System

Abstract

An object of a system according to an embodiment is to easily access in-house accounting information assets and provide necessary information in a timely manner.SOLUTION: A system includes an information acquisition unit, an analysis unit, and a provision unit. The information acquisition unit accesses an accounting information asset. The analysis unit analyzes the information acquired by the information acquisition unit. The providing unit provides the information analyzed by the analysis 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 access internal accounting information assets and provide necessary information in a timely manner.

[0005] The system according to the embodiment aims to provide easy access to in-house accounting information assets and to provide necessary information in a timely manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an information acquisition unit, an analysis unit, and a provision unit. The information acquisition unit accesses accounting information assets. The analysis unit analyzes the information acquired by the information acquisition unit. The provision unit provides the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily access accounting information assets within a company and provide necessary information in a timely manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The accounting processing assistance generation AI system according to an embodiment of the present invention is a system that allows easy access to internal accounting information assets. This system uses SmartAI-Chat to provide information about internal accounting processing to those who need it at the right time. As a result, the accounting processing assistance generation AI system can improve employee productivity and significantly reduce man-hours.

[0029] The accounting process help generation AI system according to the embodiment includes an information acquisition unit, an analysis unit, and a provision unit. The information acquisition unit accesses accounting information assets. For example, the information acquisition unit connects to an internal accounting database to acquire necessary information. The information acquisition unit can also acquire data from external accounting software via an API. The information acquisition unit can also access accounting data stored in cloud storage. For example, the information acquisition unit sends a query to an internal accounting database to acquire necessary data. When acquiring data from external accounting software via an API, the information acquisition unit authenticates using an API key and acquires the necessary data. When accessing accounting data stored in cloud storage, the information acquisition unit acquires the data using the cloud storage's API. The analysis unit analyzes the information acquired by the information acquisition unit. For example, the analysis unit analyzes a user request using natural language processing technology to identify the necessary information. The analysis unit can also predict optimal information based on the user's past search history. The analysis unit can also analyze the user's emotional state using an emotion estimation function. For example, the analysis unit analyzes a user's request using natural language processing technology to identify the required information. When predicting optimal information based on the user's past search history, the analysis unit analyzes past search queries and identifies frequently searched information. When analyzing a user's emotional state using an emotion estimation function, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The provision unit provides the information analyzed by the analysis unit. For example, the provision unit displays required information on a dashboard in response to a user's request. The provision unit can also send a reminder to notify the user of the required information. The provision unit can also provide information through a mobile device or a wearable device. For example, the provision unit displays required information on a dashboard in response to a user's request. When sending a reminder to notify the user of the required information, the provision unit analyzes the user's schedule and sends the reminder at an appropriate time. When providing information through a mobile device or a wearable device, the provision unit sends a notification to a smartphone or a smartwatch.As a result, the accounting processing assistance generation AI system according to the embodiment enables quick access to accounting information assets and the provision of information. For example, employees can quickly obtain accounting data, improving work efficiency. It also significantly reduces the time that accounting staff spends manually searching and organizing data. Furthermore, employees in other departments can easily obtain the accounting information they need, allowing work to proceed smoothly.

[0030] The analysis unit can predict the most appropriate information based on the user's past search history and provide it via the provision unit. For example, the analysis unit uses a generation AI to analyze the user's past search history and prioritize providing frequently accessed information. For example, expense reports and monthly financial statement data that have been searched for many times in the past can be instantly displayed. The analysis unit also predicts related information based on the user's search history and caches the necessary data in advance. For example, it prepares the data needed for month-end closing processing in advance. The analysis unit also uses the search history to learn patterns of information the user has accessed in the past and suggests the most appropriate information for the next search. For example, it can prioritize displaying accounting data related to a specific project. This makes it possible to provide the most appropriate information based on the user's past search history.

[0031] The analysis unit can respond to complex queries using natural language processing and provide detailed information. For example, the generation AI in the analysis unit uses natural language processing technology to analyze complex user queries and provide appropriate accounting information. For example, it responds to queries such as, "Please tell me how last year's sales and expenses compare." The analysis unit also uses natural language processing to understand the intent of the user's question and integrate and provide relevant information. For example, it responds to requests such as, "Please compare this month's expense report with last month's report." In addition, the generation AI in the analysis unit automatically extracts relevant data for complex queries and generates detailed reports. For example, it responds to requests such as, "Please summarize all expense data related to a specific project." This makes it possible to respond to complex queries using natural language processing.

[0032] The information acquisition unit can access accounting information assets using at least one interface of voice input or gesture input. The information acquisition unit, for example, uses voice input to enable a user to easily search for accounting information. For example, by simply issuing a voice command such as "Show me this month's expense report," the generation AI displays the relevant data. The information acquisition unit also uses gesture input to enable a user to intuitively access accounting information. For example, by performing a specific gesture, the generation AI displays related data. The information acquisition unit also integrates various interfaces to enable a user to access accounting information in the most user-friendly way. For example, by combining voice input and gesture input, more intuitive operation is achieved. This allows access to accounting information assets using various interfaces.

[0033] The information acquisition unit can also link with information assets from different departments, integrating accounting information with information from other departments and providing it in an integrated manner. For example, the information acquisition unit can integrate accounting information with information from other departments, building a system in which the generation AI can provide it in a unified manner. For example, accounting data can be integrated with sales data, allowing the relationship between sales and expenses to be understood at a glance. The information acquisition unit can also link information assets from different departments, allowing the generation AI to automatically extract and provide the necessary information. For example, it can integrate project management data with accounting data to perform a detailed analysis of expenses for each project. The information acquisition unit can also strengthen collaboration between departments by providing accounting information and information from other departments in an integrated manner. For example, it can integrate data from the marketing and accounting departments to evaluate the cost-effectiveness of a campaign. This allows accounting information and information from other departments to be provided in an integrated manner.

