System

Abstract

An object of a system according to an embodiment is to enable a user to easily perform checkout work.SOLUTION: A system includes an interaction unit, a proposal unit, a reminder unit, and a recording unit. The interaction unit understands the situation of the user. The proposal unit proposes a handling method on the basis of the user's situation understood by the interaction unit. The reminder unit reminds the user of the procedure based on the handling method proposed by the proposal unit. The recording unit records accounting data of a user.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 accounting systems have been complex and difficult for general users to understand.

[0005] The system according to the embodiment aims to enable users to easily perform accounting tasks. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a suggestion unit, a reminder unit, and a recording unit. The dialogue unit understands the user's situation. The suggestion unit suggests a solution based on the user's situation understood by the dialogue unit. The reminder unit reminds the user of a procedure based on the solution suggested by the suggestion unit. The recording unit records the user's accounting data. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to easily perform accounting tasks. [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 AI ​​system according to the embodiment of the present invention is a system that allows users to talk to the AI ​​about the situation they are in, and suggests solutions and reminds them of procedures. This allows the AI ​​system to reduce users' accounting-related worries and improve information transparency.

[0029] The AI ​​system according to the embodiment includes a dialogue unit, a suggestion unit, a reminder unit, and a recording unit. The dialogue unit engages in dialogue to understand the user's situation. For example, when the user describes their situation, the dialogue unit analyzes the content and collects information to suggest appropriate solutions. The dialogue unit can also analyze the user's tone of voice and speaking style, grasp the user's emotional state using an emotion estimation function, and engage in appropriate dialogue. For example, if the user is nervous, the dialogue unit engages in dialogue to help the user relax. The suggestion unit suggests solutions based on the user's situation understood by the dialogue unit. For example, the suggestion unit may remind the user to prepare necessary documents as tax filing is approaching. The suggestion unit may also predict future income and expenditures based on the user's past accounting data and suggest long-term solutions. For example, the suggestion unit may suggest starting to save money now in preparation for a large future expense. The reminder unit may remind the user to take steps based on the solutions proposed by the suggestion unit. For example, the tax filing deadline is next week. The reminder may include a message such as, "Do you have all the necessary documents ready?" The reminder unit can also analyze the user's schedule and send reminders at the optimal time. For example, it can refer to the user's calendar and send a reminder the day before an important procedure. The recording unit records the user's accounting data. For example, it can make past income and expense data easily available and generate reports as needed. The recording unit can also store data records in an unalterable format using blockchain technology. For example, accounting data can be recorded on a blockchain to prevent tampering by third parties. This allows the AI ​​system according to the embodiment to reduce users' accounting-related worries and improve information transparency. For example, it can easily grasp the balance between income and expenses, allowing for smooth tax returns and other procedures. Furthermore, it can reduce the user's financial burden by preventing them from paying exorbitant fees.

[0030] The dialogue unit can refer to the user's past dialogue history and provide a dialogue customized to each individual situation. For example, the dialogue unit stores the user's past dialogue history in a database and refers to it during the current dialogue. For example, the dialogue unit conducts a dialogue based on the user's preferences and interests based on the content of previous conversations. The dialogue unit also analyzes the past dialogue history to understand the user's patterns and tendencies. For example, the dialogue unit provides pre-prepared answers to topics the user frequently asks about. The dialogue unit also conducts a dialogue customized to each individual situation based on the user's past dialogue history. For example, the dialogue unit provides specific advice to solve a problem the user previously had. This enables a more personalized dialogue based on the user's past dialogue history.

[0031] The dialogue unit analyzes the user's non-verbal communication to gain a deeper understanding. For example, the dialogue unit captures the user's facial expression with a camera and uses facial expression analysis technology to understand the user's emotional state. For example, if the user is smiling, the dialogue unit continues a positive dialogue. The dialogue unit also analyzes the user's gestures to understand the user's non-verbal communication. For example, if the user has their arms crossed, the dialogue unit determines that the user is wary and engages in a reassuring dialogue. The dialogue unit also analyzes the user's non-verbal communication and adjusts the content of the dialogue. For example, if the user is nodding, the dialogue unit determines that the user understands and proceeds to the next step. In this way, a deeper understanding can be achieved by analyzing the user's non-verbal communication.

[0032] The dialogue unit can analyze images and documents uploaded by the user during the dialogue and automatically extract accounting information. For example, the dialogue unit analyzes images of receipts and invoices uploaded by the user during the dialogue and automatically extracts accounting information. For example, it uses OCR technology to read amounts and dates. The dialogue unit also analyzes documents uploaded by the user and automatically extracts necessary accounting data. For example, it extracts deposit and withdrawal information from bank statements. The dialogue unit also analyzes images uploaded by the user during the dialogue and automatically stores the accounting information in a database. For example, it analyzes tax-related documents and extracts necessary information. This allows accounting information to be automatically extracted from images and documents uploaded by the user.

[0033] The dialogue unit can introduce a multilingual dialogue system to accommodate users from different languages ​​and cultural backgrounds. The dialogue unit, for example, introduces a multilingual dialogue system to accommodate users from different languages. For example, dialogues are conducted in multiple languages, such as English, Spanish, and Chinese. The dialogue unit also develops a dialogue system that takes cultural backgrounds into consideration to accommodate users from different cultural backgrounds. For example, dialogues are conducted that understand culturally specific expressions and customs. The dialogue unit also introduces a multilingual dialogue system to conduct dialogue in the language selected by the user. For example, if the user selects Japanese, the dialogue proceeds in Japanese. This makes it possible to accommodate users from different languages ​​and cultural backgrounds.

[0034] The suggestion unit can predict future income and expenditures based on the user's past accounting data and suggest long-term countermeasures. The suggestion unit, for example, analyzes the user's past accounting data and predicts future income and expenditures. For example, it predicts income and expenditures for the next year based on past income and expenditure patterns. The suggestion unit also suggests long-term countermeasures based on the user's past accounting data. For example, it suggests starting to save money now in preparation for large future expenses. The suggestion unit also analyzes the past accounting data, predicts future income and expenditures, and suggests specific countermeasures. For example, if there is a possibility of a decrease in income, it suggests ways to reduce expenses. In this way, it is possible to suggest future income and expenditure predictions and long-term countermeasures based on the user's past accounting data.

