system

Abstract

An object of a system according to an embodiment is to efficiently perform a procedure associated with moving and life preparation in a new house.SOLUTION: A system includes an address input part, a TODO list generation part, an environmental information provision part, a furniture proposal part, and a coupon issuance part. The address input part inputs addresses of a moving source and a moving destination of the user. The TODO list generation unit generates a TODO list based on the address input by the address input unit. An environment information providing part provides environment information around the new house on the basis of the TODO list generated by the TODO list generation part. The furniture proposal unit proposes furniture and household goods according to the room layout on the basis of the environmental information provided by the environmental information providing unit. The coupon issuing unit issues a coupon for a nearby supermarket or convenience store based on the information proposed by the furniture proposing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the procedures involved in moving and preparing for life in a new home are complicated, making it difficult to carry out these tasks efficiently.

[0005] The system according to the embodiment aims to efficiently carry out procedures associated with moving and preparations for life in a new residence. [Means for solving the problem]

[0006] The system according to the embodiment includes an address input unit, a to-do list generation unit, an environmental information providing unit, a furniture suggestion unit, and a coupon issuing unit. The address input unit inputs the addresses of the user's previous and next residences. The to-do list generation unit generates a to-do list based on the addresses input by the address input unit. The environmental information providing unit provides environmental information about the area surrounding the new residence based on the to-do list generated by the to-do list generation unit. The furniture suggestion unit suggests furniture and household goods according to the floor plan based on the environmental information provided by the environmental information providing unit. The coupon issuing unit issues coupons for nearby supermarkets and convenience stores based on the information suggested by the furniture suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently carry out procedures associated with moving and prepare for life in a new residence. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The application system according to the embodiment of the present invention is a system that automatically provides necessary procedures and lifestyle information when a user simply inputs the addresses of their current and new residences. This allows the application system to efficiently and conveniently handle procedures associated with a job transfer or move, as well as subsequent lifestyle arrangements.

[0029] An application system according to an embodiment includes an address input unit, a to-do list generation unit, an environmental information provision unit, a furniture suggestion unit, and a coupon issuance unit. The address input unit inputs the user's addresses of their current and future residences. For example, information such as a postal code, prefecture, city, ward, town, or village, and street address can be input. The to-do list generation unit generates a to-do list based on the address input by the address input unit. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do, and generates a to-do list containing information such as phone numbers to contact, gas companies, and the location of the government office where a change of address notification should be submitted. The environmental information provision unit provides environmental information about the area surrounding the new residence based on the to-do list generated by the to-do list generation unit. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do, and provides information on nearby supermarkets, convenience stores, hospitals, schools, and the like. The furniture suggestion unit suggests furniture and household goods according to the floor plan based on the environmental information provided by the environmental information provision unit. For example, the generation AI receives prompts including instructions on what the user wants the generation AI to do, and makes specific suggestions such as, "This size sofa is suitable for the living room," or "These curtains fit perfectly with this window." The coupon issuing unit issues coupons for nearby supermarkets and convenience stores based on the information suggested by the furniture suggestion unit. For example, the generation AI receives prompts including instructions on what the user wants the generation AI to do, and issues a coupon such as, "Use this coupon to get 10% off your first purchase." This allows the application system according to the embodiment to efficiently and conveniently organize procedures for transfers and moving, as well as subsequent life.

[0030] The to-do list generation unit can learn the user's past relocation history and generate an individually customized to-do list. For example, the generation AI in the to-do list generation unit learns the user's past relocation history and generates an individually customized to-do list by taking into account the regional characteristics of the new location and the procedures that have been completed in the past. For example, it suggests procedures for the new location based on the procedures required in previous moves. The to-do list generation unit also stores the history of the user's past relocation procedures in a database, and the generation AI uses that data to generate an optimal to-do list for the next move. For example, it considers the moving companies used in the past and the order of procedures. The to-do list generation unit also analyzes the user's past relocation history and generates a customized to-do list tailored to the regional characteristics of the new location and the user's lifestyle. For example, it considers issues that occurred in previous moves and suggests tasks that include preventive measures. This allows the generation of an optimal to-do list based on the user's past relocation history.

[0031] The TODO list generation unit can add relevant tasks by taking into account the user's lifestyle and hobbies and preferences. For example, the generation AI of the TODO list generation unit learns the user's lifestyle and hobbies and preferences and adds relevant tasks to the TODO list based on that. For example, for a user who has a pet, tasks related to pet moving procedures and care are added. The TODO list generation unit also takes the user's hobbies and preferences into account and adds tasks to the TODO list for starting new hobbies and activities at the new address. For example, it provides information on nearby sports clubs and cultural facilities. The TODO list generation unit also analyzes the user's lifestyle and adds tasks to the TODO list to help them start life at their new address smoothly. For example, it provides information on nearby supermarkets and convenience stores and suggests a shopping list for the first day. This allows the generation AI to add optimal tasks based on the user's lifestyle and hobbies and preferences.

[0032] The TODO list generation unit can add tasks related to pet moving procedures and care. For example, the generation AI adds tasks related to pet moving procedures and care to the TODO list based on the user's pet information. For example, it suggests pet health checkups and registering a new veterinarian. Furthermore, if the user has a pet, the TODO list generation unit adds procedures necessary for pet moving to the TODO list. For example, it suggests tasks to support pet transportation and the pet's adaptation to a new home. Furthermore, the TODO list generation unit obtains information related to pet moving procedures and care from a database, and the generation AI generates a TODO list based on that information. For example, it lists the documents and procedures necessary for pet moving. This allows tasks related to pet moving procedures and care to be added.

