System

Abstract

An object of the system according to the embodiment is to unitarily grasp contract information, friendship, account information, public utility charge information, cellular phone information, and the like of a oldest old when the user is dead.SOLUTION: A system includes a contract information collection part, a friendship analysis part, an account information collection part, a utility charge information collection part, and a mobile phone information collection part. The contract information collection unit collects contract information of the owner. The friendship analysis unit analyzes a friendship. The account information collection unit collects account information. The utility charge information collection unit collects utility charge information. The mobile phone information collection unit collects mobile phone information.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] With conventional technology, when a super-elderly person nears death, it was difficult to obtain a unified understanding of information such as their home contract details, social relationships, bank account details, utility bill details, and mobile phone information.

[0005] The system of the embodiment aims to centrally grasp information such as home contract information, friendships, bank account information, utility bill information, and mobile phone information of very elderly people when they reach the end of their life. [Means for solving the problem]

[0006] The system according to the embodiment includes a contract information collection unit, a friendship analysis unit, an account information collection unit, a utility bill information collection unit, and a mobile phone information collection unit. The contract information collection unit collects contract information for owner-occupied homes. The friendship analysis unit analyzes friendships. The account information collection unit collects account information. The utility bill information collection unit collects utility bill information. The mobile phone information collection unit collects mobile phone information. [Effects of the Invention]

[0007] When a super-elderly person nears death, the system of the embodiment can centrally obtain information such as their home contract information, social relationships, bank account information, utility bill information, and mobile phone information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information collection system according to an embodiment of the present invention is a system that, when a super-elderly person is nearing death, comprehensively collects various information about the person, such as their home contract, friendships, bank accounts, utility bills, and mobile phone contracts. As a result, the information collection system can efficiently carry out the necessary procedures when the super-elderly person is nearing death.

[0029] An information grasping system according to an embodiment includes a contract information collection unit, a relationship analysis unit, an account information collection unit, a utility bill information collection unit, and a mobile phone information collection unit. The contract information collection unit collects home contract information. For example, the contract information collection unit collects scanned data of home contracts and related documents, and the generation AI analyzes and organizes the contract information based on this data. The contract information collection unit also collects and organizes information such as home ownership, loan balances, and insurance contracts. The relationship analysis unit analyzes relationship information. For example, the relationship analysis unit collects data on contact lists and communication history, and the generation AI analyzes and organizes relationship information based on this data. The relationship analysis unit also collects data from phone books, emails, and social media accounts to identify important contacts. The account information collection unit collects account information. For example, the account information collection unit collects account statements and transaction history data provided by banks and securities companies, and the generation AI analyzes and organizes account information based on this data. The account information collection unit also collects and organizes information such as account numbers, balances, and transaction histories. The utility bill information collection unit collects utility bill information. For example, the utility bill information collection unit collects data such as utility bill bills and payment histories, and the generation AI analyzes and organizes contract information based on this data. The utility bill information collection unit also collects and organizes information such as contract details, payment histories, and unpaid bills. The mobile phone information collection unit collects mobile phone information. For example, the mobile phone information collection unit collects data such as mobile phone bills and usage histories, and the generation AI analyzes and organizes contract information based on this data. The mobile phone information collection unit also collects and organizes information such as contract details, rate plans, and usage histories. As a result, the information collection system according to the embodiment can efficiently carry out procedures necessary when a super-elderly person approaches death. For example, this system can smoothly handle inheritance procedures for a home, sorting out friendships, closing accounts, and canceling utility bill and mobile phone contracts. This reduces the burden on surviving family members and related parties and supports smooth procedures.

[0030] The contract information collection unit can analyze and organize contract information based on scanned data of homeownership contracts and related documents. For example, the contract information collection unit collects scanned data of homeownership contracts and related documents, and the generation AI analyzes and organizes the contract information based on this data. For example, the generation AI can understand the context of the contract and predict future risks and changes to the contract. This allows for efficient organization of homeownership contract information.

[0031] The friendship analysis unit can analyze and organize friendships based on data from contact lists and communication history. For example, the friendship analysis unit collects data from contact lists and communication history, and the generation AI analyzes and organizes friendships based on this data. For example, the generation AI analyzes data from contact lists and communication history to identify important contacts. This allows friendships to be organized efficiently.

[0032] The account information collection unit can analyze and organize account information based on account statement and transaction history data provided by banks and securities companies. The account information collection unit, for example, collects account statement and transaction history data provided by banks and securities companies, and the generation AI analyzes and organizes account information based on this data. For example, the generation AI analyzes account statement and transaction history data and organizes information such as account number, balance, and transaction history. This allows account information to be organized efficiently.

[0033] The utility bill information collection unit can analyze and organize contract information based on data on utility bills and payment history. For example, the utility bill information collection unit collects data on utility bills and payment history, and the generation AI analyzes and organizes contract information based on this data. For example, the generation AI analyzes data on bills and payment history and organizes information such as contract details, payment history, and unpaid charges. This allows utility bill contract information to be organized efficiently.