[0035] The suggestion unit can suggest appropriate accounting measures based on the user's life events. The suggestion unit suggests appropriate accounting measures based on the user's life events, for example. For example, for a user who is about to get married, it suggests an estimate of wedding expenses and a savings plan. The suggestion unit also customizes accounting measures taking into account the user's life events. For example, for a user who is planning to give birth, it provides an estimate of childbirth expenses and childcare expenses. The suggestion unit also suggests specific accounting measures based on the user's life events. For example, for a user who is considering changing jobs, it predicts the balance between income and expenses after the job change and suggests measures. In this way, it is possible to suggest appropriate accounting measures based on the user's life events.

[0036] The suggestion unit can visualize and present the user's proposed solution to make it easier to understand visually. For example, the suggestion unit visualizes the proposed solution to the user to make it easier to understand visually. For example, it uses graphs or charts to show the balance of income and expenditure. The suggestion unit also presents the visualized proposed solution to the user to make it easier to understand visually. For example, it uses a flowchart to show the flow of procedures. The suggestion unit also visualizes the proposed solution to make it easier for the user to understand intuitively. For example, it uses infographics to show the necessary procedures and documents. In this way, the proposed solution to the user can be visualized to make it easier to understand visually.

[0037] The suggestion unit can provide solutions specialized for different industries or occupations and customize the system to meet the needs of the user. For example, the suggestion unit provides solutions specialized for different industries or occupations and customizes the system to meet the needs of the user. For example, the suggestion unit proposes specific tax measures to a freelance user. The suggestion unit also provides solutions customized for each industry or occupation and makes suggestions to meet the needs of the user. For example, the suggestion unit proposes inventory management measures to a user in the retail industry. The suggestion unit also builds a system that provides solutions specialized for different industries or occupations and customizes the system to meet the needs of the user. For example, the suggestion unit proposes medical expense deduction measures to a user in the medical industry. This makes it possible to provide solutions specialized for different industries or occupations and customize the system to meet the needs of the user.

[0038] The reminding unit can analyze the user's schedule and provide reminders at the optimal timing. The reminding unit, for example, analyzes the user's schedule and provides reminders at the optimal timing. For example, it references the user's calendar and provides a reminder the day before an important procedure. The reminding unit also adjusts the timing of reminders based on the user's schedule. For example, it provides reminders to avoid times when the user is busy. The reminding unit also builds a system that analyzes the user's schedule and provides reminders at the optimal timing. For example, it sends reminder notifications according to the user's plans. This allows reminders to be provided at the optimal timing based on the user's schedule.

[0039] The reminding unit can customize the reminder content based on the user's past behavioral patterns. The reminding unit, for example, analyzes the user's past behavioral patterns and customizes the reminder content. For example, the reminder unit focuses on reminding users about procedures that they have tended to forget in the past. The reminding unit also individually adjusts the reminder content based on the user's past behavioral data. For example, if the user frequently forgets a specific procedure, the reminder unit will remind users about that procedure in detail. The reminding unit also analyzes the user's past behavioral patterns and builds a system that customizes the reminder content. For example, the reminder unit automatically generates optimal reminder content based on the user's behavioral history. This makes it possible to customize the reminder content based on the user's past behavioral patterns.

[0040] The reminding unit can send reminders not only via email or SMS, but also via devices such as smartwatches and smart speakers. For example, the reminding unit can send reminders not only via email or SMS, but also via smartwatches. For example, the reminding unit can send a notification to the user's smartwatch to remind them to complete a procedure. The reminding unit can also send reminders via smart speakers. For example, the reminder can be sent via voice to the user's smart speaker to prompt them to complete a procedure. The reminding unit can also build a system that sends reminders via multiple devices. For example, the reminder can be sent to a device selected by the user, such as email, SMS, smartwatch, or smart speaker. This allows reminders to be sent via multiple devices.

[0041] The reminding unit can customize the reminder content audio and visually according to the user's preferences. For example, the reminder unit customizes the reminder content audio according to the user's preferences. For example, the reminder is given in the voice of a voice actor that the user likes. The reminder unit also customizes the reminder content visually. For example, a reminder notification is created using a design or color that the user likes. The reminder unit also builds a system that customizes the reminder content audio and visually according to the user's preferences. For example, the reminder is given based on settings selected by the user. This allows the reminder content to be customized audio and visually according to the user's preferences.

[0042] The recording unit can store data records in a tamper-proof form using blockchain technology. The recording unit, for example, stores data records in a tamper-proof form using blockchain technology. For example, accounting data is recorded on a blockchain to prevent tampering by third parties. The recording unit also uses blockchain technology to ensure data transparency. For example, it allows users to check the data history at any time. The recording unit also builds a system that stores data records in a tamper-proof form using blockchain technology. For example, accounting data is recorded on a blockchain to improve reliability. This allows data records to be stored in a tamper-proof form using blockchain technology.

[0043] The recording unit can provide an interactive dashboard so that users can easily search and refer to data. The recording unit, for example, provides an interactive dashboard so that users can easily search and refer to data. For example, income and expense data can be displayed in graphs and charts. The recording unit also uses the interactive dashboard to allow users to intuitively manipulate data. For example, a filter function can be used to display data for a specific period. The recording unit also builds a system that provides an interactive dashboard so that users can easily search and refer to data. For example, the recording unit allows users to manipulate data by clicking and dragging. This makes it possible to provide an interactive dashboard so that users can easily search and refer to data.

[0044] The recording unit can link the recorded data with cloud storage to make it accessible from anywhere. The recording unit, for example, links the recorded data with cloud storage to make it accessible from anywhere. For example, as long as the user is connected to the Internet, the data can be checked anytime, anywhere. The recording unit also automatically backs up data using cloud storage. For example, the data can be periodically saved to the cloud to prevent data loss. The recording unit also builds a system that links the recorded data with cloud storage to make it accessible from anywhere. For example, the user can check the data from a smartphone or tablet. This allows the recorded data to be linked with cloud storage to make it accessible from anywhere.

[0045] The recording unit can introduce periodic audits by a third party to ensure data transparency. For example, the recording unit introduces periodic audits by a third party to ensure data transparency. For example, a third party verifies the accuracy of accounting data. The recording unit also periodically conducts audits by a third party to ensure data transparency. For example, an audit is conducted once a year to improve the reliability of the data. The recording unit also builds a system that introduces periodic audits by a third party to ensure data transparency. For example, the audit results are made public to users to increase transparency. This allows periodic audits by a third party to be introduced to ensure data transparency.