[0033] The TODO list generation unit can add tasks related to procedures for a child's school or nursery school. For example, the generation AI adds tasks related to school or nursery school procedures to the TODO list based on information about the user's child. For example, it suggests procedures for transferring to a new school or enrolling in nursery school. Furthermore, if the user has children, the generation AI adds tasks necessary for the child's school or nursery school procedures to the TODO list. For example, it suggests visiting a school or attending an entrance information session. Furthermore, the TODO list generation unit obtains information about procedures for a child's school or nursery school from a database, and the generation AI generates a TODO list based on that information. For example, it lists the necessary documents and procedures and notifies the user. This allows tasks related to the child's school or nursery school procedures to be added.

[0034] The environmental information providing unit can provide information on nearby fitness gyms and health food stores taking into account the user's health condition. In the environmental information providing unit, for example, the generation AI analyzes the user's health condition and provides information on nearby fitness gyms and health food stores. For example, gym plans and health food recommendations tailored to the user's health goals are displayed. In addition, the environmental information providing unit provides information on nearby fitness gyms and health food stores based on the user's health data using the generation AI. For example, store information tailored to the user's exercise habits and dietary habits is displayed. In addition, the environmental information providing unit provides customized information on nearby fitness gyms and health food stores taking into account the user's health condition using the generation AI. For example, promotional information on gym classes and health foods tailored to the user's health goals is displayed. This makes it possible to provide information on optimal fitness gyms and health food stores based on the user's health condition.

[0035] The environmental information providing unit can propose the optimal commuting route according to the user's means of transportation. In the environmental information providing unit, for example, the generation AI analyzes the user's means of transportation and proposes the optimal commuting route. For example, it proposes a route that takes traffic congestion information into account for users who use cars, and a safe bicycle path for users who use bicycles. In addition, the environmental information providing unit provides the optimal commuting route according to the user's means of transportation. For example, it displays the shortest route and transfer information for users who use public transportation. In addition, the generation AI considers the user's means of transportation and customizes the proposed commuting route. For example, it provides parking information for users who use cars, and bicycle parking information for users who use bicycles. This makes it possible to propose the optimal commuting route based on the user's means of transportation.

[0036] The environmental information providing unit can provide information on events and community activities being held in the vicinity of the new home. For example, the generation AI provides information on events and community activities being held in the vicinity of the new home. For example, it displays information on local festivals and flea markets. The environmental information providing unit also customizes and provides information on events and community activities being held in the vicinity of the new home based on the user's interests. For example, it displays information on sporting events and cultural activities. The environmental information providing unit also retrieves information on events and community activities being held in the vicinity of the new home from a database, and the generation AI provides this information to the user. For example, it displays information on local volunteer activities and workshops. This makes it possible to provide information on events and community activities being held in the vicinity of the new home.

[0037] The environmental information providing unit can provide information on restaurants and cafes around the new home and display recommendations based on the user's food preferences. For example, the generation AI of the environmental information providing unit provides information on restaurants and cafes around the new home and displays recommendations based on the user's food preferences. For example, the generation AI can suggest restaurants based on the user's favorite cuisine genre. The environmental information providing unit also learns the user's food preferences, and the generation AI customizes and provides information on restaurants and cafes around the new home. For example, it can display restaurants that offer vegetarian and gluten-free options. The environmental information providing unit also retrieves information on restaurants and cafes around the new home from a database, and the generation AI can use that information to display recommendations based on the user's food preferences. For example, it can suggest restaurants based on user reviews and ratings. This makes it possible to provide information on restaurants and cafes around the new home and display recommendations based on the user's food preferences.

[0038] The furniture suggestion unit can learn the user's interior style preferences and suggest furniture and miscellaneous goods based on them. For example, the generation AI of the furniture suggestion unit learns the user's interior style preferences and suggests furniture and miscellaneous goods based on them. For example, it suggests sofas and tables suitable for a living room based on the user's preferred colors and designs. The furniture suggestion unit also analyzes the user's past purchase history and interior style preferences, and the generation AI suggests furniture and miscellaneous goods based on them. For example, it makes suggestions based on the user's preferred brands and styles. The furniture suggestion unit also learns the user's interior style preferences through the generation AI and suggests the optimal furniture and miscellaneous goods for each room based on them. For example, it suggests a relaxing bed and lighting for the bedroom. This makes it possible to suggest the optimal furniture and miscellaneous goods based on the user's interior style preferences.

[0039] The furniture suggestion unit can consider the user's budget and suggest furniture and miscellaneous goods with high cost performance. For example, the generation AI considers the user's budget and suggests furniture and miscellaneous goods with high cost performance. For example, it suggests the optimal sofa or table that can be purchased within the budget. Furthermore, the furniture suggestion unit considers the user's budget and suggests furniture and miscellaneous goods with high cost performance. For example, it makes suggestions using sale information and discount coupons. Furthermore, the generation AI considers the user's budget and suggests customized furniture and miscellaneous goods with high cost performance. For example, it lists the most valuable products within the budget. This makes it possible to suggest the optimal furniture and miscellaneous goods based on the user's budget.