[0034] The provision unit can analyze the user's schedule and automatically send reminders at the necessary times. For example, the generation AI in the provision unit analyzes the user's schedule and automatically sends reminders for important accounting tasks. For example, it sets reminders for month-end closing processing and quarterly settlement. The provision unit also links with the user's calendar so that the generation AI reminds them to provide accounting data when necessary. For example, it notifies them of the documents they need the day before an accounting audit. The provision unit also allows the generation AI to learn the user's schedule and send reminders at the optimal times. For example, it sends reminders that avoid times when the user is busy. This allows reminders to be sent based on the user's schedule.

[0035] The provision unit can analyze past data and predict the optimal timing to provide information. For example, the generation AI in the provision unit analyzes past data and predicts the timing when the user will need information most, and provides the information. For example, the generation AI may notify the user in advance of the data required for closing the month-end. The provision unit also has the generation AI learn the user's work patterns based on past data and provide information at the optimal timing. For example, the generation AI may automatically send the necessary documents the day before an accounting audit. The provision unit also has the generation AI analyze past data and predict the timing when the user can most efficiently use information. For example, the generation AI may notify the user of important accounting data during times when the user is relaxing. This allows information to be provided at the optimal timing based on past data.

[0036] The providing unit can provide information at the required timing through a mobile device or a wearable device. For example, the generating AI in the providing unit provides the necessary accounting information to the user through a mobile device. For example, the providing unit sends a notification of accounting data to a smartphone. The generating AI also uses a wearable device to provide the necessary information to the user. For example, the providing unit displays a reminder of accounting data on a smartwatch. The providing unit also links the mobile device and the wearable device, allowing the generating AI to provide information at the optimal timing. For example, the providing unit sends notifications to a smartphone and a smartwatch simultaneously. This allows information to be provided through a mobile device or a wearable device.

[0037] The provision unit can work in conjunction with other business applications to provide the necessary information in a centralized manner. For example, the generation AI in the provision unit works in conjunction with other business applications to provide the necessary accounting information in a centralized manner. For example, it works in conjunction with a project management tool to provide expense data for each project. The provision unit also works in conjunction with other business applications, allowing the generation AI to automatically extract and provide the necessary information. For example, it works in conjunction with a CRM system to provide expense data for each customer. The provision unit also integrates business applications, building a system in which the generation AI provides information in a centralized manner. For example, it works in conjunction with an ERP system to provide company-wide accounting data. This allows the generation AI to work in conjunction with other business applications to provide information in a centralized manner.

[0038] The analysis unit analyzes existing accounting databases and can automatically correct duplicate data and inconsistencies. For example, the generation AI analyzes existing accounting databases and automatically detects and corrects duplicate data. For example, if the same expense item is registered multiple times, the duplicate data is merged. The analysis unit also builds a system in which the generation AI automatically corrects inconsistencies within the database. For example, it unifies data entered in different formats. The analysis unit also allows the generation AI to periodically analyze the database and maintain data quality. For example, it automatically deletes and corrects old or inaccurate data. This makes it possible to automatically correct duplicate data and inconsistencies in existing accounting databases.

[0039] The analysis unit automatically generates metadata for existing information assets, improving the searchability of information. For example, the generation AI in the analysis unit analyzes an existing accounting database and automatically generates metadata for each data item. For example, the project name and person in charge name related to an expense report are added as metadata. The analysis unit also builds a system in which the generation AI automatically generates metadata and improves the searchability of information. For example, keywords and tags related to accounting data are automatically added. The analysis unit also continuously improves the searchability of information by having the generation AI periodically analyze the database and add new metadata. For example, when a new project is added, metadata is automatically added to the related accounting data. This allows metadata to be automatically generated for existing information assets, improving the searchability of information.

[0040] The information acquisition unit can link existing information assets with cloud storage to enable remote access. For example, the information acquisition unit can link an existing accounting database with cloud storage to enable users to access it remotely. For example, accounting data can be stored on the cloud and accessed from anywhere. The information acquisition unit can also use cloud storage to build a system that automatically acquires and provides the accounting information required by the generation AI. For example, it can synchronize data on the cloud in real time to provide the latest information. The information acquisition unit can also enable remote access, allowing users to access accounting information from anywhere. For example, it can allow users to check accounting data while on a business trip or working from home. This allows existing information assets to be linked with cloud storage to enable remote access.

[0041] The information acquisition unit can also link with other companies' information assets to provide benchmark data for the entire industry. For example, the information acquisition unit can link with other companies' accounting data to build a system in which the generation AI provides benchmark data for the entire industry. For example, the information acquisition unit can compare and analyze a company's expenses based on the expense data of competitors. The information acquisition unit also automatically collects and analyzes benchmark data for the entire industry using the generation AI and provides it to users. For example, it can display industry average sales and expense ratios. The information acquisition unit also links with other companies' information assets, allowing the generation AI to provide industry-wide trends and best practices. For example, it can make suggestions for improving a company's accounting operations based on success stories from competitors. This allows the information acquisition unit to link with other companies' information assets and provide benchmark data for the entire industry.

[0042] The analysis unit learns employees' work patterns and can propose optimal work flows. For example, the analysis unit constructs a system in which a generation AI learns employees' work patterns and proposes optimal work flows. For example, it proposes efficient procedures for entering and organizing accounting data. The analysis unit also analyzes work patterns and the generation AI automatically optimizes work flows. For example, it sets priorities for accounting work and proposes an efficient work order. The analysis unit also uses a generation AI to learn employees' work patterns, identify bottlenecks in work, and propose improvements. For example, it proposes automation tools to prevent delays in data entry. This makes it possible to learn employees' work patterns and propose optimal work flows.