[0046] The suggestion unit can analyze past request history and develop an algorithm that detects patterns of exorbitant request fees. For example, the suggestion unit analyzes past request history and develops an algorithm that detects patterns of exorbitant request fees. For example, abnormally high fees are automatically detected. The suggestion unit also builds a system that analyzes patterns of exorbitant request fees based on the request history. For example, it calculates the going rate of fees based on past data and detects outliers. The suggestion unit also analyzes past request history and develops an algorithm that detects patterns of exorbitant request fees. For example, it displays a warning if a specific company is charging exorbitant fees. This makes it possible to develop an algorithm that analyzes past request history and detects patterns of exorbitant request fees.

[0047] The suggestion unit can present fair prices in real time when a user makes a request, allowing them to compare. The suggestion unit, for example, builds a system that presents fair prices in real time when a user makes a request, allowing them to compare. For example, it displays a list of prices from multiple companies. The suggestion unit also presents fair prices in real time, allowing the user to compare. For example, it calculates the going rate based on past data and presents it to the user. The suggestion unit also develops a system that presents fair prices in real time when a user makes a request, allowing them to compare. For example, it warns of exorbitant prices based on the going rate. This makes it possible to present fair prices in real time when a user makes a request, allowing them to compare.

[0048] The proposal unit can create a database of price rates in different regions and industries and provide it to users. The proposal unit, for example, creates a database of price rates in different regions and industries and builds a system to provide it to users. For example, it displays price rates for each region in a list. The proposal unit also creates a database of price rates in different industries and provides it to users. For example, it presents appropriate prices based on price rates for each industry. The proposal unit also creates a database of price rates in different regions and industries and makes it easy for users to access. For example, users can check price rates for each region and industry by searching. This allows price rates for different regions and industries to be created in a database and provided to users.

[0049] The suggestion unit can build a platform that allows users to easily provide feedback on fees. For example, the suggestion unit builds a platform that allows users to easily provide feedback on fees. For example, it provides a function that allows users to post ratings and comments on fees. The suggestion unit also uses the feedback platform to allow users to share their opinions on fees. For example, they can refer to the feedback of other users. The suggestion unit also develops a system that builds a platform that allows users to easily provide feedback on fees. For example, it improves the transparency of fees based on the feedback. This makes it possible to build a platform that allows users to easily provide feedback on fees.

[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 dialogue unit can refer to the user's past dialogue history and provide a dialogue customized to each individual situation. For example, the user's past dialogue history can be stored in a database and referenced during the current dialogue. For example, a dialogue tailored to the user's preferences and interests can be conducted based on the content of previous conversations. The dialogue unit can also analyze the past dialogue history to understand the user's patterns and tendencies. For example, the dialogue unit can provide pre-prepared answers to topics the user frequently asks about. The dialogue unit can also provide a dialogue customized to each individual situation based on the user's past dialogue history. For example, the dialogue unit can provide specific advice to solve a problem the user previously had. This enables a more personalized dialogue based on the user's past dialogue history.

[0052] The dialogue unit analyzes the user's non-verbal communication to gain a deeper understanding. For example, it captures the user's facial expression with a camera and uses facial expression analysis technology to understand their emotional state. For example, if the user is smiling, it continues a positive dialogue. The dialogue unit also analyzes the user's gestures to understand their non-verbal communication. For example, if the user has their arms crossed, it determines that the user is wary and engages in a reassuring dialogue. The dialogue unit also analyzes the user's non-verbal communication and adjusts the content of the dialogue. For example, if the user nods, it determines that the user understands and moves on to the next step. In this way, analyzing the user's non-verbal communication enables a deeper understanding.

[0053] The dialogue unit can analyze images and documents uploaded by the user during the dialogue and automatically extract accounting information. For example, it analyzes images of receipts and invoices uploaded by the user during the dialogue and automatically extracts accounting information. For example, it uses OCR technology to read amounts and dates. The dialogue unit also analyzes documents uploaded by the user and automatically extracts necessary accounting data. For example, it extracts deposit and withdrawal information from bank statements. The dialogue unit also analyzes images uploaded by the user during the dialogue and automatically saves the accounting information in a database. For example, it analyzes tax-related documents and extracts necessary information. This makes it possible to automatically extract accounting information from images and documents uploaded by the user.

[0054] The dialogue unit can introduce a multilingual dialogue system to accommodate users from different languages ​​and cultural backgrounds. For example, a multilingual dialogue system can be introduced to accommodate users from different languages. For example, dialogues can be conducted in multiple languages, such as English, Spanish, and Chinese. The dialogue unit also develops a dialogue system that takes cultural backgrounds into consideration to accommodate users from different cultural backgrounds. For example, dialogues can be conducted that understand culturally specific expressions and customs. The dialogue unit also introduces a multilingual dialogue system to conduct dialogues in the language selected by the user. For example, if the user selects Japanese, the dialogue can proceed in Japanese. This makes it possible to accommodate users from different languages ​​and cultural backgrounds.

[0055] The suggestion unit can predict future income and expenditures based on the user's past accounting data and suggest long-term countermeasures. For example, the suggestion unit analyzes the user's past accounting data to predict future income and expenditures. For example, it predicts income and expenditures for the next year based on past income and expenditure patterns. The suggestion unit also suggests long-term countermeasures based on the user's past accounting data. For example, it suggests starting to save money now in preparation for large future expenses. The suggestion unit also analyzes the past accounting data to predict future income and expenditures and suggest specific countermeasures. For example, if there is a possibility of a decrease in income, it suggests ways to reduce expenses. In this way, it is possible to suggest future income and expenditure predictions and long-term countermeasures based on the user's past accounting data.

[0056] The suggestion unit can suggest appropriate accounting strategies based on the user's life events. For example, the suggestion unit suggests appropriate accounting strategies based on the user's life events. For example, for a user who is about to get married, it suggests an estimate of wedding expenses and a savings plan. The suggestion unit also customizes accounting strategies taking into account the user's life events. For example, for a user who is planning to give birth, it provides an estimate of childbirth expenses and childcare expenses. The suggestion unit also suggests specific accounting strategies based on the user's life events. For example, for a user who is considering changing jobs, it predicts the balance between income and expenses after the job change and suggests strategies. In this way, it is possible to suggest appropriate accounting strategies based on the user's life events.