[0040] The furniture suggestion unit can suggest eco-friendly products. For example, the generation AI provides information on eco-friendly furniture and miscellaneous goods and suggests them to the user. For example, it may suggest sofas made from recycled materials or energy-efficient lighting. The furniture suggestion unit also takes into account the user's environmental awareness and suggests eco-friendly products. For example, it may list furniture and miscellaneous goods that have been certified as eco-friendly products. The furniture suggestion unit also uses a database of eco-friendly products to suggest the most suitable eco-friendly furniture and miscellaneous goods to the user. For example, it may suggest products made from sustainable materials. This makes it possible to suggest eco-friendly products.

[0041] The furniture suggestion unit can suggest DIY kits and customization options. In the furniture suggestion unit, for example, the generation AI suggests furniture and miscellaneous goods that include DIY kits and customization options. For example, it suggests furniture that the user can assemble themselves or customizable curtains. In addition, the furniture suggestion unit suggests DIY kits and customization options based on the user's preferences. For example, it lists furniture and miscellaneous goods that allow you to choose the color and material. In addition, the furniture suggestion unit provides information on DIY kits and customization options to suggest to the user. For example, it suggests furniture that you can design yourself or customizable storage items. This makes it possible to suggest DIY kits and customization options.

[0042] The coupon issuing unit can learn the user's purchasing history and issue individually customized coupons. For example, the generation AI of the coupon issuing unit learns the user's purchasing history and issues individually customized coupons. For example, it provides discount coupons related to products purchased in the past. The coupon issuing unit also issues individually customized coupons by the generation AI based on the user's purchasing history. For example, it provides discount coupons for frequently visited stores. The coupon issuing unit also analyzes the user's purchasing history and issues individually customized coupons. For example, it provides discount coupons for products and services that match the user's preferences. This makes it possible to issue optimal coupons based on the user's purchasing history.

[0043] The coupon issuing unit can provide benefits that match the user's lifestyle. For example, the generation AI in the coupon issuing unit analyzes the user's lifestyle and provides benefits that match the lifestyle. For example, discount coupons for fitness gyms or movie theater tickets are provided. The coupon issuing unit also provides customized benefits based on the user's lifestyle through the generation AI. For example, a user who loves the outdoors is provided with a discount coupon for camping equipment. The coupon issuing unit also takes the user's lifestyle into consideration and provides benefits that match the lifestyle. For example, a health-conscious user is provided with a discount coupon for a health food store. This makes it possible to provide optimal benefits based on the user's lifestyle.

[0044] The coupon issuing unit can issue coupons that include those for local small businesses and startup companies. For example, the generation AI in the coupon issuing unit collects information on local small businesses and startup companies and issues coupons based on that information. For example, it provides discount coupons for local cafes and restaurants. The coupon issuing unit also uses the generation AI to issue coupons for local small businesses and startup companies based on the user's purchasing history and interests. For example, it provides discount coupons for newly opened stores. The coupon issuing unit also uses the generation AI to analyze information on local small businesses and startup companies and customize and issue coupons based on that information. For example, it provides discount coupons for local specialties and services. This makes it possible to issue coupons that include those for local small businesses and startup companies.

[0045] The coupon issuing unit can issue coupons to which special benefits according to the season or event have been added. For example, the generation AI of the coupon issuing unit issues coupons to which special benefits according to the season or event have been added. For example, coupons for Christmas sales or summer festival discounts are provided. The coupon issuing unit also issues coupons to which special benefits according to the season or event have been added based on the user's purchasing history and interests. For example, special benefits are provided for Valentine's Day or Halloween. The coupon issuing unit also analyzes information about seasons and events using the generation AI, and customizes and issues coupons to which special benefits have been added based on that information. For example, discount coupons for seasonal products or services are provided. This makes it possible to issue coupons to which special benefits according to the season or event have been added.

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

[0047] The application system can also monitor the user's health status and add tasks for maintaining good health. For example, it can schedule regular health checkups and suggest healthy meal recipes. It can also provide information on nearby fitness gyms and yoga classes based on the user's exercise habits. Furthermore, the generative AI can propose individually customized health maintenance plans based on the user's health data. This allows it to add optimal tasks based on the user's health status.

[0048] The application system can also suggest new local activities based on the user's hobbies and interests. For example, it can provide information on nearby sports clubs and cultural facilities and add tasks to start a new hobby. It can also provide information on events and workshops that interest the user and encourage them to participate. Furthermore, the generation AI can propose individually customized activity plans based on the user's past activity history. This allows it to add optimal tasks based on the user's hobbies and interests.

[0049] The application system can also take into account the user's family structure and add tasks for the whole family. For example, tasks can be added that include school and daycare arrangements for children, or moving arrangements for pets. It can also suggest events and activities to help the whole family quickly adapt to the new environment. It can also monitor the health of each family member and add tasks to maintain their health. This allows the system to add tasks that are optimal for the needs of each family member.

[0050] The application system can also suggest the optimal means of transportation based on the user's mode of transportation. For example, it can suggest routes that take traffic congestion information into account for users who travel by car, and safe bicycle routes for users who travel by bicycle. It can also provide the fastest route and transfer information for users who travel by public transportation. Furthermore, the generation AI can propose individually customized travel plans based on the user's travel history. This makes it possible to suggest the optimal means of transportation based on the user's mode of transportation.