[0034] The mobile phone information collection unit can analyze and organize contract information based on data from mobile phone bills and usage history. For example, the mobile phone information collection unit collects data from mobile phone bills and usage history, and the generation AI analyzes and organizes contract information based on this data. For example, the generation AI analyzes data from bills and usage history and organizes information such as contract details, rate plans, and usage history. This allows mobile phone contract information to be organized efficiently.

[0035] The contract information collection unit can understand the context of the contract and predict future risks and changes to the contract. For example, the contract information collection unit uses generative AI to analyze the context of a homeowner's contract and predict future risks. For example, it analyzes the future impact of specific clauses in the contract and identifies risks. This makes it possible to predict risks and changes to the homeowner's contract information.

[0036] The contract information collection unit can automatically evaluate the market value of a home based on the analysis of the contract information and propose the optimal timing for selling or renting. The contract information collection unit can, for example, use generation AI to analyze the contract information of a home and automatically evaluate the market value. For example, it calculates the current market value of a home based on the information stated in the contract and market data. This makes it possible to evaluate the market value of a home and propose the optimal timing for selling or renting.

[0037] When analyzing the contract information of a home, the contract information collection unit simultaneously collects the building's maintenance history and repair history, enabling comprehensive management. For example, the contract information collection unit uses generation AI to collect the maintenance history and repair history along with the home's contract information, enabling comprehensive management. For example, the contract and maintenance records can be integrated to comprehensively grasp the condition of the home. This allows for comprehensive management of the home's contract information and maintenance history.

[0038] The contract information collection unit can evaluate the energy efficiency and environmental impact of the home based on the analysis results of the contract information and make improvement proposals. The contract information collection unit, for example, uses generation AI to analyze the contract information of the home and evaluate the energy efficiency and environmental impact. For example, it calculates the energy efficiency of the home based on the energy usage data stated in the contract. This makes it possible to evaluate the energy efficiency and environmental impact of the home and make improvement proposals.

[0039] The friendship relationship analysis unit can automatically prioritize important contacts based on the analysis results of friendship relationships and generate an emergency contact list. The friendship relationship analysis unit can, for example, use generation AI to automatically prioritize important contacts based on the analysis results of friendship relationships. For example, it can prioritize people who are in frequent contact. This allows important contacts to be prioritized in an emergency contact list.

[0040] The friendship relationship analysis unit can evaluate the strength of relationships from past communication history based on the analysis results of friendship relationships and identify important people.The friendship relationship analysis unit can use, for example, a generative AI to evaluate the strength of relationships from past communication history based on the analysis results of friendship relationships.For example, it can identify people who are in frequent contact as important people.This makes it possible to evaluate the strength of relationships from past communication history and identify important people.

[0041] The friendship relationship analysis unit can group contacts based on the analysis results of friendship relationships and visualize the relationships between different groups. The friendship relationship analysis unit, for example, uses generative AI to group contacts based on the analysis results of friendship relationships. For example, they may be classified into groups such as family, friends, and work-related groups. The friendship relationship analysis unit also visualizes the relationships between different groups. For example, it may visually display the relationships between different groups using graphs or charts. This makes it possible to group contacts and visualize the relationships.

[0042] The friendship analysis unit can make suggestions to strengthen relationships based on past events and common hobbies based on the analysis results of friendship relationships. The friendship analysis unit can, for example, use a generation AI to identify past events and common hobbies based on the analysis results of friendship relationships and make suggestions to strengthen relationships. For example, it can encourage interaction with people who share common hobbies. This makes it possible to make suggestions to strengthen relationships based on past events and common hobbies.

[0043] The account information collection unit can predict future fund flows based on the results of the account relationship analysis and propose optimal asset management plans. The account information collection unit can, for example, use a generative AI to predict future fund flows based on the results of the account relationship analysis. For example, it can predict future income and expenses based on past transaction history and propose asset management plans. This makes it possible to predict future fund flows and propose optimal asset management plans.

[0044] The account information collection unit can detect fraudulent transactions from past transaction history based on the results of account-related analysis, thereby strengthening security measures. The account information collection unit can, for example, use generation AI to detect fraudulent transactions from past transaction history based on the results of account-related analysis. For example, it can identify abnormal transaction patterns and issue warnings. This makes it possible to detect fraudulent transactions from past transaction history and strengthen security measures.

[0045] The account information collection unit automates asset transfers between different financial institutions based on the results of analyzing account relationships, enabling optimal asset allocation. The account information collection unit, for example, uses generation AI to automate asset transfers between different financial institutions based on the results of analyzing account relationships. For example, assets can be transferred at the optimal time to maximize profits. This allows asset transfers between different financial institutions to be automated, enabling optimal asset allocation.

[0046] The account information collection unit can propose tax-saving measures from past transaction history based on the results of the analysis of account relationships. The account information collection unit can, for example, use generation AI to propose tax-saving measures from past transaction history based on the results of the analysis of account relationships. For example, it can identify transactions that maximize tax deductions. This makes it possible to propose tax-saving measures from past transaction history.

