System

Abstract

An object of a system according to an embodiment is to provide a specific and highly accurate answer to a question about a household budget of a user.SOLUTION: A system includes a question answering part, an information collection part, and a statistical analysis part. The question answering unit generates an answer to the question of the user. The information collection unit collects information provided by a user. The statistical analysis unit analyzes the collected information to generate statistical data.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 techniques have had the problem of making it difficult to provide specific and accurate answers to questions about a user's household finances.

[0005] The system according to the embodiment aims to provide specific and accurate answers to questions about a user's household finances. [Means for solving the problem]

[0006] The system according to the embodiment includes a question answering unit, an information collecting unit, and a statistical analysis unit. The question answering unit generates answers to questions posed by users. The information collecting unit collects information provided by users. The statistical analysis unit analyzes the collected information to generate statistical data. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific and accurate answers to questions about a user's household finances. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A household accounting system according to an embodiment of the present invention provides comprehensive answers to users' money-related questions. Based on the information provided by the user, the system offers more accurate advice and suggests specific ways to save money. Furthermore, service providers can use the collected user information as statistical data to provide useful information to other users. This allows the household accounting system to help users efficiently manage their household finances and save money toward their goals.

[0029] A household accounting system according to an embodiment includes a question-answering unit, an information collection unit, and a statistical analysis unit. The question-answering unit generates answers to user questions. For example, in response to a user's question, "How much should I save for my child's college tuition?", the generation AI presents a specific amount, such as "¥X for a private school, ¥X for a public school." The generation AI can also provide more specific advice based on information provided by the user. For example, the AI ​​may provide specific advice such as, "If your current savings amount is ¥X, your annual income is ¥X, and your family structure is ¥X, you will need to save ¥X each month to cover your child's college tuition." The information collection unit collects information provided by the user. For example, when the user inputs information such as their current savings amount, annual income, and family structure, the generation AI analyzes the information and provides highly accurate advice. Furthermore, the information collection unit can analyze the user's consumption behavior and purchasing history to provide more specific advice. For example, if the user frequently makes expensive purchases, the generation AI may suggest reviewing their spending. The statistical analysis unit analyzes the collected information to generate statistical data. For example, the service provider analyzes the collected user information as statistical data and provides useful information to other users. For example, the service provider may provide information such as, "For a family with the same family structure and annual income as you, they will have saved about XX amount by the time their child is XX years old, and the average amount saved for school fees is XX." This allows the household accounting system according to the embodiment to provide comprehensive answers to users' questions and provide highly accurate advice based on the information provided by the user. For example, the user can compare their household financial situation with that of other families and use it as reference. Furthermore, the user can efficiently manage their household finances and save money toward their goals.

[0030] The question answering unit can learn the user's past question history, predict the user's question patterns, and prepare answers in advance. For example, the question answering unit uses a generation AI to analyze the user's past question history and identify frequently asked questions. For example, if the user has asked about "children's education expenses" many times in the past, the generation AI learns that pattern and prepares answers in advance for the next question. In addition, the question answering unit can provide quick and appropriate answers by predicting the user's question patterns and preparing answers in advance. This allows the system to provide quick and appropriate answers by predicting the user's question patterns and preparing answers in advance.

[0031] The question answering unit can include legal and tax advice in its answers to questions. For example, in the question answering unit, the generation AI provides relevant legal advice in response to a user's question. For example, in response to a "question about purchasing a home," the generation AI also answers "legal points to note regarding mortgage contracts" and "legal procedures for real estate transactions." In addition, in the question answering unit, the generation AI provides relevant tax advice in response to a user's question. For example, in response to a "question about year-end tax adjustments," the generation AI also answers "deductible expenses" and "tax calculation methods." In this way, by including legal and tax advice, more comprehensive answers can be provided to users.

[0032] The information collection unit can predict future changes by taking into account the user's life events. For example, the information collection unit takes into account the user's life events when the generation AI collects user information. For example, if the user is planning to "get married," the generation AI predicts changes in household finances after the marriage and provides appropriate advice. The information collection unit can also provide more specific advice by having the generation AI take into account the user's life events and predict future changes. For example, if the user is planning to "give birth," the generation AI predicts changes in household finances after the birth and provides appropriate advice. In this way, by taking into account the user's life events, more specific and accurate advice can be provided.