[0057] The suggestion unit can visualize and present the user's proposed solution to make it easier to understand visually. For example, the proposed solution to the user can be visualized to make it easier to understand visually. For example, a balance of income and expenditure can be shown using a graph or chart. The suggestion unit can also present the visualized proposed solution to the user to make it easier to understand visually. For example, a flow chart can be used to show the flow of procedures. The suggestion unit can also visualize the proposed solution to make it easier for the user to understand intuitively. For example, infographics can be used to show the necessary procedures and documents. In this way, the proposed solution to the user can be visualized to make it easier to understand visually.

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

[0059] Step 1: The dialogue unit engages in dialogue to understand the user's situation. For example, when the user describes their situation, the dialogue unit analyzes the content and collects information to suggest appropriate ways to deal with the situation. The dialogue unit can also analyze the user's tone of voice and speaking style, and use an emotion estimation function to grasp the user's emotional state and engage in appropriate dialogue. For example, if the user is nervous, the dialogue unit will engage in dialogue to help them relax. Step 2: The suggestion unit suggests solutions based on the user's situation understood by the dialogue unit. For example, if tax filing is approaching, the suggestion unit may remind the user to prepare the necessary documents. The suggestion unit can also predict future income and expenditures based on the user's past accounting data and suggest long-term solutions. For example, the suggestion unit may suggest starting to save money now in preparation for a large future expense. Step 3: The reminder section will remind the user to take action based on the solution proposed by the suggestion section. For example, the tax return deadline is next week. It will send a reminder such as, "Do you have all the necessary documents?" The reminder section can also analyze the user's schedule and send reminders at the optimal time. For example, it can refer to the user's calendar and send a reminder the day before an important procedure. Step 4: The recording unit records the user's accounting data. For example, it allows users to easily check past income and expenditure data and generate reports as needed. The recording unit can also store data records in an unalterable format using blockchain technology. For example, accounting data can be recorded on the blockchain to prevent tampering by third parties.

[0060] (Example 2) The AI ​​system according to the embodiment of the present invention is a system that allows users to talk to the AI ​​about the situation they are in, and suggests solutions and reminds them of procedures. This allows the AI ​​system to reduce users' accounting-related worries and improve information transparency.

[0061] The AI ​​system according to the embodiment includes a dialogue unit, a suggestion unit, a reminder unit, and a recording unit. The dialogue unit engages in dialogue to understand the user's situation. For example, when the user describes their situation, the dialogue unit analyzes the content and collects information to suggest appropriate solutions. The dialogue unit can also analyze the user's tone of voice and speaking style, grasp the user's emotional state using an emotion estimation function, and engage in appropriate dialogue. For example, if the user is nervous, the dialogue unit engages in dialogue to help the user relax. The suggestion unit suggests solutions based on the user's situation understood by the dialogue unit. For example, the suggestion unit may remind the user to prepare necessary documents as tax filing is approaching. The suggestion unit may also predict future income and expenditures based on the user's past accounting data and suggest long-term solutions. For example, the suggestion unit may suggest starting to save money now in preparation for a large future expense. The reminder unit may remind the user to take steps based on the solutions proposed by the suggestion unit. For example, the tax filing deadline is next week. The reminder may include a message such as, "Do you have all the necessary documents ready?" The reminder unit can also analyze the user's schedule and send reminders at the optimal time. For example, it can refer to the user's calendar and send a reminder the day before an important procedure. The recording unit records the user's accounting data. For example, it can make past income and expense data easily available and generate reports as needed. The recording unit can also store data records in an unalterable format using blockchain technology. For example, accounting data can be recorded on a blockchain to prevent tampering by third parties. This allows the AI ​​system according to the embodiment to reduce users' accounting-related worries and improve information transparency. For example, it can easily grasp the balance between income and expenses, allowing for smooth tax returns and other procedures. Furthermore, it can reduce the user's financial burden by preventing them from paying exorbitant fees.

[0062] The dialogue unit analyzes the user's tone of voice and speaking style, and uses the emotion estimation function to grasp the user's emotional state and conduct appropriate dialogue. The dialogue unit, for example, analyzes the tone, speed, and volume of the user's voice when speaking, and uses the emotion estimation function to grasp the user's emotional state in real time. For example, if the user is nervous, the dialogue unit conducts a dialogue to relax the user. The dialogue unit also analyzes the user's speaking pattern, and uses the emotion estimation function to detect signs of stress or anxiety. For example, if the user is impatient, the dialogue unit conducts a dialogue at a slower pace. The dialogue unit also analyzes the user's tone of voice and speaking style, and uses the emotion estimation function to conduct a dialogue that elicits positive emotions. For example, if the user is depressed, the dialogue unit offers words of encouragement. This makes it possible to conduct a dialogue that is appropriate for the user's emotional state.

[0063] The dialogue unit can refer to the user's past dialogue history and provide a dialogue customized to each individual situation. For example, the dialogue unit stores the user's past dialogue history in a database and refers to it during the current dialogue. For example, the dialogue unit conducts a dialogue based on the user's preferences and interests based on the content of previous conversations. The dialogue unit also analyzes the past dialogue history to understand the user's patterns and tendencies. For example, the dialogue unit provides pre-prepared answers to topics the user frequently asks about. The dialogue unit also conducts a dialogue customized to each individual situation based on the user's past dialogue history. For example, the dialogue unit provides specific advice to solve a problem the user previously had. This enables a more personalized dialogue based on the user's past dialogue history.

[0064] The dialogue unit analyzes the user's non-verbal communication to gain a deeper understanding. For example, the dialogue unit captures the user's facial expression with a camera and uses facial expression analysis technology to understand the user's emotional state. For example, if the user is smiling, the dialogue unit continues a positive dialogue. The dialogue unit also analyzes the user's gestures to understand the user's non-verbal communication. For example, if the user has their arms crossed, the dialogue unit determines that the user is wary and engages in a reassuring dialogue. The dialogue unit also analyzes the user's non-verbal communication and adjusts the content of the dialogue. For example, if the user is nodding, the dialogue unit determines that the user understands and proceeds to the next step. In this way, a deeper understanding can be achieved by analyzing the user's non-verbal communication.

[0065] The dialogue unit can analyze images and documents uploaded by the user during the dialogue and automatically extract accounting information. For example, the dialogue unit analyzes images of receipts and invoices uploaded by the user during the dialogue and automatically extracts accounting information. For example, it uses OCR technology to read amounts and dates. The dialogue unit also analyzes documents uploaded by the user and automatically extracts necessary accounting data. For example, it extracts deposit and withdrawal information from bank statements. The dialogue unit also analyzes images uploaded by the user during the dialogue and automatically stores the accounting information in a database. For example, it analyzes tax-related documents and extracts necessary information. This allows accounting information to be automatically extracted from images and documents uploaded by the user.