[0043] The analysis unit can analyze employee tasks and automatically set priorities. For example, the analysis unit constructs a system in which a generation AI analyzes employee tasks and automatically sets priorities. For example, it suggests that important accounting tasks be given priority. The analysis unit also improves employee productivity by setting task priorities. For example, it can process highly urgent tasks first, ensuring efficient work progress. The analysis unit also enables a generation AI to analyze employee workloads and set optimal task priorities. For example, it suggests that important tasks be given priority during peak work hours. This makes it possible to analyze employee tasks and automatically set priorities.

[0044] The provision department can link with other business tools to perform integrated business management. For example, the provision department builds a system in which the generation AI links with other business tools to perform integrated business management. For example, it links with a project management tool to centrally manage task progress. The provision department also links with other business tools, allowing the generation AI to automatically extract and provide the information it needs. For example, it links with a CRM system to provide expense data for each customer. The provision department also integrates business tools to build a system in which the generation AI provides information centrally. For example, it links with an ERP system to provide company-wide accounting data. This allows it to link with other business tools to perform integrated business management.

[0045] The provision unit can visualize employee productivity data in real time, allowing managers to provide appropriate guidance. For example, the provision unit builds a system in which the generation AI visualizes employee productivity data in real time. For example, it displays each employee's work progress and task completion status in a graph. The provision unit also visualizes the productivity data in real time, allowing managers to provide appropriate guidance. For example, it issues an alert if a work delay occurs. The provision unit also uses the generation AI to analyze employee productivity data and provide managers with specific guidance points. For example, it identifies bottlenecks in specific tasks and makes improvement suggestions. This makes it possible to visualize employee productivity data in real time, allowing managers to provide appropriate guidance.

[0046] The analysis unit can work with RPA to further automate accounting tasks. For example, the analysis unit's generation AI works with RPA to build a system that automates accounting tasks. For example, it automates expense settlement and invoice processing. The analysis unit also uses RPA to have the generation AI automate the input and organization of accounting data. For example, it automatically inputs expense data and stores it in a database. The analysis unit also works with the generation AI and RPA to continuously automate accounting tasks. For example, when a new accounting task arises, it adds an automation process. This allows the analysis unit to work with RPA to further automate accounting tasks.

[0047] The analysis unit can verify data in real time to prevent input errors in accounting data. For example, the analysis unit builds a system in which the generation AI verifies input errors in accounting data in real time. For example, it checks whether the input data is in the correct format. The analysis unit also verifies data in real time to prevent input errors. For example, it issues an alert if an abnormal value or inconsistency is detected. The analysis unit also builds a system in which the generation AI automatically corrects input errors in accounting data. For example, it suggests correct data if incorrect data is entered. This makes it possible to verify data in real time to prevent input errors in accounting data.

[0048] The analysis department can automate tasks in other departments as well, improving company-wide efficiency. For example, the analysis department builds a system in which the generative AI automates tasks in other departments, improving company-wide efficiency. For example, it automates data entry in the sales department and report creation in the marketing department. The analysis department also automates tasks in other departments, reducing labor costs across the company. For example, it automates tasks in project management and human resources management. The analysis department also builds processes in which the generative AI can advance company-wide automation of tasks. For example, it analyzes the work flow of each department and identifies points for automation. This allows tasks in other departments to be automated as well, improving company-wide efficiency.

[0049] The provision department can monitor the progress of accounting work in real time and automatically issue an alert if a delay occurs. For example, the provision department builds a system in which the generation AI monitors the progress of accounting work in real time and automatically issues an alert if a delay occurs. For example, it monitors the progress of monthly closings and notifies the system when a delay occurs. The provision department also monitors the progress of accounting work in real time and issues an alert if a delay occurs. For example, it notifies the system when an important accounting task is not completed by the deadline. The provision department also builds a system in which the generation AI analyzes the progress of accounting work and issues a preventative alert before a delay occurs. For example, it identifies bottlenecks in the work and proposes countermeasures in advance. This makes it possible to monitor the progress of accounting work in real time and automatically issue an alert if a delay occurs.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The information acquisition unit accesses accounting information assets. For example, the information acquisition unit connects to an internal accounting database to acquire the required information. The information acquisition unit can also acquire data from external accounting software through an API. It can also access accounting data stored in cloud storage. Specifically, the information acquisition unit sends a query to the internal accounting database to acquire the required data. When acquiring data from external accounting software through an API, the information acquisition unit authenticates using an API key and acquires the required data. When accessing accounting data stored in cloud storage, the information acquisition unit acquires the data using the cloud storage's API. Step 2: The analysis unit analyzes the information acquired by the information acquisition unit. For example, the analysis unit analyzes the user's request using natural language processing technology to identify the necessary information. The analysis unit can also predict the most suitable information based on the user's past search history. Furthermore, the analysis unit can analyze the user's emotional state using an emotion estimation function. Specifically, the analysis unit analyzes the user's request using natural language processing technology to identify the necessary information. When predicting the most suitable information based on the user's past search history, the analysis unit analyzes past search queries and identifies frequently searched information. When analyzing the user's emotional state using the emotion estimation function, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. Step 3: The providing unit provides the information analyzed by the analyzing unit. For example, the providing unit displays the necessary information on a dashboard in response to a user request. The providing unit can also send a reminder to notify the user of the necessary information. Furthermore, the providing unit can provide information through a mobile device or a wearable device. Specifically, the providing unit displays the necessary information on a dashboard in response to a user request. When sending a reminder to notify the user of the necessary information, the providing unit analyzes the user's schedule and sends the reminder at an appropriate time. When providing information through a mobile device or a wearable device, the providing unit sends a notification to a smartphone or a smartwatch.