[0047] When analyzing utility bill contract information, the utility bill information collection unit can predict future payments from past payment history. The utility bill information collection unit, for example, uses generation AI to analyze utility bill contract information and predict future payments from past payment history. For example, it predicts future payment amounts based on past payment patterns. This makes it possible to predict future payments from past payment history.

[0048] The utility fee information collection unit can propose the optimal rate plan based on the analysis results of utility fee contract information, thereby reducing costs. The utility fee information collection unit, for example, uses generation AI to analyze utility fee contract information and propose the optimal rate plan. For example, it selects the optimal rate plan based on current usage. This allows the optimal rate plan to be proposed and costs to be reduced.

[0049] The utility bill information collection unit can analyze energy consumption patterns when analyzing utility bill contract information and suggest eco-friendly lifestyles. The utility bill information collection unit, for example, uses generation AI to analyze utility bill contract information and analyze energy consumption patterns. For example, it identifies energy consumption trends based on past usage data. The utility bill information collection unit also suggests eco-friendly lifestyles. For example, it suggests energy-saving methods and recycling recommendations. This makes it possible to analyze energy consumption patterns and suggest eco-friendly lifestyles.

[0050] The utility bill information collection unit can compare different service providers based on the analysis results of utility bill contract information and suggest the most suitable provider. The utility bill information collection unit can, for example, use generation AI to analyze utility bill contract information and compare different service providers. For example, it can compare fees and service contents and select the most suitable provider. This makes it possible to compare different service providers and suggest the most suitable provider.

[0051] When analyzing mobile phone contract information, the mobile phone information collection unit can propose the optimal rate plan based on past usage history. The mobile phone information collection unit, for example, uses generation AI to analyze mobile phone contract information and propose the optimal rate plan based on past usage history. For example, it selects the optimal plan based on data usage and call time. This makes it possible to propose the optimal rate plan based on past usage history.

[0052] The mobile phone information collection unit can predict future usage patterns based on the analysis results of mobile phone contract information and suggest revisions to the contract terms. The mobile phone information collection unit, for example, uses generation AI to analyze mobile phone contract information and predict future usage patterns. For example, it predicts future data usage and call duration based on past usage data. The mobile phone information collection unit also suggests revisions to the contract terms. For example, it suggests changing the rate plan or extending the contract period. This makes it possible to predict future usage patterns and suggest revisions to the contract terms.

[0053] The mobile phone information collection unit can analyze patterns of data usage and call duration when analyzing mobile phone contract information, and can propose the optimal data plan. The mobile phone information collection unit can, for example, use generation AI to analyze mobile phone contract information and analyze patterns of data usage and call duration. For example, it can select the optimal data plan based on past usage data. This allows it to analyze patterns of data usage and call duration and propose the optimal data plan.

[0054] The mobile phone information collection unit can compare different carriers based on the analysis results of mobile phone contract information and suggest the most suitable carrier. The mobile phone information collection unit, for example, uses generation AI to analyze mobile phone contract information and compare different carriers. For example, it compares fees and service contents and selects the most suitable carrier. This makes it possible to compare different carriers and suggest the most suitable carrier.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The information monitoring system can further include a health information collection unit. The health information collection unit collects and analyzes information about the health status of the super-elderly. For example, the health information collection unit collects medical records and prescription data provided by medical institutions, and the generation AI analyzes and organizes the health status based on this data. The health information collection unit also collects health checkup results and daily health management data (blood pressure, body temperature, heart rate, etc.) to predict health risks. This allows for a comprehensive understanding of the health status of the super-elderly and allows necessary medical procedures to be carried out efficiently.

[0057] The information assessment system can further include a hobby and preference information collection unit. The hobby and preference information collection unit collects and analyzes information about the hobbies and preferences of the very elderly. For example, the hobby and preference information collection unit collects information such as social media and blog posts and online shopping history, and the generation AI analyzes and organizes the hobbies and preferences based on this data. The hobby and preference information collection unit can also provide information on events and communities related to hobbies and make suggestions to improve the quality of life of the very elderly. This makes it possible to provide lifestyle support based on the hobbies and preferences of the very elderly.

[0058] The information assessment system can further include a living environment information collection unit. The living environment information collection unit collects and analyzes information about the living environment of the super-elderly. For example, the living environment information collection unit collects data such as the temperature, humidity, and lighting conditions of the home, and the generation AI analyzes and organizes the living environment based on this data. The living environment information collection unit can also make suggestions for improving the living environment (for example, appropriate temperature settings and lighting adjustments). This can optimize the living environment of the super-elderly and support a more comfortable life.

[0059] The information assessment system can further include a crime prevention information collection unit. The crime prevention information collection unit collects and analyzes crime prevention information related to the residences of the super-elderly. For example, the crime prevention information collection unit collects data from security cameras and sensors, and the generation AI analyzes and organizes crime prevention risks based on this data. The crime prevention information collection unit can also propose crime prevention measures (for example, strengthening security systems or introducing crime prevention products). This can strengthen crime prevention measures in the residences of the super-elderly and support their safe living.

