search unit can also search operation manuals and FAQ pages to collect necessary information. Furthermore, the search unit uses full-text search and metadata search to efficiently search for information. For example, the search unit searches for task information in a project management tool to obtain related documents. Step 2: The recommendation unit recommends the optimal method from the information acquired by the search unit. For example, the recommendation unit selects the most efficient and easy-to-understand method based on the acquired information and presents it to the employee. The recommendation unit can also evaluate and recommend the optimal method based on the user's past behavioral history and work content. For example, the recommendation unit presents the optimal procedure based on the history of past business trip applications.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 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 search section that searches across internal tools and guide pages, a recommendation unit that recommends an optimal method from the information acquired by the search unit. A system characterized by:

2. The search unit Inferring user sentiment and customizing search results based on said sentiment 2. The system of claim 1.

3. The search unit Provide search results through voice assistants, allowing users to retrieve information hands-free 2. The system of claim 1.

4. The recommendation unit Learns past recommendation history and makes recommendations that match the user's preferences 2. The system of claim 1.

5. The search unit Automatically detects updates to the internal tools and guide pages and notifies users 2. The system of claim 1.

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