[0066] The dialogue unit can introduce a multilingual dialogue system to accommodate users from different languages ​​and cultural backgrounds. The dialogue unit, for example, introduces a multilingual dialogue system to accommodate users from different languages. For example, dialogues are conducted in multiple languages, such as English, Spanish, and Chinese. The dialogue unit also develops a dialogue system that takes cultural backgrounds into consideration to accommodate users from different cultural backgrounds. For example, dialogues are conducted that understand culturally specific expressions and customs. The dialogue unit also introduces a multilingual dialogue system to conduct dialogue in the language selected by the user. For example, if the user selects Japanese, the dialogue proceeds in Japanese. This makes it possible to accommodate users from different languages ​​and cultural backgrounds.

[0067] The dialogue unit can use the emotion estimation function to make relaxation suggestions to reduce the stress and anxiety the user feels during the dialogue. For example, the dialogue unit uses the emotion estimation function to detect the stress and anxiety the user feels during the dialogue and make relaxation suggestions. For example, it may suggest deep breathing or playing relaxing music. The dialogue unit also analyzes the user's emotional state in real time and engages in dialogue to reduce stress and anxiety. For example, if the user is nervous, it may offer a topic that will help the user relax. The dialogue unit also uses the emotion estimation function to suggest relaxation methods to reduce the stress and anxiety the user feels during the dialogue. For example, it may introduce meditation or stretching techniques. This makes it possible to make relaxation suggestions to reduce the user's stress and anxiety.

[0068] The suggestion unit can predict future income and expenditures based on the user's past accounting data and suggest long-term countermeasures. The suggestion unit, for example, analyzes the user's past accounting data and predicts future income and expenditures. For example, it predicts income and expenditures for the next year based on past income and expenditure patterns. The suggestion unit also suggests long-term countermeasures based on the user's past accounting data. For example, it suggests starting to save money now in preparation for large future expenses. The suggestion unit also analyzes the past accounting data, predicts future income and expenditures, and suggests specific countermeasures. For example, if there is a possibility of a decrease in income, it suggests ways to reduce expenses. In this way, it is possible to suggest future income and expenditure predictions and long-term countermeasures based on the user's past accounting data.

[0069] The suggestion unit can suggest appropriate accounting measures based on the user's life events. The suggestion unit suggests appropriate accounting measures based on the user's life events, for example. For example, for a user who is about to get married, it suggests an estimate of wedding expenses and a savings plan. The suggestion unit also customizes accounting measures taking into account the user's life events. For example, for a user who is planning to give birth, it provides an estimate of childbirth expenses and childcare expenses. The suggestion unit also suggests specific accounting measures based on the user's life events. For example, for a user who is considering changing jobs, it predicts the balance between income and expenses after the job change and suggests measures. In this way, it is possible to suggest appropriate accounting measures based on the user's life events.

[0070] The suggestion unit can use the emotion estimation function to present options that are most reassuring to the user. For example, the suggestion unit uses the emotion estimation function to present options that are most reassuring to the user. For example, if the user is feeling anxious, the suggestion unit suggests options that involve less risk. The suggestion unit also analyzes the user's emotional state in real time and presents options that are reassuring to the user. For example, if the user is feeling stressed, the suggestion unit suggests options that are simple and easy to implement. The suggestion unit also uses the emotion estimation function to build a system that presents options that are most reassuring to the user. For example, the suggestion unit automatically selects the optimal option based on the user's emotion score. This makes it possible to present options that are most reassuring to the user.

[0071] The suggestion unit can visualize and present the user's proposed solution to make it easier to understand visually. For example, the suggestion unit visualizes the proposed solution to the user to make it easier to understand visually. For example, it uses graphs or charts to show the balance of income and expenditure. The suggestion unit also presents the visualized proposed solution to the user to make it easier to understand visually. For example, it uses a flowchart to show the flow of procedures. The suggestion unit also visualizes the proposed solution to make it easier for the user to understand intuitively. For example, it uses infographics to show the necessary procedures and documents. In this way, the proposed solution to the user can be visualized to make it easier to understand visually.

[0072] The suggestion unit can provide solutions specialized for different industries or occupations and customize the system to meet the needs of the user. For example, the suggestion unit provides solutions specialized for different industries or occupations and customizes the system to meet the needs of the user. For example, the suggestion unit proposes specific tax measures to a freelance user. The suggestion unit also provides solutions customized for each industry or occupation and makes suggestions to meet the needs of the user. For example, the suggestion unit proposes inventory management measures to a user in the retail industry. The suggestion unit also builds a system that provides solutions specialized for different industries or occupations and customizes the system to meet the needs of the user. For example, the suggestion unit proposes medical expense deduction measures to a user in the medical industry. This makes it possible to provide solutions specialized for different industries or occupations and customize the system to meet the needs of the user.

[0073] The suggestion unit can use the emotion estimation function to resolve any anxiety or doubt the user may have about the proposed solution in real time. The suggestion unit, for example, uses the emotion estimation function to resolve any anxiety or doubt the user may have about the proposed solution in real time. For example, if the user is feeling anxious, the suggestion unit provides additional explanations. The suggestion unit also analyzes the user's emotional state in real time and engages in a dialogue to resolve the anxiety or doubt. For example, if the user has a question, the suggestion unit provides detailed information. The suggestion unit also uses the emotion estimation function to build a system that resolves any anxiety or doubt the user may have about the proposed solution in real time. For example, the suggestion unit automatically provides an appropriate answer based on the user's emotion score. This allows any anxiety or doubt the user may have about the proposed solution to be resolved in real time.

[0074] The reminding unit can analyze the user's schedule and provide reminders at the optimal timing. The reminding unit, for example, analyzes the user's schedule and provides reminders at the optimal timing. For example, it references the user's calendar and provides a reminder the day before an important procedure. The reminding unit also adjusts the timing of reminders based on the user's schedule. For example, it provides reminders to avoid times when the user is busy. The reminding unit also builds a system that analyzes the user's schedule and provides reminders at the optimal timing. For example, it sends reminder notifications according to the user's plans. This allows reminders to be provided at the optimal timing based on the user's schedule.