[0051] The application system can also learn the user's purchasing history and issue individually customized coupons. For example, it can provide discount coupons related to products purchased in the past. The generation AI can also issue individually customized coupons based on the user's purchasing history. Furthermore, it is also possible to analyze the user's purchasing history and issue individually customized coupons. This allows the system to issue optimal coupons based on the user's purchasing history.

[0052] The application system can also provide rewards tailored to the user's lifestyle. For example, it can provide discount coupons for fitness gyms or movie theater tickets. The generation AI can also customize and provide rewards based on the user's lifestyle. It can also take the user's lifestyle into consideration and provide rewards that correspond to that lifestyle. This allows the system to provide optimal rewards based on the user's lifestyle.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The address input section inputs the addresses of the user's previous and next residences. For example, information such as a postal code, prefecture, city, ward, town, or village, and street address can be input. Step 2: The TODO list generator generates a TODO list based on the address entered by the address input unit. For example, the generator receives a prompt containing instructions on what the user wants the generator to do, and generates a TODO list including the phone number to contact, the gas company, and the location of the government office where the change of address notification should be submitted. Step 3: The environmental information provider provides environmental information about the new residence based on the to-do list generated by the to-do list generator. For example, the generator receives prompts containing instructions on what the user wants the generator to do, and provides information about nearby supermarkets, convenience stores, hospitals, schools, etc. Step 4: The furniture suggestion unit suggests furniture and household goods that fit the floor plan based on the environmental information provided by the environmental information provision unit. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do, and makes specific suggestions such as, "This size sofa is suitable for the living room," or "These curtains fit perfectly with this window." Step 5: The coupon issuing unit issues coupons for nearby supermarkets and convenience stores based on the information suggested by the furniture suggestion unit. For example, the generation AI receives a prompt containing instructions on what the user wants the generation AI to do, and issues a coupon that says, "Use this coupon to get 10% off your first purchase."

[0055] (Example 2) The application system according to the embodiment of the present invention is a system that automatically provides necessary procedures and lifestyle information when a user simply inputs the addresses of their current and new residences. This allows the application system to efficiently and conveniently handle procedures associated with a job transfer or move, as well as subsequent lifestyle arrangements.

[0056] An application system according to an embodiment includes an address input unit, a to-do list generation unit, an environmental information provision unit, a furniture suggestion unit, and a coupon issuance unit. The address input unit inputs the user's addresses of their current and future residences. For example, information such as a postal code, prefecture, city, ward, town, or village, and street address can be input. The to-do list generation unit generates a to-do list based on the address input by the address input unit. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do, and generates a to-do list containing information such as phone numbers to contact, gas companies, and the location of the government office where a change of address notification should be submitted. The environmental information provision unit provides environmental information about the area surrounding the new residence based on the to-do list generated by the to-do list generation unit. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do, and provides information on nearby supermarkets, convenience stores, hospitals, schools, and the like. The furniture suggestion unit suggests furniture and household goods according to the floor plan based on the environmental information provided by the environmental information provision unit. For example, the generation AI receives prompts including instructions on what the user wants the generation AI to do, and makes specific suggestions such as, "This size sofa is suitable for the living room," or "These curtains fit perfectly with this window." The coupon issuing unit issues coupons for nearby supermarkets and convenience stores based on the information suggested by the furniture suggestion unit. For example, the generation AI receives prompts including instructions on what the user wants the generation AI to do, and issues a coupon such as, "Use this coupon to get 10% off your first purchase." This allows the application system according to the embodiment to efficiently and conveniently organize procedures for transfers and moving, as well as subsequent life.

[0057] The to-do list generation unit can learn the user's past relocation history and generate an individually customized to-do list. For example, the generation AI in the to-do list generation unit learns the user's past relocation history and generates an individually customized to-do list by taking into account the regional characteristics of the new location and the procedures that have been completed in the past. For example, it suggests procedures for the new location based on the procedures required in previous moves. The to-do list generation unit also stores the history of the user's past relocation procedures in a database, and the generation AI uses that data to generate an optimal to-do list for the next move. For example, it considers the moving companies used in the past and the order of procedures. The to-do list generation unit also analyzes the user's past relocation history and generates a customized to-do list tailored to the regional characteristics of the new location and the user's lifestyle. For example, it considers issues that occurred in previous moves and suggests tasks that include preventive measures. This allows the generation of an optimal to-do list based on the user's past relocation history.

[0058] The TODO list generation unit can add relevant tasks by taking into account the user's lifestyle and hobbies and preferences. For example, the generation AI of the TODO list generation unit learns the user's lifestyle and hobbies and preferences and adds relevant tasks to the TODO list based on that. For example, for a user who has a pet, tasks related to pet moving procedures and care are added. The TODO list generation unit also takes the user's hobbies and preferences into account and adds tasks to the TODO list for starting new hobbies and activities at the new address. For example, it provides information on nearby sports clubs and cultural facilities. The TODO list generation unit also analyzes the user's lifestyle and adds tasks to the TODO list to help them start life at their new address smoothly. For example, it provides information on nearby supermarkets and convenience stores and suggests a shopping list for the first day. This allows the generation AI to add optimal tasks based on the user's lifestyle and hobbies and preferences.