[0060] The information assessment system can further include a traffic information collection unit. The traffic information collection unit collects and analyzes information related to the movements of the super-elderly. For example, the traffic information collection unit collects data on public transportation usage history and travel routes, and the generation AI analyzes and organizes movement patterns based on this data. The traffic information collection unit can also propose optimal means of transportation and routes (for example, bus and train timetables and transfer information). This can efficiently support the movements of the super-elderly.

[0061] The information monitoring system can further include a dietary information collection unit. The dietary information collection unit collects and analyzes information about the diet of the very elderly. For example, the dietary information collection unit collects data on meal menus and nutrients, and the generation AI analyzes and organizes the meal contents based on this data. The dietary information collection unit can also make dietary suggestions based on health status and preferences (for example, nutritionally balanced menus and recipes that suit preferences). This can efficiently support dietary management for the very elderly.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The contract information collection unit collects home contract information. For example, the contract information collection unit collects scanned data of home contracts and related documents, and the generation AI analyzes and organizes the contract information based on this data. The contract information collection unit also collects and organizes information such as home ownership, loan balances, and insurance contracts. Step 2: The friendship analysis unit analyzes friendships. For example, the friendship analysis unit collects data from contact lists and communication history, and the generation AI analyzes and organizes friendships based on this data. The friendship analysis unit also collects data from phone books, emails, and social media to identify important contacts. Step 3: The account information collection unit collects account information. For example, the account information collection unit collects account details and transaction history data provided by banks and securities companies, and the generation AI analyzes and organizes the account information based on this data. The account information collection unit also collects and organizes information such as account numbers, balances, and transaction history. Step 4: The utility bill information collection unit collects utility bill information. For example, the utility bill information collection unit collects data such as utility bills and payment history, and the generation AI analyzes and organizes contract information based on this data. The utility bill information collection unit also collects and organizes information such as contract details, payment history, and unpaid charges. Step 5: The mobile phone information collection unit collects mobile phone information. For example, the mobile phone information collection unit collects data such as mobile phone bills and usage history, and the generation AI analyzes and organizes contract information based on this data. The mobile phone information collection unit also collects and organizes information such as contract details, fee plans, and usage history.

[0064] (Example 2) The information collection system according to an embodiment of the present invention is a system that, when a super-elderly person is nearing death, comprehensively collects various information about the person, such as their home contract, friendships, bank accounts, utility bills, and mobile phone contracts. As a result, the information collection system can efficiently carry out the necessary procedures when the super-elderly person is nearing death.

[0065] An information grasping system according to an embodiment includes a contract information collection unit, a relationship analysis unit, an account information collection unit, a utility bill information collection unit, and a mobile phone information collection unit. The contract information collection unit collects home contract information. For example, the contract information collection unit collects scanned data of home contracts and related documents, and the generation AI analyzes and organizes the contract information based on this data. The contract information collection unit also collects and organizes information such as home ownership, loan balances, and insurance contracts. The relationship analysis unit analyzes relationship information. For example, the relationship analysis unit collects data on contact lists and communication history, and the generation AI analyzes and organizes relationship information based on this data. The relationship analysis unit also collects data from phone books, emails, and social media accounts to identify important contacts. The account information collection unit collects account information. For example, the account information collection unit collects account statements and transaction history data provided by banks and securities companies, and the generation AI analyzes and organizes account information based on this data. The account information collection unit also collects and organizes information such as account numbers, balances, and transaction histories. The utility bill information collection unit collects utility bill information. For example, the utility bill information collection unit collects data such as utility bill bills and payment histories, and the generation AI analyzes and organizes contract information based on this data. The utility bill information collection unit also collects and organizes information such as contract details, payment histories, and unpaid bills. The mobile phone information collection unit collects mobile phone information. For example, the mobile phone information collection unit collects data such as mobile phone bills and usage histories, and the generation AI analyzes and organizes contract information based on this data. The mobile phone information collection unit also collects and organizes information such as contract details, rate plans, and usage histories. As a result, the information collection system according to the embodiment can efficiently carry out procedures necessary when a super-elderly person approaches death. For example, this system can smoothly handle inheritance procedures for a home, sorting out friendships, closing accounts, and canceling utility bill and mobile phone contracts. This reduces the burden on surviving family members and related parties and supports smooth procedures.

[0066] The contract information collection unit can analyze and organize contract information based on scanned data of homeownership contracts and related documents. For example, the contract information collection unit collects scanned data of homeownership contracts and related documents, and the generation AI analyzes and organizes the contract information based on this data. For example, the generation AI can understand the context of the contract and predict future risks and changes to the contract. This allows for efficient organization of homeownership contract information.