[0075] The reminding unit can customize the reminder content based on the user's past behavioral patterns. The reminding unit, for example, analyzes the user's past behavioral patterns and customizes the reminder content. For example, the reminder unit focuses on reminding users about procedures that they have tended to forget in the past. The reminding unit also individually adjusts the reminder content based on the user's past behavioral data. For example, if the user frequently forgets a specific procedure, the reminder unit will remind users about that procedure in detail. The reminding unit also analyzes the user's past behavioral patterns and builds a system that customizes the reminder content. For example, the reminder unit automatically generates optimal reminder content based on the user's behavioral history. This makes it possible to customize the reminder content based on the user's past behavioral patterns.

[0076] The reminding unit can use the emotion estimation function to consider the emotional state of the user when receiving the reminder and provide a reminding method that reduces stress. The reminding unit, for example, uses the emotion estimation function to analyze the emotional state of the user when receiving the reminder and provide a reminding method that reduces stress. For example, the reminder is sent during a time when the user is relaxed. The reminding unit also analyzes the user's emotional state in real time and provides a reminding method that reduces stress. For example, if the user is feeling stressed, the reminder is sent using gentle words. The reminding unit also uses the emotion estimation function to build a system that considers the emotional state of the user when receiving the reminder and provides a reminding method that reduces stress. For example, the optimal reminding method is automatically selected based on the user's emotion score. This makes it possible to provide a reminding method that takes the user's emotional state into consideration and reduces stress.

[0077] The reminding unit can send reminders not only via email or SMS, but also via devices such as smartwatches and smart speakers. For example, the reminding unit can send reminders not only via email or SMS, but also via smartwatches. For example, the reminding unit can send a notification to the user's smartwatch to remind them to complete a procedure. The reminding unit can also send reminders via smart speakers. For example, the reminder can be sent via voice to the user's smart speaker to prompt them to complete a procedure. The reminding unit can also build a system that sends reminders via multiple devices. For example, the reminder can be sent to a device selected by the user, such as email, SMS, smartwatch, or smart speaker. This allows reminders to be sent via multiple devices.

[0078] The reminding unit can customize the reminder content audio and visually according to the user's preferences. For example, the reminder unit customizes the reminder content audio according to the user's preferences. For example, the reminder is given in the voice of a voice actor that the user likes. The reminder unit also customizes the reminder content visually. For example, a reminder notification is created using a design or color that the user likes. The reminder unit also builds a system that customizes the reminder content audio and visually according to the user's preferences. For example, the reminder is given based on settings selected by the user. This allows the reminder content to be customized audio and visually according to the user's preferences.

[0079] The reminding unit can use the emotion estimation function to monitor the user's emotional response when receiving a reminder and continuously adjust the optimal reminder method. The reminding unit, for example, uses the emotion estimation function to monitor the user's emotional response when receiving a reminder and continuously adjust the optimal reminder method. For example, if the user is feeling stressed, the reminding unit reduces the frequency of reminders. The reminding unit also analyzes the user's emotional response in real time and adjusts the reminder method. For example, reminders are sent during times when the user is relaxed. The reminding unit also uses the emotion estimation function to monitor the user's emotional response when receiving a reminder and builds a system that continuously adjusts the optimal reminder method. For example, the reminder method is dynamically changed based on the user's emotion score. This makes it possible to monitor the user's emotional response and continuously adjust the optimal reminder method.

[0080] The recording unit can store data records in a tamper-proof form using blockchain technology. The recording unit, for example, stores data records in a tamper-proof form using blockchain technology. For example, accounting data is recorded on a blockchain to prevent tampering by third parties. The recording unit also uses blockchain technology to ensure data transparency. For example, it allows users to check the data history at any time. The recording unit also builds a system that stores data records in a tamper-proof form using blockchain technology. For example, accounting data is recorded on a blockchain to improve reliability. This allows data records to be stored in a tamper-proof form using blockchain technology.

[0081] The recording unit can provide an interactive dashboard so that users can easily search and refer to data. The recording unit, for example, provides an interactive dashboard so that users can easily search and refer to data. For example, income and expense data can be displayed in graphs and charts. The recording unit also uses the interactive dashboard to allow users to intuitively manipulate data. For example, a filter function can be used to display data for a specific period. The recording unit also builds a system that provides an interactive dashboard so that users can easily search and refer to data. For example, the recording unit allows users to manipulate data by clicking and dragging. This makes it possible to provide an interactive dashboard so that users can easily search and refer to data.

[0082] The recording unit can use the emotion estimation function to design an interface that increases the sense of security when the user checks data. The recording unit, for example, uses the emotion estimation function to design an interface that increases the sense of security when the user checks data. For example, the recording unit displays data during times when the user is relaxed. The recording unit also analyzes the user's emotional state in real time and provides an interface that increases the sense of security. For example, if the user is feeling anxious, a gentle design and color are used. The recording unit also uses the emotion estimation function to build a system that designs an interface that increases the sense of security when the user checks data. For example, the interface design is dynamically changed based on the user's emotion score. This makes it possible to design an interface that increases the sense of security when the user checks data.

[0083] The recording unit can link the recorded data with cloud storage to make it accessible from anywhere. The recording unit, for example, links the recorded data with cloud storage to make it accessible from anywhere. For example, as long as the user is connected to the Internet, the data can be checked anytime, anywhere. The recording unit also automatically backs up data using cloud storage. For example, the data can be periodically saved to the cloud to prevent data loss. The recording unit also builds a system that links the recorded data with cloud storage to make it accessible from anywhere. For example, the user can check the data from a smartphone or tablet. This allows the recorded data to be linked with cloud storage to make it accessible from anywhere.

[0084] The recording unit can introduce periodic audits by a third party to ensure data transparency. For example, the recording unit introduces periodic audits by a third party to ensure data transparency. For example, a third party verifies the accuracy of accounting data. The recording unit also periodically conducts audits by a third party to ensure data transparency. For example, an audit is conducted once a year to improve the reliability of the data. The recording unit also builds a system that introduces periodic audits by a third party to ensure data transparency. For example, the audit results are made public to users to increase transparency. This allows periodic audits by a third party to be introduced to ensure data transparency.

[0085] The recording unit can use the emotion estimation function to provide information to alleviate the anxiety the user feels about data transparency. The recording unit, for example, uses the emotion estimation function to provide information to alleviate the anxiety the user feels about data transparency. For example, if the user feels anxious, the recording unit provides a detailed explanation about data transparency. The recording unit also analyzes the user's emotional state in real time and provides information to alleviate the anxiety. For example, if the user has questions, the recording unit shows specific examples. The recording unit also uses the emotion estimation function to build a system that provides information to alleviate the anxiety the user feels about data transparency. For example, appropriate information is automatically provided based on the user's emotion score. This makes it possible to provide information to alleviate the anxiety the user feels about data transparency.