[0059] The TODO list generation unit can estimate the user's stress level using the emotion estimation function and suggest relaxation tasks to reduce stress. For example, the TODO list generation unit can use the emotion estimation function to analyze the user's stress level in real time and add relaxation tasks to the TODO list to reduce stress. For example, it can suggest time to listen to relaxing music. The TODO list generation unit can also measure the user's stress level using the emotion estimation function and, if stress is high, prioritize adding relaxation tasks to the TODO list. For example, it can suggest taking a walk in a nearby park or joining a yoga class. The TODO list generation unit can also regularly monitor the user's stress level using the emotion estimation function and automatically add tasks to reduce stress to the TODO list. For example, it can suggest booking a massage or spa appointment during times of high stress. This allows it to suggest relaxation tasks based on the user's stress level.

[0060] The TODO list generation unit can add tasks related to pet moving procedures and care. For example, the generation AI adds tasks related to pet moving procedures and care to the TODO list based on the user's pet information. For example, it suggests pet health checkups and registering a new veterinarian. Furthermore, if the user has a pet, the TODO list generation unit adds procedures necessary for pet moving to the TODO list. For example, it suggests tasks to support pet transportation and the pet's adaptation to a new home. Furthermore, the TODO list generation unit obtains information related to pet moving procedures and care from a database, and the generation AI generates a TODO list based on that information. For example, it lists the documents and procedures necessary for pet moving. This allows tasks related to pet moving procedures and care to be added.

[0061] The TODO list generation unit can add tasks related to procedures for a child's school or nursery school. For example, the generation AI adds tasks related to school or nursery school procedures to the TODO list based on information about the user's child. For example, it suggests procedures for transferring to a new school or enrolling in nursery school. Furthermore, if the user has children, the generation AI adds tasks necessary for the child's school or nursery school procedures to the TODO list. For example, it suggests visiting a school or attending an entrance information session. Furthermore, the TODO list generation unit obtains information about procedures for a child's school or nursery school from a database, and the generation AI generates a TODO list based on that information. For example, it lists the necessary documents and procedures and notifies the user. This allows tasks related to the child's school or nursery school procedures to be added.

[0062] The TODO list generation unit can use the emotion estimation function to provide positive feedback to enhance the sense of accomplishment when the user completes a task. For example, the TODO list generation unit uses the emotion estimation function to analyze the emotion of the user when completing a task and provides positive feedback to enhance the sense of accomplishment. For example, an encouraging message can be displayed when the task is completed. The TODO list generation unit also uses the emotion estimation function to provide feedback that elicits positive emotions when the user completes a task. For example, a congratulatory message or badge can be displayed after the task is completed. The TODO list generation unit also uses the emotion estimation function to monitor the emotion of the user when completing a task and provides real-time feedback to enhance the sense of accomplishment. For example, positive music or animation can be displayed when the task is completed. This makes it possible to provide positive feedback to enhance the sense of accomplishment when the user completes a task.

[0063] The environmental information providing unit can provide information on nearby fitness gyms and health food stores taking into account the user's health condition. In the environmental information providing unit, for example, the generation AI analyzes the user's health condition and provides information on nearby fitness gyms and health food stores. For example, gym plans and health food recommendations tailored to the user's health goals are displayed. In addition, the environmental information providing unit provides information on nearby fitness gyms and health food stores based on the user's health data using the generation AI. For example, store information tailored to the user's exercise habits and dietary habits is displayed. In addition, the environmental information providing unit provides customized information on nearby fitness gyms and health food stores taking into account the user's health condition using the generation AI. For example, promotional information on gym classes and health foods tailored to the user's health goals is displayed. This makes it possible to provide information on optimal fitness gyms and health food stores based on the user's health condition.

[0064] The environmental information providing unit can propose the optimal commuting route according to the user's means of transportation. In the environmental information providing unit, for example, the generation AI analyzes the user's means of transportation and proposes the optimal commuting route. For example, it proposes a route that takes traffic congestion information into account for users who use cars, and a safe bicycle path for users who use bicycles. In addition, the environmental information providing unit provides the optimal commuting route according to the user's means of transportation. For example, it displays the shortest route and transfer information for users who use public transportation. In addition, the generation AI considers the user's means of transportation and customizes the proposed commuting route. For example, it provides parking information for users who use cars, and bicycle parking information for users who use bicycles. This makes it possible to propose the optimal commuting route based on the user's means of transportation.

[0065] The environmental information providing unit can use the emotion estimation function to suggest relaxation spots to help the user reduce anxiety felt in a new environment. For example, the environmental information providing unit uses the emotion estimation function to analyze the anxiety felt by the user in a new environment and suggest relaxation spots to help reduce anxiety. For example, the environmental information providing unit can introduce nearby parks and cafes. The environmental information providing unit can also monitor the user's emotional state in real time and suggest relaxation spots if the user is feeling anxious. For example, the environmental information providing unit can provide information on hot springs and spas where the user can relax. The environmental information providing unit can also use the emotion estimation function to customize and suggest relaxation spots to help the user reduce anxiety felt in a new environment. For example, the environmental information providing unit can introduce relaxation facilities tailored to the user's preferences. This makes it possible to suggest relaxation spots to help the user reduce anxiety felt in a new environment.

[0066] The environmental information providing unit can provide information on events and community activities being held in the vicinity of the new home. For example, the generation AI provides information on events and community activities being held in the vicinity of the new home. For example, it displays information on local festivals and flea markets. The environmental information providing unit also customizes and provides information on events and community activities being held in the vicinity of the new home based on the user's interests. For example, it displays information on sporting events and cultural activities. The environmental information providing unit also retrieves information on events and community activities being held in the vicinity of the new home from a database, and the generation AI provides this information to the user. For example, it displays information on local volunteer activities and workshops. This makes it possible to provide information on events and community activities being held in the vicinity of the new home.