[0067] The friendship analysis unit can analyze and organize friendships based on data from contact lists and communication history. For example, the friendship analysis unit collects data from contact lists and communication history, and the generation AI analyzes and organizes friendships based on this data. For example, the generation AI analyzes data from contact lists and communication history to identify important contacts. This allows friendships to be organized efficiently.

[0068] The account information collection unit can analyze and organize account information based on account statement and transaction history data provided by banks and securities companies. The account information collection unit, for example, collects account statement and transaction history data provided by banks and securities companies, and the generation AI analyzes and organizes account information based on this data. For example, the generation AI analyzes account statement and transaction history data and organizes information such as account number, balance, and transaction history. This allows account information to be organized efficiently.

[0069] The utility bill information collection unit can analyze and organize contract information based on data on utility bills and payment history. For example, the utility bill information collection unit collects data on utility bills and payment history, and the generation AI analyzes and organizes contract information based on this data. For example, the generation AI analyzes data on bills and payment history and organizes information such as contract details, payment history, and unpaid charges. This allows utility bill contract information to be organized efficiently.

[0070] The mobile phone information collection unit can analyze and organize contract information based on data from mobile phone bills and usage history. For example, the mobile phone information collection unit collects data from mobile phone bills and usage history, and the generation AI analyzes and organizes contract information based on this data. For example, the generation AI analyzes data from bills and usage history and organizes information such as contract details, rate plans, and usage history. This allows mobile phone contract information to be organized efficiently.

[0071] The contract information collection unit can understand the context of the contract and predict future risks and changes to the contract. For example, the contract information collection unit uses generative AI to analyze the context of a homeowner's contract and predict future risks. For example, it analyzes the future impact of specific clauses in the contract and identifies risks. This makes it possible to predict risks and changes to the homeowner's contract information.

[0072] The contract information collection unit can automatically evaluate the market value of a home based on the analysis of the contract information and propose the optimal timing for selling or renting. The contract information collection unit can, for example, use generation AI to analyze the contract information of a home and automatically evaluate the market value. For example, it calculates the current market value of a home based on the information stated in the contract and market data. This makes it possible to evaluate the market value of a home and propose the optimal timing for selling or renting.

[0073] The contract information collection unit can use the emotion estimation function to analyze contract information related to a home and prioritize contract details that are emotionally important, taking into account the owner's emotions. The contract information collection unit can, for example, use the emotion estimation function to analyze contract information related to a home and prioritize contract details that are emotionally important, taking into account the owner's emotions. For example, the contract information collection unit can analyze the owner's emotions regarding specific clauses in the contract and prioritize contract details that are emotionally important. This allows the contract information to be organized taking into account the owner's emotions.

[0074] When analyzing the contract information of a home, the contract information collection unit simultaneously collects the building's maintenance history and repair history, enabling comprehensive management. For example, the contract information collection unit uses generation AI to collect the maintenance history and repair history along with the home's contract information, enabling comprehensive management. For example, the contract and maintenance records can be integrated to comprehensively grasp the condition of the home. This allows for comprehensive management of the home's contract information and maintenance history.

[0075] The contract information collection unit can evaluate the energy efficiency and environmental impact of the home based on the analysis results of the contract information and make improvement proposals. The contract information collection unit, for example, uses generation AI to analyze the contract information of the home and evaluate the energy efficiency and environmental impact. For example, it calculates the energy efficiency of the home based on the energy usage data stated in the contract. This makes it possible to evaluate the energy efficiency and environmental impact of the home and make improvement proposals.

[0076] The contract information collection unit can use the emotion estimation function to analyze the contract information of the home and propose changes or updates to the contract contents based on the owner's emotions. The contract information collection unit can, for example, use the emotion estimation function to analyze the contract information of the home and propose changes or updates to the contract contents based on the owner's emotions. For example, it can identify contract clauses that make the owner feel uneasy and propose changes. This makes it possible to propose changes or updates to the contract contents based on the owner's emotions.

[0077] The friendship relationship analysis unit can automatically prioritize important contacts based on the analysis results of friendship relationships and generate an emergency contact list. The friendship relationship analysis unit can, for example, use generation AI to automatically prioritize important contacts based on the analysis results of friendship relationships. For example, it can prioritize people who are in frequent contact. This allows important contacts to be prioritized in an emergency contact list.

[0078] The friendship relationship analysis unit can evaluate the strength of relationships from past communication history based on the analysis results of friendship relationships and identify important people.The friendship relationship analysis unit can use, for example, a generative AI to evaluate the strength of relationships from past communication history based on the analysis results of friendship relationships.For example, it can identify people who are in frequent contact as important people.This makes it possible to evaluate the strength of relationships from past communication history and identify important people.

[0079] The friendship relationship analysis unit can use the emotion estimation function to identify emotionally important people based on the analysis results of the friendship relationships and make suggestions to contact them preferentially. The friendship relationship analysis unit can, for example, use the emotion estimation function to identify emotionally important people based on the analysis results of the friendship relationships. For example, people with strong emotional ties can be listed preferentially. This makes it possible to identify emotionally important people and make suggestions to contact them preferentially.