[0033] The information collection unit analyzes the user's consumption behavior and purchasing history and can provide more specific advice. For example, the generation AI in the information collection unit analyzes the user's consumption behavior and purchasing history and provides specific advice. For example, if the user frequently makes "expensive purchases," the generation AI will suggest reviewing their spending. The information collection unit can also support the user's household management by analyzing the user's consumption behavior and purchasing history and providing more specific advice. For example, it will suggest specific ways for the user to reduce "wasteful spending." In this way, by analyzing the user's consumption behavior and purchasing history, more specific and appropriate advice can be provided.

[0034] The statistical analysis unit uses statistical data to not only compare the user's household situation with other users, but also to predict future economic trends. In the statistical analysis unit, for example, the generation AI uses statistical data to compare the user's household situation with other users. For example, the generation AI evaluates the user's savings and spending patterns by comparing them with households with the same annual income and family structure. In addition, the generation AI uses statistical data to predict future economic trends. For example, the generation AI predicts future economic conditions using analysis of economic indicators and macroeconomic models. This allows the user's household situation to be compared with other users and future economic trends to be predicted, making it possible to provide more appropriate advice.

[0035] The statistical analysis unit uses statistical data to analyze household trends by region and age, and can provide region-specific advice. In the statistical analysis unit, for example, the generation AI uses statistical data to analyze household trends by region. For example, it evaluates the differences in household spending patterns between urban and rural areas and provides region-specific advice. In addition, the generation AI uses statistical data to analyze household trends by age. For example, it evaluates the differences in consumption trends between young people and the elderly and provides age-specific advice. In this way, by analyzing household trends by region and age and providing region-specific advice, more appropriate advice can be provided to users.

[0036] The statistical analysis unit uses statistical data to compare the user's household situation not only with other users, but also with household situations in different cultural spheres and countries. For example, the generation AI uses statistical data to compare the user's household situation with other users. For example, the generation AI evaluates the user's savings and spending patterns by comparing with households with the same annual income and family structure. The generation AI also uses statistical data to compare household situations in different cultural spheres and countries. For example, it evaluates income and spending patterns and cultural consumption trends by country, and provides advice from an international perspective. This allows the generation AI to provide advice from an international perspective by comparing household situations in different cultural spheres and countries.

[0037] The statistical analysis unit uses statistical data to not only compare the user's household situation with other users, but also to suggest areas for improvement by comparing it with past data. In the statistical analysis unit, for example, the generation AI uses statistical data to compare the user's household situation with other users. For example, it evaluates the user's savings and spending patterns by comparing them with households with the same annual income and family structure. In addition, the generation AI uses statistical data to suggest areas for improvement by comparing it with past data. For example, it analyzes the user's past income and expenditure data and suggests areas for improvement. This allows the user to more effectively support household management by suggesting areas for improvement by comparing it with past data.

[0038] The information collection unit can automate the collection of user information by linking with wearable devices and smart home devices. For example, the generation AI of the information collection unit links with wearable devices to automatically collect the user's health data. For example, the generation AI of the information collection unit links with wearable devices to automatically collect the user's health data. For example, the generation AI of the information collection unit links with smart home devices to automatically collect the user's lifestyle data. For example, the generation AI of the information collection unit suggests optimizing energy consumption based on data from a smart thermostat. In this way, by linking with wearable devices and smart home devices, the collection of user information can be automated and more accurate advice can be provided.

[0039] The information collection unit can gamify the collection of user information, allowing the user to provide information while having fun. For example, the information collection unit gamifies the collection of user information using a generation AI, allowing the user to provide information while having fun. For example, a system is provided whereby points can be earned by clearing household management missions. Furthermore, the information collection unit gamifies the collection of user information using a generation AI, allowing the user to provide information while having fun, thereby enabling the collection of more information to be collected. For example, the user provides income and expenditure data by playing a household management game. In this way, gamifying the collection of user information allows the user to provide information while having fun and collect more information.

[0040] The question answering unit can provide answers to questions not only in text format but also in infographic or video format. For example, the generation AI in the question answering unit provides answers to user questions not only in text format but also in infographic format. For example, in response to a question about "how to review your household finances," an answer is provided using graphs or charts that are easy to understand visually. The question answering unit can also provide answers to user questions in video format. For example, in response to a question about "how to save money," an answer is provided using an animated video. In this way, by providing answers not only in text format but also in infographic or video format, it is possible to provide users with information that is easier to understand.

[0041] The question answering unit can customize answers to questions according to the user's preferences. In the question answering unit, for example, the generation AI customizes answers according to the user's preferences. For example, if the user prefers "detailed explanations," the generation AI provides detailed answers, and if the user prefers "concise explanations," the generation AI provides concise answers. Furthermore, the question answering unit can provide more appropriate information to the user by having the generation AI customize answers according to the user's preferences. For example, if the user prefers "visuals," the generation AI provides answers using infographics. In this way, by customizing answers according to the user's preferences, more appropriate information can be provided to the user.