[0086] The suggestion unit can analyze past request history and develop an algorithm that detects patterns of exorbitant request fees. For example, the suggestion unit analyzes past request history and develops an algorithm that detects patterns of exorbitant request fees. For example, abnormally high fees are automatically detected. The suggestion unit also builds a system that analyzes patterns of exorbitant request fees based on the request history. For example, it calculates the going rate of fees based on past data and detects outliers. The suggestion unit also analyzes past request history and develops an algorithm that detects patterns of exorbitant request fees. For example, it displays a warning if a specific company is charging exorbitant fees. This makes it possible to develop an algorithm that analyzes past request history and detects patterns of exorbitant request fees.

[0087] The suggestion unit can present fair prices in real time when a user makes a request, allowing them to compare. The suggestion unit, for example, builds a system that presents fair prices in real time when a user makes a request, allowing them to compare. For example, it displays a list of prices from multiple companies. The suggestion unit also presents fair prices in real time, allowing the user to compare. For example, it calculates the going rate based on past data and presents it to the user. The suggestion unit also develops a system that presents fair prices in real time when a user makes a request, allowing them to compare. For example, it warns of exorbitant prices based on the going rate. This makes it possible to present fair prices in real time when a user makes a request, allowing them to compare.

[0088] The suggestion unit can use the emotion estimation function to provide support to reduce the anxiety the user feels about fees. The suggestion unit, for example, uses the emotion estimation function to provide support to reduce the anxiety the user feels about fees. For example, if the user feels anxious, the suggestion unit provides a detailed explanation of fees. The suggestion unit also analyzes the user's emotional state in real time and provides support to reduce the anxiety about fees. For example, if the user has questions, the suggestion unit provides specific examples. The suggestion unit also uses the emotion estimation function to build a system that provides support to reduce the anxiety the user feels about fees. For example, appropriate information is automatically provided based on the user's emotion score. This makes it possible to provide support to reduce the anxiety the user feels about fees.

[0089] The proposal unit can create a database of price rates in different regions and industries and provide it to users. The proposal unit, for example, creates a database of price rates in different regions and industries and builds a system to provide it to users. For example, it displays price rates for each region in a list. The proposal unit also creates a database of price rates in different industries and provides it to users. For example, it presents appropriate prices based on price rates for each industry. The proposal unit also creates a database of price rates in different regions and industries and makes it easy for users to access. For example, users can check price rates for each region and industry by searching. This allows price rates for different regions and industries to be created in a database and provided to users.

[0090] The suggestion unit can build a platform that allows users to easily provide feedback on fees. For example, the suggestion unit builds a platform that allows users to easily provide feedback on fees. For example, it provides a function that allows users to post ratings and comments on fees. The suggestion unit also uses the feedback platform to allow users to share their opinions on fees. For example, they can refer to the feedback of other users. The suggestion unit also develops a system that builds a platform that allows users to easily provide feedback on fees. For example, it improves the transparency of fees based on the feedback. This makes it possible to build a platform that allows users to easily provide feedback on fees.

[0091] The suggestion unit can use the emotion estimation function to collect in real time the user's dissatisfaction with fees and propose improvement measures. For example, the suggestion unit uses the emotion estimation function to collect in real time the user's dissatisfaction with fees and propose improvement measures. For example, if the user is dissatisfied, the suggestion unit proposes a review of the fees. The suggestion unit also analyzes the user's emotional state in real time and collects dissatisfaction with fees. For example, if the user is dissatisfied, the suggestion unit presents specific improvement measures. The suggestion unit also uses the emotion estimation function to build a system that collects in real time the user's dissatisfaction with fees and proposes improvement measures. For example, appropriate improvement measures are automatically provided based on the user's emotion score. This makes it possible to collect in real time the user's dissatisfaction with fees and propose improvement measures.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The dialogue unit analyzes the user's tone of voice and speaking style, and uses the emotion estimation function to grasp the user's emotional state and conduct appropriate dialogue. For example, if the user is nervous, the dialogue unit conducts dialogue to relax the user. The dialogue unit also analyzes the user's speaking pattern and uses the emotion estimation function to detect signs of stress or anxiety. For example, if the user is impatient, the dialogue unit conducts dialogue at a slower pace. The dialogue unit also analyzes the user's tone of voice and speaking style, and uses the emotion estimation function to conduct dialogue that elicits positive emotions. For example, if the user is depressed, the dialogue unit offers words of encouragement. This makes it possible to conduct dialogue that is appropriate for the user's emotional state.

[0094] The dialogue unit can refer to the user's past dialogue history and provide a dialogue customized to each individual situation. For example, the user's past dialogue history can be stored in a database and referenced during the current dialogue. For example, a dialogue tailored to the user's preferences and interests can be conducted based on the content of previous conversations. The dialogue unit can also analyze the past dialogue history to understand the user's patterns and tendencies. For example, the dialogue unit can provide pre-prepared answers to topics the user frequently asks about. The dialogue unit can also provide a dialogue customized to each individual situation based on the user's past dialogue history. For example, the dialogue unit can provide specific advice to solve a problem the user previously had. This enables a more personalized dialogue based on the user's past dialogue history.

[0095] The dialogue unit analyzes the user's non-verbal communication to gain a deeper understanding. For example, it captures the user's facial expression with a camera and uses facial expression analysis technology to understand their emotional state. For example, if the user is smiling, it continues a positive dialogue. The dialogue unit also analyzes the user's gestures to understand their non-verbal communication. For example, if the user has their arms crossed, it determines that the user is wary and engages in a reassuring dialogue. The dialogue unit also analyzes the user's non-verbal communication and adjusts the content of the dialogue. For example, if the user nods, it determines that the user understands and moves on to the next step. In this way, analyzing the user's non-verbal communication enables a deeper understanding.

[0096] The dialogue unit can analyze images and documents uploaded by the user during the dialogue and automatically extract accounting information. For example, it analyzes images of receipts and invoices uploaded by the user during the dialogue and automatically extracts accounting information. For example, it uses OCR technology to read amounts and dates. The dialogue unit also analyzes documents uploaded by the user and automatically extracts necessary accounting data. For example, it extracts deposit and withdrawal information from bank statements. The dialogue unit also analyzes images uploaded by the user during the dialogue and automatically saves the accounting information in a database. For example, it analyzes tax-related documents and extracts necessary information. This makes it possible to automatically extract accounting information from images and documents uploaded by the user.