[0053] (Example 2) The accounting processing assistance generation AI system according to an embodiment of the present invention is a system that allows easy access to internal accounting information assets. This system uses SmartAI-Chat to provide information about internal accounting processing to those who need it at the right time. As a result, the accounting processing assistance generation AI system can improve employee productivity and significantly reduce man-hours.

[0054] The accounting process help generation AI system according to the embodiment includes an information acquisition unit, an analysis unit, and a provision unit. The information acquisition unit accesses accounting information assets. For example, the information acquisition unit connects to an internal accounting database to acquire necessary information. The information acquisition unit can also acquire data from external accounting software via an API. The information acquisition unit can also access accounting data stored in cloud storage. For example, the information acquisition unit sends a query to an internal accounting database to acquire necessary data. When acquiring data from external accounting software via an API, the information acquisition unit authenticates using an API key and acquires the necessary data. When accessing accounting data stored in cloud storage, the information acquisition unit acquires the data using the cloud storage's API. The analysis unit analyzes the information acquired by the information acquisition unit. For example, the analysis unit analyzes a user request using natural language processing technology to identify the necessary information. The analysis unit can also predict optimal information based on the user's past search history. The analysis unit can also analyze the user's emotional state using an emotion estimation function. For example, the analysis unit analyzes a user's request using natural language processing technology to identify the required information. When predicting optimal information based on the user's past search history, the analysis unit analyzes past search queries and identifies frequently searched information. When analyzing a user's emotional state using an emotion estimation function, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The provision unit provides the information analyzed by the analysis unit. For example, the provision unit displays required information on a dashboard in response to a user's request. The provision unit can also send a reminder to notify the user of the required information. The provision unit can also provide information through a mobile device or a wearable device. For example, the provision unit displays required information on a dashboard in response to a user's request. When sending a reminder to notify the user of the required information, the provision unit analyzes the user's schedule and sends the reminder at an appropriate time. When providing information through a mobile device or a wearable device, the provision unit sends a notification to a smartphone or a smartwatch.As a result, the accounting processing assistance generation AI system according to the embodiment enables quick access to accounting information assets and the provision of information. For example, employees can quickly obtain accounting data, improving work efficiency. It also significantly reduces the time that accounting staff spends manually searching and organizing data. Furthermore, employees in other departments can easily obtain the accounting information they need, allowing work to proceed smoothly.

[0055] The analysis unit can predict the most appropriate information based on the user's past search history and provide it via the provision unit. For example, the analysis unit uses a generation AI to analyze the user's past search history and prioritize providing frequently accessed information. For example, expense reports and monthly financial statement data that have been searched for many times in the past can be instantly displayed. The analysis unit also predicts related information based on the user's search history and caches the necessary data in advance. For example, it prepares the data needed for month-end closing processing in advance. The analysis unit also uses the search history to learn patterns of information the user has accessed in the past and suggests the most appropriate information for the next search. For example, it can prioritize displaying accounting data related to a specific project. This makes it possible to provide the most appropriate information based on the user's past search history.

[0056] The analysis unit can respond to complex queries using natural language processing and provide detailed information. For example, the generation AI in the analysis unit uses natural language processing technology to analyze complex user queries and provide appropriate accounting information. For example, it responds to queries such as, "Please tell me how last year's sales and expenses compare." The analysis unit also uses natural language processing to understand the intent of the user's question and integrate and provide relevant information. For example, it responds to requests such as, "Please compare this month's expense report with last month's report." In addition, the generation AI in the analysis unit automatically extracts relevant data for complex queries and generates detailed reports. For example, it responds to requests such as, "Please summarize all expense data related to a specific project." This makes it possible to respond to complex queries using natural language processing.

[0057] The analysis unit uses the emotion estimation function to provide information according to the user's emotional state, and the provision unit can reduce stress. For example, when the user is feeling stressed, the analysis unit uses the emotion estimation function to have the generation AI provide concise and easy-to-understand information. For example, complex data is displayed in simple graphs and charts. The analysis unit also analyzes the user's emotional state in real time, and when stress is high, the generation AI provides messages and advice to help the user relax. For example, it suggests, "Take a short break and try again." The analysis unit also uses the emotion estimation function to provide important accounting information when the user is relaxing. For example, it notifies the user of monthly financial statement data during a time when the user is relaxing. This makes it possible to provide information according to the user's emotional state, thereby reducing stress.

[0058] The information acquisition unit can access accounting information assets using at least one interface of voice input or gesture input. The information acquisition unit, for example, uses voice input to enable a user to easily search for accounting information. For example, by simply issuing a voice command such as "Show me this month's expense report," the generation AI displays the relevant data. The information acquisition unit also uses gesture input to enable a user to intuitively access accounting information. For example, by performing a specific gesture, the generation AI displays related data. The information acquisition unit also integrates various interfaces to enable a user to access accounting information in the most user-friendly way. For example, by combining voice input and gesture input, more intuitive operation is achieved. This allows access to accounting information assets using various interfaces.

[0059] The information acquisition unit can also link with information assets from different departments, integrating accounting information with information from other departments and providing it in an integrated manner. For example, the information acquisition unit can integrate accounting information with information from other departments, building a system in which the generation AI can provide it in a unified manner. For example, accounting data can be integrated with sales data, allowing the relationship between sales and expenses to be understood at a glance. The information acquisition unit can also link information assets from different departments, allowing the generation AI to automatically extract and provide the necessary information. For example, it can integrate project management data with accounting data to perform a detailed analysis of expenses for each project. The information acquisition unit can also strengthen collaboration between departments by providing accounting information and information from other departments in an integrated manner. For example, it can integrate data from the marketing and accounting departments to evaluate the cost-effectiveness of a campaign. This allows accounting information and information from other departments to be provided in an integrated manner.