[0042] The information collection unit can anonymize user information and strengthen data encryption and access control to protect user privacy. In the information collection unit, for example, the generation AI anonymizes user information and encrypts the data. For example, the information collection unit protects the user's personal information by encrypting it. In addition, the information collection unit can also protect the user's privacy by restricting access to the user information and allowing only authenticated users to access it. In this way, the user's privacy can be protected by anonymizing user information and strengthening data encryption and access control.

[0043] The information collection unit can increase transparency regarding the handling of user information, allowing users to check how their information is being used. The information collection unit, for example, increases transparency regarding the handling of user information by the generation AI. For example, it provides a dashboard that allows users to check how their information is being used. Furthermore, the information collection unit increases transparency regarding the handling of user information by the generation AI, allowing users to provide information with peace of mind. For example, it clearly states the purpose of data use and notifies the user. In this way, increasing transparency regarding the handling of user information allows users to check how their information is being used and provide information with peace of mind.

[0044] The information collection unit can strengthen privacy protection of user information by using blockchain technology. For example, the generation AI in the information collection unit strengthens privacy protection of user information by using blockchain technology. For example, the transaction history of user information is recorded on the blockchain to prevent tampering. Furthermore, the information collection unit can prevent data tampering by having the generation AI strengthen privacy protection of user information by using blockchain technology. For example, it provides distributed management of data and transaction traceability. As a result, the use of blockchain technology can strengthen privacy protection of user information and prevent data tampering.

[0045] The information collection unit can increase reliability by having the privacy protection of user information audited by a third-party organization. The information collection unit can increase reliability, for example, by having the generation AI have the privacy protection of user information audited by a third-party organization. For example, by conducting regular security audits and making the results public. The information collection unit can also increase reliability by having the generation AI have the privacy protection of user information audited by a third-party organization. For example, by clearly indicating the frequency of audits and the items to be audited and notifying the user. In this way, having the generation AI have the audited by a third-party organization can increase reliability in the privacy protection of user information and provide users with a sense of security.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The question answering unit can provide answers to user questions not only in text format but also in infographic or video format. For example, in response to a question about "how to review your household finances," an answer using a visually easy-to-understand graph or chart is provided. In addition, in response to a question about "how to save money," an answer using an animated video is provided. In this way, by providing answers in infographic or video format instead of just text format, it is possible to provide users with information that is easier to understand.

[0048] The information collection unit can predict future changes by taking into account the user's life events. For example, if the user is planning to "get married," the information collection unit predicts changes in household finances after marriage and provides appropriate advice. Also, if the user is planning to "have a baby," the information collection unit predicts changes in household finances after the baby is born and provides appropriate advice. In this way, by taking into account the user's life events, more specific and accurate advice can be provided.

[0049] The statistical analysis unit uses statistical data to not only compare the user's financial situation with other users, but also to predict future economic trends. For example, it predicts future economic conditions using analysis of economic indicators and macroeconomic models. This allows the user's financial situation to be compared with other users' and future economic trends to be predicted, thereby providing more appropriate advice.

[0050] The information collection unit can automate the collection of user information by linking with wearable devices and smart home devices. For example, the generation AI can link with wearable devices to automatically collect the user's health data. For example, it can provide advice on household management based on the user's heart rate and step count data according to their health condition. The generation AI can also link with smart home devices to automatically collect the user's lifestyle data. For example, it can suggest ways to optimize energy consumption based on data from a smart thermostat. In this way, by linking with wearable devices and smart home devices, it can automate the collection of user information and provide more accurate advice.

[0051] The information collection unit can gamify the collection of user information, allowing users to provide information while having fun. For example, the generation AI can gamify the collection of user information, allowing users to provide information while having fun. For example, a system can be provided whereby points can be earned by clearing household management missions. In addition, by playing a household management game, users can provide income and expenditure data. In this way, gamifying the collection of user information allows users to provide information while having fun, and collect more information.