[0080] The friendship relationship analysis unit can group contacts based on the analysis results of friendship relationships and visualize the relationships between different groups. The friendship relationship analysis unit, for example, uses generative AI to group contacts based on the analysis results of friendship relationships. For example, they may be classified into groups such as family, friends, and work-related groups. The friendship relationship analysis unit also visualizes the relationships between different groups. For example, it may visually display the relationships between different groups using graphs or charts. This makes it possible to group contacts and visualize the relationships.

[0081] The friendship analysis unit can make suggestions to strengthen relationships based on past events and common hobbies based on the analysis results of friendship relationships. The friendship analysis unit can, for example, use a generation AI to identify past events and common hobbies based on the analysis results of friendship relationships and make suggestions to strengthen relationships. For example, it can encourage interaction with people who share common hobbies. This makes it possible to make suggestions to strengthen relationships based on past events and common hobbies.

[0082] The friendship relationship analysis unit can use the emotion estimation function to make suggestions to prioritize maintaining emotionally positive relationships based on the analysis results of the friendship relationships. The friendship relationship analysis unit can, for example, use the emotion estimation function to identify emotionally positive relationships based on the analysis results of the friendship relationships and make suggestions to prioritize maintaining them. For example, the friendship relationship analysis unit can encourage interactions with people who have positive emotions. This makes it possible to make suggestions to prioritize maintaining emotionally positive relationships.

[0083] The account information collection unit can predict future fund flows based on the results of the account relationship analysis and propose optimal asset management plans. The account information collection unit can, for example, use a generative AI to predict future fund flows based on the results of the account relationship analysis. For example, it can predict future income and expenses based on past transaction history and propose asset management plans. This makes it possible to predict future fund flows and propose optimal asset management plans.

[0084] The account information collection unit can detect fraudulent transactions from past transaction history based on the results of account-related analysis, thereby strengthening security measures. The account information collection unit can, for example, use generation AI to detect fraudulent transactions from past transaction history based on the results of account-related analysis. For example, it can identify abnormal transaction patterns and issue warnings. This makes it possible to detect fraudulent transactions from past transaction history and strengthen security measures.

[0085] The account information collection unit can use the emotion estimation function to identify emotionally important transactions based on the analysis results of account relationships and make a proposal to manage them with priority. The account information collection unit can, for example, use the emotion estimation function to identify emotionally important transactions based on the analysis results of account relationships. For example, transactions with high emotional value can be managed with priority. This makes it possible to identify emotionally important transactions and make a proposal to manage them with priority.

[0086] The account information collection unit automates asset transfers between different financial institutions based on the results of analyzing account relationships, enabling optimal asset allocation. The account information collection unit, for example, uses generation AI to automate asset transfers between different financial institutions based on the results of analyzing account relationships. For example, assets can be transferred at the optimal time to maximize profits. This allows asset transfers between different financial institutions to be automated, enabling optimal asset allocation.

[0087] The account information collection unit can propose tax-saving measures from past transaction history based on the results of the analysis of account relationships. The account information collection unit can, for example, use generation AI to propose tax-saving measures from past transaction history based on the results of the analysis of account relationships. For example, it can identify transactions that maximize tax deductions. This makes it possible to propose tax-saving measures from past transaction history.

[0088] The account information collection unit can use the emotion estimation function to make a suggestion to prioritize emotionally positive transactions based on the analysis results of the account relationships. The account information collection unit, for example, uses the emotion estimation function to identify emotionally positive transactions based on the analysis results of the account relationships and makes a suggestion to prioritize them. For example, transactions with high emotional satisfaction are prioritized. This makes it possible to make a suggestion to prioritize emotionally positive transactions.

[0089] When analyzing utility bill contract information, the utility bill information collection unit can predict future payments from past payment history. The utility bill information collection unit, for example, uses generation AI to analyze utility bill contract information and predict future payments from past payment history. For example, it predicts future payment amounts based on past payment patterns. This makes it possible to predict future payments from past payment history.

[0090] The utility fee information collection unit can propose the optimal rate plan based on the analysis results of utility fee contract information, thereby reducing costs. The utility fee information collection unit, for example, uses generation AI to analyze utility fee contract information and propose the optimal rate plan. For example, it selects the optimal rate plan based on current usage. This allows the optimal rate plan to be proposed and costs to be reduced.

[0091] The utility bill information collection unit can use the emotion estimation function to analyze utility bill contract information and make a proposal to prioritize management of emotionally important payments. The utility bill information collection unit, for example, uses the emotion estimation function to analyze utility bill contract information and identify emotionally important payments. For example, it prioritizes management of payments with high emotional value. This makes it possible to make a proposal to prioritize management of emotionally important payments.