[0060] The analysis unit uses the emotion estimation function to predict the information the user will want to access, and the provision unit can prepare it in advance. For example, the analysis unit uses the emotion estimation function to predict the information the user will want to access, and the generation AI prepares it in advance. For example, if the user is feeling stressed, related data is displayed in advance for easy access. The analysis unit also analyzes the user's emotional state, and the generation AI provides the necessary information at the optimal time. For example, important accounting data is notified when the user is relaxing. The analysis unit also uses the emotion estimation function to predict the information the user will want to access, and the generation AI automatically caches the data. For example, data required for month-end closing processing is prepared in advance. This allows the information the user wants to access to be prepared in advance.

[0061] The provision unit can analyze the user's schedule and automatically send reminders at the necessary times. For example, the generation AI in the provision unit analyzes the user's schedule and automatically sends reminders for important accounting tasks. For example, it sets reminders for month-end closing processing and quarterly settlement. The provision unit also links with the user's calendar so that the generation AI reminds them to provide accounting data when necessary. For example, it notifies them of the documents they need the day before an accounting audit. The provision unit also allows the generation AI to learn the user's schedule and send reminders at the optimal times. For example, it sends reminders that avoid times when the user is busy. This allows reminders to be sent based on the user's schedule.

[0062] The provision unit can analyze past data and predict the optimal timing to provide information. For example, the generation AI in the provision unit analyzes past data and predicts the timing when the user will need information most, and provides the information. For example, the generation AI may notify the user in advance of the data required for closing the month-end. The provision unit also has the generation AI learn the user's work patterns based on past data and provide information at the optimal timing. For example, the generation AI may automatically send the necessary documents the day before an accounting audit. The provision unit also has the generation AI analyze past data and predict the timing when the user can most efficiently use information. For example, the generation AI may notify the user of important accounting data during times when the user is relaxing. This allows information to be provided at the optimal timing based on past data.

[0063] The providing unit uses the emotion estimation function to provide information at a timing appropriate to the user's emotional state, thereby improving work efficiency. The providing unit, for example, uses the emotion estimation function to provide information at a timing appropriate to the user's emotional state. For example, important accounting data is notified during a time period when the user is relaxed. The providing unit also analyzes the user's emotional state in real time and provides information at a time when stress is low. For example, monthly financial statement data is notified during a time period when the user is relaxed. The providing unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data is notified during a time period when the user is relaxed. This allows information to be provided at a timing appropriate to the user's emotional state, thereby improving work efficiency.

[0064] The providing unit can provide information at the required timing through a mobile device or a wearable device. For example, the generating AI in the providing unit provides the necessary accounting information to the user through a mobile device. For example, the providing unit sends a notification of accounting data to a smartphone. The generating AI also uses a wearable device to provide the necessary information to the user. For example, the providing unit displays a reminder of accounting data on a smartwatch. The providing unit also links the mobile device and the wearable device, allowing the generating AI to provide information at the optimal timing. For example, the providing unit sends notifications to a smartphone and a smartwatch simultaneously. This allows information to be provided through a mobile device or a wearable device.

[0065] The provision unit can work in conjunction with other business applications to provide the necessary information in a centralized manner. For example, the generation AI in the provision unit works in conjunction with other business applications to provide the necessary accounting information in a centralized manner. For example, it works in conjunction with a project management tool to provide expense data for each project. The provision unit also works in conjunction with other business applications, allowing the generation AI to automatically extract and provide the necessary information. For example, it works in conjunction with a CRM system to provide expense data for each customer. The provision unit also integrates business applications, building a system in which the generation AI provides information in a centralized manner. For example, it works in conjunction with an ERP system to provide company-wide accounting data. This allows the generation AI to work in conjunction with other business applications to provide information in a centralized manner.

[0066] The providing unit can use the emotion estimation function to provide information at the time when the user is most relaxed. The providing unit, for example, uses the emotion estimation function to provide information at the time when the user is most relaxed. For example, important accounting data is notified during the time period when the user is relaxed. The providing unit also analyzes the user's emotional state in real time and provides information at the time when the user is relaxed. For example, monthly financial statement data is notified during the time period when the user is relaxed. The providing unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data is notified during the time period when the user is relaxed. This allows information to be provided at the time when the user is most relaxed.

[0067] The analysis unit analyzes existing accounting databases and can automatically correct duplicate data and inconsistencies. For example, the generation AI analyzes existing accounting databases and automatically detects and corrects duplicate data. For example, if the same expense item is registered multiple times, the duplicate data is merged. The analysis unit also builds a system in which the generation AI automatically corrects inconsistencies within the database. For example, it unifies data entered in different formats. The analysis unit also allows the generation AI to periodically analyze the database and maintain data quality. For example, it automatically deletes and corrects old or inaccurate data. This makes it possible to automatically correct duplicate data and inconsistencies in existing accounting databases.

[0068] The analysis unit automatically generates metadata for existing information assets, improving the searchability of information. For example, the generation AI in the analysis unit analyzes an existing accounting database and automatically generates metadata for each data item. For example, the project name and person in charge name related to an expense report are added as metadata. The analysis unit also builds a system in which the generation AI automatically generates metadata and improves the searchability of information. For example, keywords and tags related to accounting data are automatically added. The analysis unit also continuously improves the searchability of information by having the generation AI periodically analyze the database and add new metadata. For example, when a new project is added, metadata is automatically added to the related accounting data. This allows metadata to be automatically generated for existing information assets, improving the searchability of information.