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

[0053] Step 1: The question-answering unit generates an answer to the user's question. For example, if the user asks, "How much should I save for my child's college tuition?", the generation AI will present a specific amount such as "¥X for private school, ¥X for public school." The generation AI can also provide more specific advice based on the information provided by the user. For example, it can provide specific advice such as, "If your current savings are ¥X, your annual income is ¥X, and your family structure is ¥X, you will need to save ¥X each month to cover your child's college tuition." Step 2: The information collection unit collects information provided by the user. For example, if the user inputs information such as their current savings, annual income, and family composition, the generation AI will analyze that information and provide highly accurate advice. Furthermore, the information collection unit can analyze the user's consumption behavior and purchasing history to provide more specific advice. For example, if the user frequently makes "expensive purchases," the generation AI will suggest reviewing their spending. Step 3: The statistical analysis unit analyzes the collected information to generate statistical data. For example, the service provider analyzes the collected user information as statistical data and provides useful information to other users. For example, it may provide information such as, "If your family has the same family structure and annual income as you, they will have saved about XX amount by the time their child is XX years old, and the average amount they have saved for school fees is about XX."

[0054] (Example 2) A household accounting system according to an embodiment of the present invention provides comprehensive answers to users' money-related questions. Based on the information provided by the user, the system offers more accurate advice and suggests specific ways to save money. Furthermore, service providers can use the collected user information as statistical data to provide useful information to other users. This allows the household accounting system to help users efficiently manage their household finances and save money toward their goals.

[0055] A household accounting system according to an embodiment includes a question-answering unit, an information collection unit, and a statistical analysis unit. The question-answering unit generates answers to user questions. For example, in response to a user's question, "How much should I save for my child's college tuition?", the generation AI presents a specific amount, such as "¥X for a private school, ¥X for a public school." The generation AI can also provide more specific advice based on information provided by the user. For example, the AI ​​may provide specific advice such as, "If your current savings amount is ¥X, your annual income is ¥X, and your family structure is ¥X, you will need to save ¥X each month to cover your child's college tuition." The information collection unit collects information provided by the user. For example, when the user inputs information such as their current savings amount, annual income, and family structure, the generation AI analyzes the information and provides highly accurate advice. Furthermore, the information collection unit can analyze the user's consumption behavior and purchasing history to provide more specific advice. For example, if the user frequently makes expensive purchases, the generation AI may suggest reviewing their spending. The statistical analysis unit analyzes the collected information to generate statistical data. For example, the service provider analyzes the collected user information as statistical data and provides useful information to other users. For example, the service provider may provide information such as, "For a family with the same family structure and annual income as you, they will have saved about XX amount by the time their child is XX years old, and the average amount saved for school fees is XX." This allows the household accounting system according to the embodiment to provide comprehensive answers to users' questions and provide highly accurate advice based on the information provided by the user. For example, the user can compare their household financial situation with that of other families and use it as reference. Furthermore, the user can efficiently manage their household finances and save money toward their goals.

[0056] The question answering unit can learn the user's past question history, predict the user's question patterns, and prepare answers in advance. For example, the question answering unit uses a generation AI to analyze the user's past question history and identify frequently asked questions. For example, if the user has asked about "children's education expenses" many times in the past, the generation AI learns that pattern and prepares answers in advance for the next question. In addition, the question answering unit can provide quick and appropriate answers by predicting the user's question patterns and preparing answers in advance. This allows the system to provide quick and appropriate answers by predicting the user's question patterns and preparing answers in advance.

[0057] The question answering unit can include legal and tax advice in its answers to questions. For example, in the question answering unit, the generation AI provides relevant legal advice in response to a user's question. For example, in response to a "question about purchasing a home," the generation AI also answers "legal points to note regarding mortgage contracts" and "legal procedures for real estate transactions." In addition, in the question answering unit, the generation AI provides relevant tax advice in response to a user's question. For example, in response to a "question about year-end tax adjustments," the generation AI also answers "deductible expenses" and "tax calculation methods." In this way, by including legal and tax advice, more comprehensive answers can be provided to users.

[0058] The question answering unit uses the emotion estimation function to analyze the emotion a user feels when asking a question and can adjust the tone and content of the answer according to that emotion. For example, the question answering unit uses the emotion estimation function to analyze the emotion a user feels when entering a question. For example, if the user feels "anxiety," the generation AI provides an answer in a tone that gives a sense of security. The question answering unit also uses the emotion estimation function to analyze the emotion a user feels when entering a question and adjusts the content of the answer according to that emotion. For example, if the user feels "anger," the generation AI provides an answer in a calm tone. This makes it possible to improve user satisfaction by providing answers that correspond to the user's emotions.