[0067] The environmental information providing unit can provide information on restaurants and cafes around the new home and display recommendations based on the user's food preferences. For example, the generation AI of the environmental information providing unit provides information on restaurants and cafes around the new home and displays recommendations based on the user's food preferences. For example, the generation AI can suggest restaurants based on the user's favorite cuisine genre. The environmental information providing unit also learns the user's food preferences, and the generation AI customizes and provides information on restaurants and cafes around the new home. For example, it can display restaurants that offer vegetarian and gluten-free options. The environmental information providing unit also retrieves information on restaurants and cafes around the new home from a database, and the generation AI can use that information to display recommendations based on the user's food preferences. For example, it can suggest restaurants based on user reviews and ratings. This makes it possible to provide information on restaurants and cafes around the new home and display recommendations based on the user's food preferences.

[0068] The environmental information providing unit can use the emotion estimation function to suggest social events that will help the user quickly adapt to a new environment. The environmental information providing unit, for example, uses the emotion estimation function to suggest social events that will help the user quickly adapt to a new environment. For example, it provides information about local social gatherings and parties. The environmental information providing unit also monitors the user's emotional state in real time and suggests social events that will reduce anxiety about the new environment. For example, it introduces community activities based on hobbies and interests. The environmental information providing unit also uses the emotion estimation function to customize and suggest social events that will help the user quickly adapt to a new environment. For example, it introduces events and group activities that match the user's preferences. In this way, it is possible to suggest social events that will help the user quickly adapt to a new environment.

[0069] The furniture suggestion unit can learn the user's interior style preferences and suggest furniture and miscellaneous goods based on them. For example, the generation AI of the furniture suggestion unit learns the user's interior style preferences and suggests furniture and miscellaneous goods based on them. For example, it suggests sofas and tables suitable for a living room based on the user's preferred colors and designs. The furniture suggestion unit also analyzes the user's past purchase history and interior style preferences, and the generation AI suggests furniture and miscellaneous goods based on them. For example, it makes suggestions based on the user's preferred brands and styles. The furniture suggestion unit also learns the user's interior style preferences through the generation AI and suggests the optimal furniture and miscellaneous goods for each room based on them. For example, it suggests a relaxing bed and lighting for the bedroom. This makes it possible to suggest the optimal furniture and miscellaneous goods based on the user's interior style preferences.

[0070] The furniture suggestion unit can consider the user's budget and suggest furniture and miscellaneous goods with high cost performance. For example, the generation AI considers the user's budget and suggests furniture and miscellaneous goods with high cost performance. For example, it suggests the optimal sofa or table that can be purchased within the budget. Furthermore, the furniture suggestion unit considers the user's budget and suggests furniture and miscellaneous goods with high cost performance. For example, it makes suggestions using sale information and discount coupons. Furthermore, the generation AI considers the user's budget and suggests customized furniture and miscellaneous goods with high cost performance. For example, it lists the most valuable products within the budget. This makes it possible to suggest the optimal furniture and miscellaneous goods based on the user's budget.

[0071] The furniture suggestion unit can use the emotion estimation function to suggest an interior layout for creating a space where the user can relax. The furniture suggestion unit, for example, uses the emotion estimation function to suggest an interior layout for creating a space where the user can relax. For example, it suggests colors and lighting layouts that have a relaxing effect. The furniture suggestion unit also monitors the user's emotional state in real time and suggests an interior layout for creating a space where the user can relax. For example, it suggests plants or art that have a stress-reducing effect. The furniture suggestion unit also uses the emotion estimation function to customize and suggest an interior layout for creating a space where the user can relax. For example, it suggests furniture and miscellaneous goods that have a relaxing effect that match the user's preferences. In this way, it is possible to suggest an interior layout for creating a space where the user can relax.

[0072] The furniture suggestion unit can suggest eco-friendly products. For example, the generation AI provides information on eco-friendly furniture and miscellaneous goods and suggests them to the user. For example, it may suggest sofas made from recycled materials or energy-efficient lighting. The furniture suggestion unit also takes into account the user's environmental awareness and suggests eco-friendly products. For example, it may list furniture and miscellaneous goods that have been certified as eco-friendly products. The furniture suggestion unit also uses a database of eco-friendly products to suggest the most suitable eco-friendly furniture and miscellaneous goods to the user. For example, it may suggest products made from sustainable materials. This makes it possible to suggest eco-friendly products.

[0073] The furniture suggestion unit can suggest DIY kits and customization options. In the furniture suggestion unit, for example, the generation AI suggests furniture and miscellaneous goods that include DIY kits and customization options. For example, it suggests furniture that the user can assemble themselves or customizable curtains. In addition, the furniture suggestion unit suggests DIY kits and customization options based on the user's preferences. For example, it lists furniture and miscellaneous goods that allow you to choose the color and material. In addition, the furniture suggestion unit provides information on DIY kits and customization options to suggest to the user. For example, it suggests furniture that you can design yourself or customizable storage items. This makes it possible to suggest DIY kits and customization options.