[0069] The providing unit can use the emotion estimation function to provide information in a format that is most user-friendly for the user. The providing unit, for example, uses the emotion estimation function to provide accounting information in a format that is most user-friendly for the user. For example, if the user is feeling stressed, a concise summary is displayed. The providing unit also analyzes the user's emotional state in real time and provides information in the optimal format. For example, if the user is relaxed, a detailed report is displayed. The providing unit also uses the emotion estimation function to predict the format in which the user can most efficiently use the information, and the generation AI provides the information. For example, visual data using graphs and charts is displayed during times when the user is relaxing. This allows information to be provided in a format that is most user-friendly for the user.

[0070] The information acquisition unit can link existing information assets with cloud storage to enable remote access. For example, the information acquisition unit can link an existing accounting database with cloud storage to enable users to access it remotely. For example, accounting data can be stored on the cloud and accessed from anywhere. The information acquisition unit can also use cloud storage to build a system that automatically acquires and provides the accounting information required by the generation AI. For example, it can synchronize data on the cloud in real time to provide the latest information. The information acquisition unit can also enable remote access, allowing users to access accounting information from anywhere. For example, it can allow users to check accounting data while on a business trip or working from home. This allows existing information assets to be linked with cloud storage to enable remote access.

[0071] The information acquisition unit can also link with other companies' information assets to provide benchmark data for the entire industry. For example, the information acquisition unit can link with other companies' accounting data to build a system in which the generation AI provides benchmark data for the entire industry. For example, the information acquisition unit can compare and analyze a company's expenses based on the expense data of competitors. The information acquisition unit also automatically collects and analyzes benchmark data for the entire industry using the generation AI and provides it to users. For example, it can display industry average sales and expense ratios. The information acquisition unit also links with other companies' information assets, allowing the generation AI to provide industry-wide trends and best practices. For example, it can make suggestions for improving a company's accounting operations based on success stories from competitors. This allows the information acquisition unit to link with other companies' information assets and provide benchmark data for the entire industry.

[0072] The providing unit can use the emotion estimation function to prioritize providing information that the user is most interested in. For example, the providing unit uses the emotion estimation function to prioritize providing accounting information that the user is most interested in. For example, expense data for projects that the user is interested in is displayed preferentially. The providing unit also analyzes the user's emotional state in real time and prioritizes providing information that is of high interest to the user. For example, important accounting data is notified to the user during times when the user is relaxing. The providing unit also uses the emotion estimation function to predict the information that the user will be most interested in, and the generation AI automatically provides it. For example, data that the user has frequently accessed in the past is displayed preferentially. This allows the information that the user is most interested in to be provided preferentially.

[0073] The analysis unit learns employees' work patterns and can propose optimal work flows. For example, the analysis unit constructs a system in which a generation AI learns employees' work patterns and proposes optimal work flows. For example, it proposes efficient procedures for entering and organizing accounting data. The analysis unit also analyzes work patterns and the generation AI automatically optimizes work flows. For example, it sets priorities for accounting work and proposes an efficient work order. The analysis unit also uses a generation AI to learn employees' work patterns, identify bottlenecks in work, and propose improvements. For example, it proposes automation tools to prevent delays in data entry. This makes it possible to learn employees' work patterns and propose optimal work flows.

[0074] The analysis unit can analyze employee tasks and automatically set priorities. For example, the analysis unit constructs a system in which a generation AI analyzes employee tasks and automatically sets priorities. For example, it suggests that important accounting tasks be given priority. The analysis unit also improves employee productivity by setting task priorities. For example, it can process highly urgent tasks first, ensuring efficient work progress. The analysis unit also enables a generation AI to analyze employee workloads and set optimal task priorities. For example, it suggests that important tasks be given priority during peak work hours. This makes it possible to analyze employee tasks and automatically set priorities.

[0075] The providing unit can use the emotion estimation function to provide feedback to increase employee motivation. The providing unit, for example, uses the emotion estimation function to build a system for providing feedback to increase employee motivation. For example, it displays messages that elicit positive emotions. The providing unit also analyzes the emotional state of employees in real time and provides specific advice to increase motivation. For example, if stress is high, it makes suggestions for relaxation. The providing unit also uses the emotion estimation function to continuously provide feedback to maintain employee motivation. For example, it sends encouraging messages according to the progress of work. This makes it possible to provide feedback to increase employee motivation.

[0076] The provision department can link with other business tools to perform integrated business management. For example, the provision department builds a system in which the generation AI links with other business tools to perform integrated business management. For example, it links with a project management tool to centrally manage task progress. The provision department also links with other business tools, allowing the generation AI to automatically extract and provide the information it needs. For example, it links with a CRM system to provide expense data for each customer. The provision department also integrates business tools to build a system in which the generation AI provides information centrally. For example, it links with an ERP system to provide company-wide accounting data. This allows it to link with other business tools to perform integrated business management.

[0077] The provision unit can visualize employee productivity data in real time, allowing managers to provide appropriate guidance. For example, the provision unit builds a system in which the generation AI visualizes employee productivity data in real time. For example, it displays each employee's work progress and task completion status in a graph. The provision unit also visualizes the productivity data in real time, allowing managers to provide appropriate guidance. For example, it issues an alert if a work delay occurs. The provision unit also uses the generation AI to analyze employee productivity data and provide managers with specific guidance points. For example, it identifies bottlenecks in specific tasks and makes improvement suggestions. This makes it possible to visualize employee productivity data in real time, allowing managers to provide appropriate guidance.