[0059] The information collection unit can predict future changes by taking into account the user's life events. For example, the information collection unit takes into account the user's life events when the generation AI collects user information. For example, if the user is planning to "get married," the generation AI predicts changes in household finances after the marriage and provides appropriate advice. The information collection unit can also provide more specific advice by having the generation AI take into account the user's life events and predict future changes. For example, if the user is planning to "give birth," the generation AI predicts changes in household finances after the birth and provides appropriate advice. In this way, by taking into account the user's life events, more specific and accurate advice can be provided.

[0060] The information collection unit analyzes the user's consumption behavior and purchasing history and can provide more specific advice. For example, the generation AI in the information collection unit analyzes the user's consumption behavior and purchasing history and provides specific advice. For example, if the user frequently makes "expensive purchases," the generation AI will suggest reviewing their spending. The information collection unit can also support the user's household management by analyzing the user's consumption behavior and purchasing history and providing more specific advice. For example, it will suggest specific ways for the user to reduce "wasteful spending." In this way, by analyzing the user's consumption behavior and purchasing history, more specific and appropriate advice can be provided.

[0061] The information collection unit can use the emotion estimation function to analyze the emotion a user feels when providing information and provide incentives to elicit positive emotions. The information collection unit, for example, uses the emotion estimation function to analyze the emotion a user feels when providing information. For example, if the user feels "anxiety," the generation AI provides an incentive to give a sense of security. The information collection unit also uses the emotion estimation function to analyze the emotion a user feels when providing information and provide incentives to elicit positive emotions. For example, a reward that makes the user feel "joy" is provided. In this way, by analyzing the user's emotions and providing incentives to elicit positive emotions, it is possible to increase the user's motivation to provide information.

[0062] The statistical analysis unit uses statistical data to not only compare the user's household situation with other users, but also to predict future economic trends. In the statistical analysis unit, for example, the generation AI uses statistical data to compare the user's household situation with other users. For example, the generation AI evaluates the user's savings and spending patterns by comparing them with households with the same annual income and family structure. In addition, the generation AI uses statistical data to predict future economic trends. For example, the generation AI predicts future economic conditions using analysis of economic indicators and macroeconomic models. This allows the user's household situation to be compared with other users and future economic trends to be predicted, making it possible to provide more appropriate advice.

[0063] The statistical analysis unit uses statistical data to analyze household trends by region and age, and can provide region-specific advice. In the statistical analysis unit, for example, the generation AI uses statistical data to analyze household trends by region. For example, it evaluates the differences in household spending patterns between urban and rural areas and provides region-specific advice. In addition, the generation AI uses statistical data to analyze household trends by age. For example, it evaluates the differences in consumption trends between young people and the elderly and provides age-specific advice. In this way, by analyzing household trends by region and age and providing region-specific advice, more appropriate advice can be provided to users.

[0064] The statistical analysis unit uses the emotion estimation function to analyze the user's emotions when providing information based on statistical data, and can provide information to elicit positive emotions. The statistical analysis unit, for example, uses the emotion estimation function to analyze the user's emotions when providing information based on statistical data. For example, if the user is feeling "anxious," the generation AI provides information that gives a sense of security. The statistical analysis unit also uses the emotion estimation function to analyze the user's emotions when providing information based on statistical data, and provides information to elicit positive emotions. For example, it introduces success stories that make the user feel "joy." This makes it possible to analyze the user's emotions and provide information to elicit positive emotions, thereby improving user satisfaction.

[0065] The statistical analysis unit uses statistical data to compare the user's household situation not only with other users, but also with household situations in different cultural spheres and countries. For example, the generation AI uses statistical data to compare the user's household situation with other users. For example, the generation AI evaluates the user's savings and spending patterns by comparing with households with the same annual income and family structure. The generation AI also uses statistical data to compare household situations in different cultural spheres and countries. For example, it evaluates income and spending patterns and cultural consumption trends by country, and provides advice from an international perspective. This allows the generation AI to provide advice from an international perspective by comparing household situations in different cultural spheres and countries.

[0066] The statistical analysis unit uses statistical data to not only compare the user's household situation with other users, but also to suggest areas for improvement by comparing it with past data. In the statistical analysis unit, for example, the generation AI uses statistical data to compare the user's household situation with other users. For example, it evaluates the user's savings and spending patterns by comparing them with households with the same annual income and family structure. In addition, the generation AI uses statistical data to suggest areas for improvement by comparing it with past data. For example, it analyzes the user's past income and expenditure data and suggests areas for improvement. This allows the user to more effectively support household management by suggesting areas for improvement by comparing it with past data.