[0074] The furniture suggestion unit can use the emotion estimation function to suggest colors and materials that the user finds most comfortable. For example, the furniture suggestion unit uses the emotion estimation function to suggest colors and materials that the user finds most comfortable. For example, it suggests colors that have a relaxing effect and materials that feel good to the touch. The furniture suggestion unit also monitors the user's emotional state in real time and suggests colors and materials that the user finds most comfortable. For example, it lists colors and materials that have a stress-reducing effect. The furniture suggestion unit also uses the emotion estimation function to customize and suggest colors and materials that the user finds most comfortable. For example, it suggests colors and materials that match the user's preferences. This makes it possible to suggest colors and materials that the user finds most comfortable.

[0075] The coupon issuing unit can learn the user's purchasing history and issue individually customized coupons. For example, the generation AI of the coupon issuing unit learns the user's purchasing history and issues individually customized coupons. For example, it provides discount coupons related to products purchased in the past. The coupon issuing unit also issues individually customized coupons by the generation AI based on the user's purchasing history. For example, it provides discount coupons for frequently visited stores. The coupon issuing unit also analyzes the user's purchasing history and issues individually customized coupons. For example, it provides discount coupons for products and services that match the user's preferences. This makes it possible to issue optimal coupons based on the user's purchasing history.

[0076] The coupon issuing unit can provide benefits that match the user's lifestyle. For example, the generation AI in the coupon issuing unit analyzes the user's lifestyle and provides benefits that match the lifestyle. For example, discount coupons for fitness gyms or movie theater tickets are provided. The coupon issuing unit also provides customized benefits based on the user's lifestyle through the generation AI. For example, a user who loves the outdoors is provided with a discount coupon for camping equipment. The coupon issuing unit also takes the user's lifestyle into consideration and provides benefits that match the lifestyle. For example, a health-conscious user is provided with a discount coupon for a health food store. This makes it possible to provide optimal benefits based on the user's lifestyle.

[0077] The coupon issuing unit can use the emotion estimation function to issue a coupon for providing a benefit that the user will be most pleased with. The coupon issuing unit, for example, uses the emotion estimation function to issue a coupon for providing a benefit that the user will be most pleased with. For example, the coupon issuing unit analyzes the user's emotional state and provides the benefit that the user will be most pleased with. The coupon issuing unit also monitors the user's emotional state in real time and issues a coupon for providing the benefit that the user will be most pleased with. For example, a benefit with a high emotion score is provided preferentially. The coupon issuing unit also uses the emotion estimation function to customize and issue a coupon for providing the benefit that the user will be most pleased with. For example, a benefit tailored to the user's preferences is provided. In this way, a coupon for providing the benefit that the user will be most pleased with can be issued.

[0078] The coupon issuing unit can issue coupons that include those for local small businesses and startup companies. For example, the generation AI in the coupon issuing unit collects information on local small businesses and startup companies and issues coupons based on that information. For example, it provides discount coupons for local cafes and restaurants. The coupon issuing unit also uses the generation AI to issue coupons for local small businesses and startup companies based on the user's purchasing history and interests. For example, it provides discount coupons for newly opened stores. The coupon issuing unit also uses the generation AI to analyze information on local small businesses and startup companies and customize and issue coupons based on that information. For example, it provides discount coupons for local specialties and services. This makes it possible to issue coupons that include those for local small businesses and startup companies.

[0079] The coupon issuing unit can issue coupons to which special benefits according to the season or event have been added. For example, the generation AI of the coupon issuing unit issues coupons to which special benefits according to the season or event have been added. For example, coupons for Christmas sales or summer festival discounts are provided. The coupon issuing unit also issues coupons to which special benefits according to the season or event have been added based on the user's purchasing history and interests. For example, special benefits are provided for Valentine's Day or Halloween. The coupon issuing unit also analyzes information about seasons and events using the generation AI, and customizes and issues coupons to which special benefits have been added based on that information. For example, discount coupons for seasonal products or services are provided. This makes it possible to issue coupons to which special benefits according to the season or event have been added.

[0080] The coupon issuing unit can use the emotion estimation function to provide coupons for products and services that the user is most interested in in real time. The coupon issuing unit, for example, uses the emotion estimation function to provide coupons for products and services that the user is most interested in in real time. For example, the coupon issuing unit analyzes the user's emotional state and provides the most interesting benefits. The coupon issuing unit also monitors the user's emotional state in real time and provides coupons for products and services that the user is most interested in. For example, coupons for products and services with high emotion scores are provided preferentially. The coupon issuing unit also uses the emotion estimation function to customize and provide coupons for products and services that the user is most interested in. For example, benefits tailored to the user's preferences are provided. This makes it possible to provide coupons for products and services that the user is most interested in in real time.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The application system can also monitor the user's health status and add tasks for maintaining good health. For example, it can schedule regular health checkups and suggest healthy meal recipes. It can also provide information on nearby fitness gyms and yoga classes based on the user's exercise habits. Furthermore, the generative AI can propose individually customized health maintenance plans based on the user's health data. This allows it to add optimal tasks based on the user's health status.

[0083] The application system can also suggest new local activities based on the user's hobbies and interests. For example, it can provide information on nearby sports clubs and cultural facilities and add tasks to start a new hobby. It can also provide information on events and workshops that interest the user and encourage them to participate. Furthermore, the generation AI can propose individually customized activity plans based on the user's past activity history. This allows it to add optimal tasks based on the user's hobbies and interests.

[0084] The application system can also monitor the user's emotional state and suggest tasks based on their emotions. For example, if the user is feeling stressed, it can prioritize relaxation tasks. It can also suggest new challenges or activities if the user is feeling positive. Furthermore, it can use emotion estimation to adjust task priorities based on the user's emotional state. This allows it to suggest optimal tasks based on the user's emotional state.