[0097] The dialogue unit can introduce a multilingual dialogue system to accommodate users from different languages ​​and cultural backgrounds. For example, a multilingual dialogue system can be introduced to accommodate users from different languages. For example, dialogues can be conducted in multiple languages, such as English, Spanish, and Chinese. The dialogue unit also develops a dialogue system that takes cultural backgrounds into consideration to accommodate users from different cultural backgrounds. For example, dialogues can be conducted that understand culturally specific expressions and customs. The dialogue unit also introduces a multilingual dialogue system to conduct dialogues in the language selected by the user. For example, if the user selects Japanese, the dialogue can proceed in Japanese. This makes it possible to accommodate users from different languages ​​and cultural backgrounds.

[0098] The dialogue unit can use the emotion estimation function to make relaxation suggestions to reduce the stress and anxiety the user feels during the dialogue. For example, the emotion estimation function can be used to detect the stress and anxiety the user feels during the dialogue and make relaxation suggestions. For example, the dialogue unit can suggest deep breathing or playing relaxing music. The dialogue unit can also analyze the user's emotional state in real time and engage in dialogue to reduce stress and anxiety. For example, if the user is nervous, the dialogue unit can provide a topic that will help them relax. The dialogue unit can also use the emotion estimation function to suggest relaxation methods to reduce the stress and anxiety the user feels during the dialogue. For example, the dialogue unit can introduce meditation or stretching techniques. This makes it possible to make relaxation suggestions to reduce the user's stress and anxiety.

[0099] The suggestion unit can predict future income and expenditures based on the user's past accounting data and suggest long-term countermeasures. For example, the suggestion unit analyzes the user's past accounting data to predict future income and expenditures. For example, it predicts income and expenditures for the next year based on past income and expenditure patterns. The suggestion unit also suggests long-term countermeasures based on the user's past accounting data. For example, it suggests starting to save money now in preparation for large future expenses. The suggestion unit also analyzes the past accounting data to predict future income and expenditures and suggest specific countermeasures. For example, if there is a possibility of a decrease in income, it suggests ways to reduce expenses. In this way, it is possible to suggest future income and expenditure predictions and long-term countermeasures based on the user's past accounting data.

[0100] The suggestion unit can suggest appropriate accounting strategies based on the user's life events. For example, the suggestion unit suggests appropriate accounting strategies based on the user's life events. For example, for a user who is about to get married, it suggests an estimate of wedding expenses and a savings plan. The suggestion unit also customizes accounting strategies taking into account the user's life events. For example, for a user who is planning to give birth, it provides an estimate of childbirth expenses and childcare expenses. The suggestion unit also suggests specific accounting strategies based on the user's life events. For example, for a user who is considering changing jobs, it predicts the balance between income and expenses after the job change and suggests strategies. In this way, it is possible to suggest appropriate accounting strategies based on the user's life events.

[0101] The suggestion unit can use the emotion estimation function to present options that are most reassuring to the user. For example, the emotion estimation function is used to present options that are most reassuring to the user. For example, if the user is feeling anxious, a low-risk solution is proposed. The suggestion unit also analyzes the user's emotional state in real time and presents options that are reassuring to the user. For example, if the user is feeling stressed, a simple and easy-to-implement solution is proposed. The suggestion unit also uses the emotion estimation function to build a system that presents options that are most reassuring to the user. For example, the optimal solution is automatically selected based on the user's emotion score. This makes it possible to present options that are most reassuring to the user.

[0102] The suggestion unit can visualize and present the user's proposed solution to make it easier to understand visually. For example, the proposed solution to the user can be visualized to make it easier to understand visually. For example, a balance of income and expenditure can be shown using a graph or chart. The suggestion unit can also present the visualized proposed solution to the user to make it easier to understand visually. For example, a flow chart can be used to show the flow of procedures. The suggestion unit can also visualize the proposed solution to make it easier for the user to understand intuitively. For example, infographics can be used to show the necessary procedures and documents. In this way, the proposed solution to the user can be visualized to make it easier to understand visually.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The dialogue unit engages in dialogue to understand the user's situation. For example, when the user describes their situation, the dialogue unit analyzes the content and collects information to suggest appropriate ways to deal with the situation. The dialogue unit can also analyze the user's tone of voice and speaking style, and use an emotion estimation function to grasp the user's emotional state and engage in appropriate dialogue. For example, if the user is nervous, the dialogue unit will engage in dialogue to help them relax. Step 2: The suggestion unit suggests solutions based on the user's situation understood by the dialogue unit. For example, if tax filing is approaching, the suggestion unit may remind the user to prepare the necessary documents. The suggestion unit can also predict future income and expenditures based on the user's past accounting data and suggest long-term solutions. For example, the suggestion unit may suggest starting to save money now in preparation for a large future expense. Step 3: The reminder section will remind the user to take action based on the solution proposed by the suggestion section. For example, the tax return deadline is next week. It will send a reminder such as, "Do you have all the necessary documents?" The reminder section can also analyze the user's schedule and send reminders at the optimal time. For example, it can refer to the user's calendar and send a reminder the day before an important procedure. Step 4: The recording unit records the user's accounting data. For example, it allows users to easily check past income and expenditure data and generate reports as needed. The recording unit can also store data records in an unalterable format using blockchain technology. For example, accounting data can be recorded on the blockchain to prevent tampering by third parties.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, a dialogue section for understanding the user's situation; a suggestion unit that suggests a solution based on the user's situation understood by the dialogue unit; a reminding unit that reminds the user to take a procedure based on the solution proposed by the suggesting unit; a recording unit that records the user's accounting data. A system characterized by:

2. The dialogue unit Analyzing the user's tone of voice and speaking style, understanding the user's emotional state, and conducting appropriate dialogue 2. The system of claim 1.

3. The dialogue unit Refer to the user's past interaction history and provide a customized interaction according to the individual situation.

2. The system of claim 1.

4. The dialogue unit Analyze the user's non-verbal communication to gain a deeper understanding 2. The system of claim 1.

5. The dialogue unit Analyze images and documents uploaded by the user during the interaction and automatically extract accounting information.

2. The system of claim 1.

6. The dialogue unit To accommodate users from different languages ​​and cultures, a multilingual dialogue system is introduced.

2. The system of claim 1.

Images