[0067] The statistical analysis unit uses the emotion estimation function to monitor the user's emotions in real time when providing information based on statistical data, and can make suggestions that elicit positive emotions. The statistical analysis unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when providing information based on statistical data. For example, if the user is feeling "anxious," the generation AI will make suggestions that give a sense of security. The statistical analysis unit also uses the emotion estimation function to monitor the user's emotions in real time when providing information based on statistical data, and make suggestions that elicit positive emotions. For example, it can introduce success stories that make the user feel "joy." This makes it possible to improve user satisfaction by monitoring the user's emotions in real time and making suggestions that elicit positive emotions.

[0068] The information collection unit can automate the collection of user information by linking with wearable devices and smart home devices. For example, the generation AI of the information collection unit links with wearable devices to automatically collect the user's health data. For example, the generation AI of the information collection unit links with wearable devices to automatically collect the user's health data. For example, the generation AI of the information collection unit links with smart home devices to automatically collect the user's lifestyle data. For example, the generation AI of the information collection unit suggests optimizing energy consumption based on data from a smart thermostat. In this way, by linking with wearable devices and smart home devices, the collection of user information can be automated and more accurate advice can be provided.

[0069] The information collection unit can gamify the collection of user information, allowing the user to provide information while having fun. For example, the information collection unit gamifies the collection of user information using a generation AI, allowing the user to provide information while having fun. For example, a system is provided whereby points can be earned by clearing household management missions. Furthermore, the information collection unit gamifies the collection of user information using a generation AI, allowing the user to provide information while having fun, thereby enabling the collection of more information to be collected. For example, the user provides income and expenditure data by playing a household management game. In this way, gamifying the collection of user information allows the user to provide information while having fun and collect more information.

[0070] The information collection unit uses the emotion estimation function to monitor the emotions of the user when providing information in real time and make suggestions that elicit positive emotions. The information collection unit, for example, uses the emotion estimation function to monitor the emotions of the user when providing information in real time. For example, if the user is feeling "anxious," the generation AI makes suggestions that give a sense of security. The information collection unit also uses the emotion estimation function to monitor the emotions of the user when providing information in real time and make suggestions that elicit positive emotions. For example, it introduces success stories that make the user feel "joy." In this way, by monitoring the user's emotions in real time and making suggestions that elicit positive emotions, it is possible to increase the user's willingness to provide information.

[0071] The question answering unit can provide answers to questions not only in text format but also in infographic or video format. For example, the generation AI in the question answering unit provides answers to user questions not only in text format but also in infographic format. For example, in response to a question about "how to review your household finances," an answer is provided using graphs or charts that are easy to understand visually. The question answering unit can also provide answers to user questions in video format. For example, in response to a question about "how to save money," an answer is provided using an animated video. In this way, by providing answers not only in text format but also in infographic or video format, it is possible to provide users with information that is easier to understand.

[0072] The question answering unit can customize answers to questions according to the user's preferences. In the question answering unit, for example, the generation AI customizes answers according to the user's preferences. For example, if the user prefers "detailed explanations," the generation AI provides detailed answers, and if the user prefers "concise explanations," the generation AI provides concise answers. Furthermore, the question answering unit can provide more appropriate information to the user by having the generation AI customize answers according to the user's preferences. For example, if the user prefers "visuals," the generation AI provides answers using infographics. In this way, by customizing answers according to the user's preferences, more appropriate information can be provided to the user.

[0073] The question answering unit uses the emotion estimation function to estimate the emotion a user is feeling when entering a question in real time, and can make suggestions that elicit positive emotions. The question answering unit, for example, uses the emotion estimation function to estimate the emotion a user is feeling when entering a question in real time. For example, if the user is feeling "anxiety," the generation AI makes suggestions that give a sense of security. The question answering unit also uses the emotion estimation function to estimate the emotion a user is feeling when entering a question in real time, and makes suggestions that elicit positive emotions. For example, it introduces success stories that make the user feel "joy." In this way, by estimating the user's emotion in real time and making suggestions that elicit positive emotions, it is possible to improve user satisfaction.

[0074] The information collection unit can anonymize user information and strengthen data encryption and access control to protect user privacy. In the information collection unit, for example, the generation AI anonymizes user information and encrypts the data. For example, the information collection unit protects the user's personal information by encrypting it. In addition, the information collection unit can also protect the user's privacy by restricting access to the user information and allowing only authenticated users to access it. In this way, the user's privacy can be protected by anonymizing user information and strengthening data encryption and access control.