[0092] The utility bill information collection unit can analyze energy consumption patterns when analyzing utility bill contract information and suggest eco-friendly lifestyles. The utility bill information collection unit, for example, uses generation AI to analyze utility bill contract information and analyze energy consumption patterns. For example, it identifies energy consumption trends based on past usage data. The utility bill information collection unit also suggests eco-friendly lifestyles. For example, it suggests energy-saving methods and recycling recommendations. This makes it possible to analyze energy consumption patterns and suggest eco-friendly lifestyles.

[0093] The utility bill information collection unit can compare different service providers based on the analysis results of utility bill contract information and suggest the most suitable provider. The utility bill information collection unit can, for example, use generation AI to analyze utility bill contract information and compare different service providers. For example, it can compare fees and service contents and select the most suitable provider. This makes it possible to compare different service providers and suggest the most suitable provider.

[0094] The utility bill information collection unit can use the emotion estimation function to analyze utility bill contract information and suggest emotionally positive payment methods. The utility bill information collection unit, for example, uses the emotion estimation function to analyze utility bill contract information and identify emotionally positive payment methods. For example, it preferentially suggests payment methods with high emotional satisfaction. This makes it possible to suggest emotionally positive payment methods.

[0095] When analyzing mobile phone contract information, the mobile phone information collection unit can propose the optimal rate plan based on past usage history. The mobile phone information collection unit, for example, uses generation AI to analyze mobile phone contract information and propose the optimal rate plan based on past usage history. For example, it selects the optimal plan based on data usage and call time. This makes it possible to propose the optimal rate plan based on past usage history.

[0096] The mobile phone information collection unit can predict future usage patterns based on the analysis results of mobile phone contract information and suggest revisions to the contract terms. The mobile phone information collection unit, for example, uses generation AI to analyze mobile phone contract information and predict future usage patterns. For example, it predicts future data usage and call duration based on past usage data. The mobile phone information collection unit also suggests revisions to the contract terms. For example, it suggests changing the rate plan or extending the contract period. This makes it possible to predict future usage patterns and suggest revisions to the contract terms.

[0097] The mobile phone information collection unit can use the emotion estimation function to analyze mobile phone contract information and make a suggestion to prioritize managing emotionally important contacts. The mobile phone information collection unit can, for example, use the emotion estimation function to analyze mobile phone contract information and identify emotionally important contacts. For example, contacts with high emotional value can be managed with priority. This makes it possible to make a suggestion to prioritize managing emotionally important contacts.

[0098] The mobile phone information collection unit can analyze patterns of data usage and call duration when analyzing mobile phone contract information, and can propose the optimal data plan. The mobile phone information collection unit can, for example, use generation AI to analyze mobile phone contract information and analyze patterns of data usage and call duration. For example, it can select the optimal data plan based on past usage data. This allows it to analyze patterns of data usage and call duration and propose the optimal data plan.

[0099] The mobile phone information collection unit can compare different carriers based on the analysis results of mobile phone contract information and suggest the most suitable carrier. The mobile phone information collection unit, for example, uses generation AI to analyze mobile phone contract information and compare different carriers. For example, it compares fees and service contents and selects the most suitable carrier. This makes it possible to compare different carriers and suggest the most suitable carrier.

[0100] The mobile phone information collection unit can use the emotion estimation function to analyze mobile phone contract information and suggest emotionally positive usage patterns. The mobile phone information collection unit, for example, uses the emotion estimation function to analyze mobile phone contract information and identify emotionally positive usage patterns. For example, usage patterns with high emotional satisfaction are preferentially suggested. This makes it possible to suggest emotionally positive usage patterns.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The information monitoring system can further include a health information collection unit. The health information collection unit collects and analyzes information about the health status of the super-elderly. For example, the health information collection unit collects medical records and prescription data provided by medical institutions, and the generation AI analyzes and organizes the health status based on this data. The health information collection unit also collects health checkup results and daily health management data (blood pressure, body temperature, heart rate, etc.) to predict health risks. This allows for a comprehensive understanding of the health status of the super-elderly and allows necessary medical procedures to be carried out efficiently.

[0103] The information assessment system can further include a hobby and preference information collection unit. The hobby and preference information collection unit collects and analyzes information about the hobbies and preferences of the very elderly. For example, the hobby and preference information collection unit collects information such as social media and blog posts and online shopping history, and the generation AI analyzes and organizes the hobbies and preferences based on this data. The hobby and preference information collection unit can also provide information on events and communities related to hobbies and make suggestions to improve the quality of life of the very elderly. This makes it possible to provide lifestyle support based on the hobbies and preferences of the very elderly.

[0104] The information assessment system can also use an emotion estimation function to monitor the emotional state of the very elderly and provide emotional support. For example, the emotion estimation function can be used to analyze emotions from everyday conversations and social media posts to identify feelings of loneliness or anxiety. The emotion estimation function can also be used to suggest emotionally positive activities (hobbies, interactions with friends, etc.). This makes it possible to understand the emotional state of the very elderly and provide emotional support.