[0085] The application system can also take into account the user's family structure and add tasks for the whole family. For example, tasks can be added that include school and daycare arrangements for children, or moving arrangements for pets. It can also suggest events and activities to help the whole family quickly adapt to the new environment. It can also monitor the health of each family member and add tasks to maintain their health. This allows the system to add tasks that are optimal for the needs of each family member.

[0086] The application system can also monitor the user's emotional state and provide feedback according to the user's emotions. For example, when the user completes a task, the emotion estimation function can be used to provide positive feedback. If the user is feeling stressed, the system can also suggest encouraging messages or relaxation tasks. Furthermore, the emotion estimation function can be used to customize the content of the feedback based on the user's emotional state. This allows the system to provide optimal feedback based on the user's emotional state.

[0087] The application system can also suggest the optimal means of transportation based on the user's mode of transportation. For example, it can suggest routes that take traffic congestion information into account for users who travel by car, and safe bicycle routes for users who travel by bicycle. It can also provide the fastest route and transfer information for users who travel by public transportation. Furthermore, the generation AI can propose individually customized travel plans based on the user's travel history. This makes it possible to suggest the optimal means of transportation based on the user's mode of transportation.

[0088] The application system can also monitor the user's emotional state and suggest relaxation spots to reduce anxiety in new environments. For example, it can recommend nearby parks or cafes. It can also monitor the user's emotional state in real time and prioritize relaxation spot suggestions if the user is feeling anxious. Furthermore, it can use the emotion estimation function to customize the suggested relaxation spots based on the user's emotional state. This allows it to suggest optimal relaxation spots based on the user's emotional state.

[0089] The application system can also learn the user's purchasing history and issue individually customized coupons. For example, it can provide discount coupons related to products purchased in the past. The generation AI can also issue individually customized coupons based on the user's purchasing history. Furthermore, it is also possible to analyze the user's purchasing history and issue individually customized coupons. This allows the system to issue optimal coupons based on the user's purchasing history.

[0090] The application system can also provide rewards tailored to the user's lifestyle. For example, it can provide discount coupons for fitness gyms or movie theater tickets. The generation AI can also customize and provide rewards based on the user's lifestyle. It can also take the user's lifestyle into consideration and provide rewards that correspond to that lifestyle. This allows the system to provide optimal rewards based on the user's lifestyle.

[0091] The application system can further use the emotion estimation function to issue coupons to provide the user with the most pleasing benefits. For example, the application system can analyze the user's emotional state and provide the user with the most pleasing benefits. It can also monitor the user's emotional state in real time and issue coupons to provide the user with the most pleasing benefits. Furthermore, the emotion estimation function can also be used to customize the content of the coupon based on the user's emotional state. This makes it possible to issue coupons to provide the user with the most pleasing benefits.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The address input section inputs the addresses of the user's previous and next residences. For example, information such as a postal code, prefecture, city, ward, town, or village, and street address can be input. Step 2: The TODO list generator generates a TODO list based on the address entered by the address input unit. For example, the generator receives a prompt containing instructions on what the user wants the generator to do, and generates a TODO list including the phone number to contact, the gas company, and the location of the government office where the change of address notification should be submitted. Step 3: The environmental information provider provides environmental information about the new residence based on the to-do list generated by the to-do list generator. For example, the generator receives prompts containing instructions on what the user wants the generator to do, and provides information about nearby supermarkets, convenience stores, hospitals, schools, etc. Step 4: The furniture suggestion unit suggests furniture and household goods that fit the floor plan based on the environmental information provided by the environmental information provision unit. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do, and makes specific suggestions such as, "This size sofa is suitable for the living room," or "These curtains fit perfectly with this window." Step 5: The coupon issuing unit issues coupons for nearby supermarkets and convenience stores based on the information suggested by the furniture suggestion unit. For example, the generation AI receives a prompt containing instructions on what the user wants the generation AI to do, and issues a coupon that says, "Use this coupon to get 10% off your first purchase."

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an address input section for inputting the addresses of the user's previous and next residences; a to-do list generation unit that generates a to-do list based on the address input by the address input unit; an environmental information providing unit that provides environmental information around the new residence based on the to-do list generated by the to-do list generating unit; a furniture suggestion unit that suggests furniture and household goods according to the floor plan based on the environmental information provided by the environmental information providing unit; a coupon issuing unit that issues coupons for nearby supermarkets and convenience stores based on the information suggested by the furniture suggestion unit. A system characterized by:

2. The TODO list generation unit It learns the user's past moving history and generates a personalized to-do list.

2. The system of claim 1.

3. The TODO list generation unit Add related tasks taking into account the user's lifestyle and hobbies.

2. The system of claim 1.

4. The TODO list generation unit Estimate the user's stress level and suggest relaxation tasks to reduce stress.

2. The system of claim 1.

5. The TODO list generation unit Add tasks related to pet relocation and care 2. The system of claim 1.

6. The TODO list generation unit Add tasks related to your child's school or daycare schedule 2. The system of claim 1.

7. The TODO list generation unit Providing positive feedback to enhance the user's sense of accomplishment upon completing a task 2. The system of claim 1.

8. The environmental information providing unit Taking into account the user's health condition, provide information on nearby fitness gyms and health food stores 2. The system of claim 1.

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