[0075] The information collection unit can increase transparency regarding the handling of user information, allowing users to check how their information is being used. The information collection unit, for example, increases transparency regarding the handling of user information by the generation AI. For example, it provides a dashboard that allows users to check how their information is being used. Furthermore, the information collection unit increases transparency regarding the handling of user information by the generation AI, allowing users to provide information with peace of mind. For example, it clearly states the purpose of data use and notifies the user. In this way, increasing transparency regarding the handling of user information allows users to check how their information is being used and provide information with peace of mind.

[0076] The information collection unit can use the emotion estimation function to analyze the anxiety and concern the user feels when providing information and take measures to provide a sense of security. The information collection unit, for example, uses the emotion estimation function to analyze the anxiety and concern the user feels when providing information. For example, if the user feels "anxiety," the generation AI takes measures to provide a sense of security. The information collection unit also uses the emotion estimation function to analyze the anxiety and concern the user feels when providing information and take measures to provide a sense of security. For example, the information collection unit can clearly state the privacy policy and ensure the security of data. In this way, the anxiety and concern the user feels when providing information can be analyzed and measures to provide a sense of security can be taken, allowing the user to provide information with peace of mind.

[0077] The information collection unit can strengthen privacy protection of user information by using blockchain technology. For example, the generation AI in the information collection unit strengthens privacy protection of user information by using blockchain technology. For example, the transaction history of user information is recorded on the blockchain to prevent tampering. Furthermore, the information collection unit can prevent data tampering by having the generation AI strengthen privacy protection of user information by using blockchain technology. For example, it provides distributed management of data and transaction traceability. As a result, the use of blockchain technology can strengthen privacy protection of user information and prevent data tampering.

[0078] The information collection unit can increase reliability by having the privacy protection of user information audited by a third-party organization. The information collection unit can increase reliability, for example, by having the generation AI have the privacy protection of user information audited by a third-party organization. For example, by conducting regular security audits and making the results public. The information collection unit can also increase reliability by having the generation AI have the privacy protection of user information audited by a third-party organization. For example, by clearly indicating the frequency of audits and the items to be audited and notifying the user. In this way, having the generation AI have the audited by a third-party organization can increase reliability in the privacy protection of user information and provide users with a sense of security.

[0079] The information collection unit can use the emotion estimation function to monitor in real time the anxiety and concern the user feels when providing information and take measures to provide a sense of security. The information collection unit, for example, uses the emotion estimation function to monitor in real time the anxiety and concern the user feels when providing information. For example, if the user feels "anxiety," the generation AI takes measures to provide a sense of security. The information collection unit also uses the emotion estimation function to monitor in real time the anxiety and concern the user feels when providing information and take measures to provide a sense of security. For example, the information collection unit can clearly state the privacy policy and ensure data security. In this way, by monitoring in real time the anxiety and concern the user feels when providing information and taking measures to provide a sense of security, the user can provide information with peace of mind.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The question answering unit can provide answers to user questions not only in text format but also in infographic or video format. For example, in response to a question about "how to review your household finances," an answer using a visually easy-to-understand graph or chart is provided. In addition, in response to a question about "how to save money," an answer using an animated video is provided. In this way, by providing answers in infographic or video format instead of just text format, it is possible to provide users with information that is easier to understand.

[0082] The information collection unit can predict future changes by taking into account the user's life events. For example, if the user is planning to "get married," the information collection unit predicts changes in household finances after marriage and provides appropriate advice. Also, if the user is planning to "have a baby," the information collection unit predicts changes in household finances after the baby is born and provides appropriate advice. In this way, by taking into account the user's life events, more specific and accurate advice can be provided.

[0083] The statistical analysis unit uses statistical data to not only compare the user's financial situation with other users, but also to predict future economic trends. For example, it predicts future economic conditions using analysis of economic indicators and macroeconomic models. This allows the user's financial situation to be compared with other users' and future economic trends to be predicted, thereby providing more appropriate advice.

[0084] The information collection unit can automate the collection of user information by linking with wearable devices and smart home devices. For example, the generation AI can link with wearable devices to automatically collect the user's health data. For example, it can provide advice on household management based on the user's heart rate and step count data according to their health condition. The generation AI can also link with smart home devices to automatically collect the user's lifestyle data. For example, it can suggest ways to optimize energy consumption based on data from a smart thermostat. In this way, by linking with wearable devices and smart home devices, it can automate the collection of user information and provide more accurate advice.

[0085] The information collection unit can gamify the collection of user information, allowing users to provide information while having fun. For example, the generation AI can gamify the collection of user information, allowing users to provide information while having fun. For example, a system can be provided whereby points can be earned by clearing household management missions. In addition, by playing a household management game, users can provide income and expenditure data. In this way, gamifying the collection of user information allows users to provide information while having fun, and collect more information.