[0105] The information assessment system can further include a living environment information collection unit. The living environment information collection unit collects and analyzes information about the living environment of the super-elderly. For example, the living environment information collection unit collects data such as the temperature, humidity, and lighting conditions of the home, and the generation AI analyzes and organizes the living environment based on this data. The living environment information collection unit can also make suggestions for improving the living environment (for example, appropriate temperature settings and lighting adjustments). This can optimize the living environment of the super-elderly and support a more comfortable life.

[0106] The information grasping system can further use an emotion estimation function to provide communication support based on the emotions of the very elderly. For example, the emotion estimation function can be used to analyze emotions from everyday conversations and social media posts, and identify emotionally important topics. The emotion estimation function can also be used to provide emotionally positive topics and make suggestions to facilitate smooth communication. This makes it possible to provide communication support based on the emotions of the very elderly.

[0107] The information assessment system can further include a crime prevention information collection unit. The crime prevention information collection unit collects and analyzes crime prevention information related to the residences of the super-elderly. For example, the crime prevention information collection unit collects data from security cameras and sensors, and the generation AI analyzes and organizes crime prevention risks based on this data. The crime prevention information collection unit can also propose crime prevention measures (for example, strengthening security systems or introducing crime prevention products). This can strengthen crime prevention measures in the residences of the super-elderly and support their safe living.

[0108] The information grasping system can further use an emotion estimation function to provide health management based on the emotions of the very elderly. For example, the emotion estimation function can be used to analyze emotions from everyday conversations and social media posts to identify stress and anxiety. The emotion estimation function can also be used to suggest emotionally positive health management methods (such as relaxation and hobby activities). This makes it possible to provide health management based on the emotions of the very elderly.

[0109] The information assessment system can further include a traffic information collection unit. The traffic information collection unit collects and analyzes information related to the movements of the super-elderly. For example, the traffic information collection unit collects data on public transportation usage history and travel routes, and the generation AI analyzes and organizes movement patterns based on this data. The traffic information collection unit can also propose optimal means of transportation and routes (for example, bus and train timetables and transfer information). This can efficiently support the movements of the super-elderly.

[0110] The information grasping system can further use an emotion estimation function to suggest hobby activities based on the emotions of the very elderly. For example, the emotion estimation function can be used to analyze emotions from everyday conversations and social media posts to identify emotionally positive hobby activities. The emotion estimation function can also be used to suggest hobby activities that are emotionally satisfying. This makes it possible to suggest hobby activities based on the emotions of the very elderly.

[0111] The information monitoring system can further include a dietary information collection unit. The dietary information collection unit collects and analyzes information about the diet of the very elderly. For example, the dietary information collection unit collects data on meal menus and nutrients, and the generation AI analyzes and organizes the meal contents based on this data. The dietary information collection unit can also make dietary suggestions based on health status and preferences (for example, nutritionally balanced menus and recipes that suit preferences). This can efficiently support dietary management for the very elderly.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The contract information collection unit collects home contract information. For example, the contract information collection unit collects scanned data of home contracts and related documents, and the generation AI analyzes and organizes the contract information based on this data. The contract information collection unit also collects and organizes information such as home ownership, loan balances, and insurance contracts. Step 2: The friendship analysis unit analyzes friendships. For example, the friendship analysis unit collects data from contact lists and communication history, and the generation AI analyzes and organizes friendships based on this data. The friendship analysis unit also collects data from phone books, emails, and social media to identify important contacts. Step 3: The account information collection unit collects account information. For example, the account information collection unit collects account details and transaction history data provided by banks and securities companies, and the generation AI analyzes and organizes the account information based on this data. The account information collection unit also collects and organizes information such as account numbers, balances, and transaction history. Step 4: The utility bill information collection unit collects utility bill information. For example, the utility bill information collection unit collects data such as utility bills and payment history, and the generation AI analyzes and organizes contract information based on this data. The utility bill information collection unit also collects and organizes information such as contract details, payment history, and unpaid charges. Step 5: The mobile phone information collection unit collects mobile phone information. For example, the mobile phone information collection unit collects data such as mobile phone bills and usage history, and the generation AI analyzes and organizes contract information based on this data. The mobile phone information collection unit also collects and organizes information such as contract details, fee plans, and usage history.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] 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.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 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.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] 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]

[0181] 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 contract information collection unit that collects homeowner contract information; a friendship analysis unit that analyzes friendships; an account information collection unit that collects account information; a utility fee information collection unit that collects utility fee information; A mobile phone information collection unit that collects mobile phone information. A system characterized by:

2. The contract information collection unit When analyzing the contract information of the home, the building's maintenance history and repair history are also collected at the same time, and comprehensive management is performed.

2. The system of claim 1.

3. The friendship analysis unit Based on the results of the friendship analysis, the system automatically prioritizes important contacts and generates an emergency contact list.

2. The system of claim 1.

4. The account information collection unit Based on the results of an analysis of account relationships, we predict future cash flows and propose optimal asset management plans.

2. The system of claim 1.

5. The utility fee information collection unit Analyze the contract information for utility bills and make suggestions for managing emotionally important payments with priority.

2. The system of claim 1.

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