[0078] The provision unit can use the emotion estimation function to monitor employees' stress levels and suggest appropriate breaks. The provision unit, for example, uses the emotion estimation function to build a system that monitors employees' stress levels in real time. For example, it suggests taking a break if stress is high. The provision unit also analyzes the employee's emotional state and makes specific suggestions for relaxation if the stress level is high. For example, it suggests taking a short break or an activity to refresh. The provision unit also uses the emotion estimation function to continuously monitor employees' stress levels and suggest breaks at appropriate times. For example, it suggests taking a short break during peak work hours. In this way, it is possible to monitor employees' stress levels and suggest appropriate breaks.

[0079] The analysis unit can work with RPA to further automate accounting tasks. For example, the analysis unit's generation AI works with RPA to build a system that automates accounting tasks. For example, it automates expense settlement and invoice processing. The analysis unit also uses RPA to have the generation AI automate the input and organization of accounting data. For example, it automatically inputs expense data and stores it in a database. The analysis unit also works with the generation AI and RPA to continuously automate accounting tasks. For example, when a new accounting task arises, it adds an automation process. This allows the analysis unit to work with RPA to further automate accounting tasks.

[0080] The analysis unit can verify data in real time to prevent input errors in accounting data. For example, the analysis unit builds a system in which the generation AI verifies input errors in accounting data in real time. For example, it checks whether the input data is in the correct format. The analysis unit also verifies data in real time to prevent input errors. For example, it issues an alert if an abnormal value or inconsistency is detected. The analysis unit also builds a system in which the generation AI automatically corrects input errors in accounting data. For example, it suggests correct data if incorrect data is entered. This makes it possible to verify data in real time to prevent input errors in accounting data.

[0081] The providing unit can use the emotion estimation function to propose work allocation to reduce the burden on employees. The providing unit, for example, uses the emotion estimation function to build a system that proposes work allocation to reduce the burden on employees. For example, it assigns less burdensome tasks to employees who are highly stressed. The providing unit also analyzes the emotional state of employees in real time and divides up work so that the burden is not concentrated. For example, it proposes dividing up specific tasks among multiple employees. The providing unit also uses the emotion estimation function to continuously propose work allocation to reduce the burden on employees. For example, it adjusts tasks so that the burden is not concentrated during peak work hours. In this way, it is possible to propose work allocation to reduce the burden on employees.

[0082] The analysis department can automate tasks in other departments as well, improving company-wide efficiency. For example, the analysis department builds a system in which the generative AI automates tasks in other departments, improving company-wide efficiency. For example, it automates data entry in the sales department and report creation in the marketing department. The analysis department also automates tasks in other departments, reducing labor costs across the company. For example, it automates tasks in project management and human resources management. The analysis department also builds processes in which the generative AI can advance company-wide automation of tasks. For example, it analyzes the work flow of each department and identifies points for automation. This allows tasks in other departments to be automated as well, improving company-wide efficiency.

[0083] The provision department can monitor the progress of accounting work in real time and automatically issue an alert if a delay occurs. For example, the provision department builds a system in which the generation AI monitors the progress of accounting work in real time and automatically issues an alert if a delay occurs. For example, it monitors the progress of monthly closings and notifies the system when a delay occurs. The provision department also monitors the progress of accounting work in real time and issues an alert if a delay occurs. For example, it notifies the system when an important accounting task is not completed by the deadline. The provision department also builds a system in which the generation AI analyzes the progress of accounting work and issues a preventative alert before a delay occurs. For example, it identifies bottlenecks in the work and proposes countermeasures in advance. This makes it possible to monitor the progress of accounting work in real time and automatically issue an alert if a delay occurs.

[0084] The provision unit can use the emotion estimation function to monitor the employee's fatigue level and suggest a break at an appropriate time. The provision unit, for example, uses the emotion estimation function to build a system that monitors the employee's fatigue level in real time. For example, if the fatigue level is high, it suggests a break. The provision unit also analyzes the employee's emotional state and, if the fatigue level is high, makes specific suggestions for relaxation. For example, it suggests a short break or an activity for refreshing. The provision unit also uses the emotion estimation function to continuously monitor the employee's fatigue level and suggest a break at an appropriate time. For example, it suggests taking a short break during peak work hours. In this way, the fatigue level of the employee can be monitored and a break can be suggested at an appropriate time.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The analysis unit can estimate the user's emotions and, based on the estimated user emotions, provide information at the time when the user is most relaxed. For example, important accounting data can be notified during the user's relaxed hours. The analysis unit also analyzes the user's emotional state in real time and provides information at the time when stress is low. For example, monthly financial statement data can be notified during the user's relaxed hours. The analysis unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data can be notified during the user's relaxed hours. This allows information to be provided at the time that suits the user's emotional state, improving work efficiency.

[0087] The analysis unit can estimate the user's emotions and, based on the estimated user emotions, provide information at the time when the user is most relaxed. For example, important accounting data can be notified during the user's relaxed hours. The analysis unit also analyzes the user's emotional state in real time and provides information at the time when stress is low. For example, monthly financial statement data can be notified during the user's relaxed hours. The analysis unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data can be notified during the user's relaxed hours. This allows information to be provided at the time that suits the user's emotional state, improving work efficiency.

[0088] The analysis unit can estimate the user's emotions and, based on the estimated user emotions, provide information at the time when the user is most relaxed. For example, important accounting data can be notified during the user's relaxed hours. The analysis unit also analyzes the user's emotional state in real time and provides information at the time when stress is low. For example, monthly financial statement data can be notified during the user's relaxed hours. The analysis unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data can be notified during the user's relaxed hours. This allows information to be provided at the time that suits the user's emotional state, improving work efficiency.