[0086] The question answering unit uses the emotion estimation function to analyze the user's emotions when asking a question and can adjust the tone and content of the answer accordingly. For example, if the user is feeling "anxious," the generation AI will provide an answer in a tone that gives a sense of security. On the other hand, if the user is feeling "anger," the generation AI will provide an answer in a calm tone. This allows the system to provide answers that correspond to the user's emotions, thereby improving user satisfaction.

[0087] The information collection unit uses the emotion estimation function to analyze the emotions felt when a user provides information and can provide incentives to elicit positive emotions. For example, if a user feels "anxiety," the generation AI provides an incentive to give a sense of security. It also provides a reward that makes the user feel "joy." In this way, by analyzing the user's emotions and providing incentives to elicit positive emotions, it is possible to increase the user's motivation to provide information.

[0088] The statistical analysis unit uses the emotion estimation function to analyze the user's emotions when providing information based on statistical data, and can provide information that elicits positive emotions. For example, if the user is feeling "anxious," the generation AI will provide information that gives a sense of security. It will also introduce success stories that make the user feel "joy." This allows the system to analyze the user's emotions and provide information that elicits positive emotions, thereby improving user satisfaction.

[0089] The information collection unit uses the emotion estimation function to analyze the anxiety and concerns the user feels when providing information and can take measures to provide a sense of security. For example, if the user feels "anxious," the generation AI will take measures to provide a sense of security. It also clearly states the privacy policy and ensures data security. This allows the system to analyze the anxiety and concerns the user feels when providing information and take measures to provide a sense of security, allowing the user to provide information with peace of mind.

[0090] The question answering unit uses an emotion estimation function to estimate the user's emotions in real time when they enter a question, and can make suggestions that elicit positive emotions. For example, if the user is feeling "anxious," the generative AI will make suggestions that give them a sense of security. It will also introduce successful examples that make users feel "joy." This allows the system to estimate the user's emotions in real time and make suggestions that elicit positive emotions, thereby improving user satisfaction.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The question-answering unit generates an answer to the user's question. For example, if the user asks, "How much should I save for my child's college tuition?", the generation AI will present a specific amount such as "¥X for private school, ¥X for public school." The generation AI can also provide more specific advice based on the information provided by the user. For example, it can provide specific advice such as, "If your current savings are ¥X, your annual income is ¥X, and your family structure is ¥X, you will need to save ¥X each month to cover your child's college tuition." Step 2: The information collection unit collects information provided by the user. For example, if the user inputs information such as their current savings, annual income, and family composition, the generation AI will analyze that information and provide highly accurate advice. Furthermore, the information collection unit can analyze the user's consumption behavior and purchasing history to provide more specific advice. For example, if the user frequently makes "expensive purchases," the generation AI will suggest reviewing their spending. Step 3: The statistical analysis unit analyzes the collected information to generate statistical data. For example, the service provider analyzes the collected user information as statistical data and provides useful information to other users. For example, it may provide information such as, "If your family has the same family structure and annual income as you, they will have saved about XX amount by the time their child is XX years old, and the average amount they have saved for school fees is about XX."

[0093] 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.

[0094] 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.

[0095] 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.

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

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

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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, in order to avoid confusion and to 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.

[0159] 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]

[0160] 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 question answering unit that generates answers to questions from users; an information collection unit that collects information provided by a user; a statistical analysis unit that analyzes the collected information and generates statistical data. A system characterized by:

2. The question answering unit The system learns the user's past question history, predicts the user's question patterns, and prepares the answers in advance.

2. The system of claim 1.

3. The information collecting unit Taking into account the user's life events, predict future changes 2. The system of claim 1.

4. The statistical analysis unit The statistical data is used to compare the user's financial situation with other users, as well as to predict future economic trends.

2. The system of claim 1.

5. The question answering unit Analyzing the user's emotions when asking a question and adjusting the tone and content of the answer according to the emotions 2. The system of claim 1.

6. The information collecting unit Analyzing the emotions felt when the user provides information, and providing incentives to elicit positive emotions.

2. The system of claim 1.

7. The statistical analysis unit Analyzing the user's emotions when providing information based on the statistical data, and providing the information to elicit positive emotions 2. The system of claim 1.

8. The information collecting unit Analyze the anxieties and concerns that users may have when providing information and take measures to provide a sense of security.

2. The system of claim 1.

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