[0089] The analysis unit can estimate the user's emotions and, based on the estimated user emotions, provide information at the time when the user is most relaxed. For example, important accounting data can be notified during the user's relaxed hours. The analysis unit also analyzes the user's emotional state in real time and provides information at the time when stress is low. For example, monthly financial statement data can be notified during the user's relaxed hours. The analysis unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data can be notified during the user's relaxed hours. This allows information to be provided at the time that suits the user's emotional state, improving work efficiency.

[0090] The analysis unit can estimate the user's emotions and, based on the estimated user emotions, provide information at the time when the user is most relaxed. For example, important accounting data can be notified during the user's relaxed hours. The analysis unit also analyzes the user's emotional state in real time and provides information at the time when stress is low. For example, monthly financial statement data can be notified during the user's relaxed hours. The analysis unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data can be notified during the user's relaxed hours. This allows information to be provided at the time that suits the user's emotional state, improving work efficiency.

[0091] The analysis unit can estimate the user's emotions and, based on the estimated user emotions, provide information at the time when the user is most relaxed. For example, important accounting data can be notified during the user's relaxed hours. The analysis unit also analyzes the user's emotional state in real time and provides information at the time when stress is low. For example, monthly financial statement data can be notified during the user's relaxed hours. The analysis unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data can be notified during the user's relaxed hours. This allows information to be provided at the time that suits the user's emotional state, improving work efficiency.

[0092] The analysis unit can estimate the user's emotions and, based on the estimated user emotions, provide information at the time when the user is most relaxed. For example, important accounting data can be notified during the user's relaxed hours. The analysis unit also analyzes the user's emotional state in real time and provides information at the time when stress is low. For example, monthly financial statement data can be notified during the user's relaxed hours. The analysis unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data can be notified during the user's relaxed hours. This allows information to be provided at the time that suits the user's emotional state, improving work efficiency.

[0093] The analysis unit can estimate the user's emotions and, based on the estimated user emotions, provide information at the time when the user is most relaxed. For example, important accounting data can be notified during the user's relaxed hours. The analysis unit also analyzes the user's emotional state in real time and provides information at the time when stress is low. For example, monthly financial statement data can be notified during the user's relaxed hours. The analysis unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data can be notified during the user's relaxed hours. This allows information to be provided at the time that suits the user's emotional state, improving work efficiency.

[0094] The analysis unit can estimate the user's emotions and, based on the estimated user emotions, provide information at the time when the user is most relaxed. For example, important accounting data can be notified during the user's relaxed hours. The analysis unit also analyzes the user's emotional state in real time and provides information at the time when stress is low. For example, monthly financial statement data can be notified during the user's relaxed hours. The analysis unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data can be notified during the user's relaxed hours. This allows information to be provided at the time that suits the user's emotional state, improving work efficiency.

[0095] The analysis unit can estimate the user's emotions and, based on the estimated user emotions, provide information at the time when the user is most relaxed. For example, important accounting data can be notified during the user's relaxed hours. The analysis unit also analyzes the user's emotional state in real time and provides information at the time when stress is low. For example, monthly financial statement data can be notified during the user's relaxed hours. The analysis unit also uses the emotion estimation function to predict the time when the user can most efficiently use information, and the generation AI provides the information. For example, important accounting data can be notified during the user's relaxed hours. This allows information to be provided at the time that suits the user's emotional state, improving work efficiency.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The information acquisition unit accesses accounting information assets. For example, the information acquisition unit connects to an internal accounting database to acquire the required information. The information acquisition unit can also acquire data from external accounting software through an API. It can also access accounting data stored in cloud storage. Specifically, the information acquisition unit sends a query to the internal accounting database to acquire the required data. When acquiring data from external accounting software through an API, the information acquisition unit authenticates using an API key and acquires the required data. When accessing accounting data stored in cloud storage, the information acquisition unit acquires the data using the cloud storage's API. Step 2: The analysis unit analyzes the information acquired by the information acquisition unit. For example, the analysis unit analyzes the user's request using natural language processing technology to identify the necessary information. The analysis unit can also predict the most suitable information based on the user's past search history. Furthermore, the analysis unit can analyze the user's emotional state using an emotion estimation function. Specifically, the analysis unit analyzes the user's request using natural language processing technology to identify the necessary information. When predicting the most suitable information based on the user's past search history, the analysis unit analyzes past search queries and identifies frequently searched information. When analyzing the user's emotional state using the emotion estimation function, the analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. Step 3: The providing unit provides the information analyzed by the analyzing unit. For example, the providing unit displays the necessary information on a dashboard in response to a user request. The providing unit can also send a reminder to notify the user of the necessary information. Furthermore, the providing unit can provide information through a mobile device or a wearable device. Specifically, the providing unit displays the necessary information on a dashboard in response to a user request. When sending a reminder to notify the user of the necessary information, the providing unit analyzes the user's schedule and sends the reminder at an appropriate time. When providing information through a mobile device or a wearable device, the providing unit sends a notification to a smartphone or a smartwatch.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information acquisition unit that accesses accounting information assets; an analysis unit that analyzes the information acquired by the information acquisition unit; a providing unit that provides the information analyzed by the analyzing unit. A system characterized by:

2. The analysis unit The optimal information is predicted based on the user's past search history and provided by the providing unit.

2. The system of claim 1.

3. The analysis unit The provision unit responds to complex queries using natural language processing and provides detailed information.

2. The system of claim 1.

4. The analysis unit The information providing unit provides information according to the user's emotional state, thereby reducing stress.

2. The system of claim 1.

5. The information acquisition unit Access to accounting information assets through at least one of the following interfaces: voice input or gesture input 2. The system of claim 1.

Images