System

Abstract

To provide a system for automatically generating a life plan and a cash flow on the basis of life data of a user, and efficiently reviewing an existing plan.SOLUTION: A system includes a life data collection part, a life plan generation part, a cash flow generation part, and a plan review part. The life data collection unit collects life data of a user. The life plan generation unit analyzes the life data collected by the life data collection unit and generates a life plan. A cash flow generation part generates cash flow on the basis of the data of income and expenditure on the basis of the life plan generated by the life plan generation part. The plan review unit manually and automatically reviews and changes an existing plan.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that generating and reviewing life plans and cash flows based on a user's lifestyle data is time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to automatically generate a life plan and cash flow based on a user's lifestyle data and to efficiently review existing plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a life data collection unit, a life plan generation unit, a cash flow generation unit, and a plan review unit. The life data collection unit collects a user's life data. The life plan generation unit analyzes the life data collected by the life data collection unit and generates a life plan. The cash flow generation unit generates a cash flow based on income and expenditure data in accordance with the life plan generated by the life plan generation unit. The plan review unit manually and automatically reviews and modifies existing plans. [Effects of the Invention]

[0007] The system according to the embodiment automatically generates a life plan and cash flow based on the user's lifestyle data, enabling the user to efficiently review existing plans. [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) A life tuning system according to an embodiment of the present invention is a system that automatically generates a life plan and cash flow on behalf of a user and manually and automatically reviews and modifies existing plans. This allows the life tuning system to analyze the user's lifestyle data and propose and generate optimal life plans and cash flows. Existing plans can also be manually and automatically reviewed and modified.

[0029] A life tuning system according to an embodiment includes a life data collection unit, a life plan generation unit, a cash flow generation unit, and a plan review unit. The life data collection unit collects a user's life data. For example, it can collect daily activity data, health data, purchase history, etc. The life data collection unit can also collect the user's income and expenditure data. The life plan generation unit analyzes the life data collected by the life data collection unit and generates a life plan. For example, the generation AI considers the balance between the user's income and expenditure and proposes specific savings plans and investment plans. The generation AI generates the life plan using a text generation AI (e.g., LLM). The generation AI can also generate the life plan using a multimodal generation AI. The cash flow generation unit generates a cash flow based on the income and expenditure data in accordance with the life plan generated by the life plan generation unit. For example, the generation AI analyzes details of monthly income and expenditure and calculates the monthly income and expenditure balance. The generation AI also predicts future income and expenditure and suggests how the user should manage their finances. The plan review unit manually and automatically reviews and modifies existing plans. For example, when a user inputs new information such as fluctuations in income or changes in family structure, the generation AI reevaluates the existing plan based on that information and proposes necessary changes. The user can also manually make changes. This allows the life tuning system according to the embodiment to automatically generate a life plan and cash flow based on the user's lifestyle data and to manually and automatically review and modify the existing plan.

[0030] The life plan generation unit can import health data and propose a life plan based on the user's health condition. For example, the generation AI in the life plan generation unit collects the user's health data from a fitness tracker and generates a life plan based on the user's health condition. For example, it proposes an exercise plan for a user who is not getting enough exercise. The life plan generation unit also analyzes the health data and proposes a life plan based on the user's health goals. For example, it provides a plan that includes a meal plan for a user who needs weight management. The life plan generation unit also monitors the user's health data in real time and dynamically adjusts the life plan according to changes in the user's health condition. For example, if the user's health condition worsens, it proposes a plan that prioritizes rest. This makes it possible to propose a life plan based on the user's health condition.

[0031] The life plan generation unit can analyze social media activity and generate a life plan based on the user's interests. For example, the generation AI analyzes the user's social media activity and generates a life plan based on the user's interests. For example, if the user is interested in traveling, it incorporates travel plans into the plan. The life plan generation unit also analyzes social media posts and suggests a life plan based on the user's interests. For example, if the user is interested in cooking, it suggests taking a cooking class. The life plan generation unit also monitors the user's social media activity in real time and dynamically adjusts the life plan according to changes in the user's interests. For example, if a new hobby is discovered, it adds that activity to the plan. This makes it possible to generate a life plan based on the user's interests.

[0032] The life plan generation unit can take in the pet's health data and simultaneously generate a life plan for the pet. For example, the life plan generation unit uses a generation AI to collect the user's pet's health data and generate a life plan for the pet. For example, it proposes exercise plans and dietary plans for the pet. The life plan generation unit also analyzes the pet's health data and proposes a life plan based on the pet's health condition. For example, if the pet is elderly, it will provide a plan that emphasizes health management. The life plan generation unit also integrates the user's and pet's health data and adjusts the life plans of both. For example, it incorporates activities that the user and pet can enjoy together into the plan. This allows the pet's life plan to be simultaneously generated based on the user's pet's health data.

[0033] The life plan generation unit can analyze data on hobbies and leisure activities and propose a life plan based on the hobbies. For example, the generation AI collects data on the user's hobbies and leisure activities and generates a life plan based on the hobbies. For example, if the user is interested in music, it suggests attending a music event. The life plan generation unit also analyzes data on hobbies and leisure activities and proposes a life plan based on the user's interests. For example, if the user likes outdoor activities, it provides a camping or hiking plan. The life plan generation unit also monitors data on the user's hobbies and leisure activities in real time and dynamically adjusts the life plan according to changes in interests. For example, if a new hobby is discovered, it adds that activity to the plan. This makes it possible to propose a life plan based on the user's hobbies and leisure activities.

[0034] The cash flow generation unit can analyze purchase history and make specific suggestions to reduce wasteful spending. For example, the cash flow generation unit uses a generation AI to analyze a user's purchase history and make specific suggestions to reduce wasteful spending. For example, it may suggest canceling unnecessary subscription services. The cash flow generation unit also analyzes the user's spending patterns based on the purchase history and provides specific advice for saving money. For example, it may suggest reviewing expensive items that are frequently purchased. The cash flow generation unit also monitors the user's purchase history in real time and dynamically makes suggestions to reduce wasteful spending. For example, it may display a warning if spending in a specific category increases. This makes it possible to make specific suggestions to reduce wasteful spending based on the user's purchase history.

[0035] The cash flow generation unit can incorporate energy consumption data and propose eco-friendly cash flows. For example, the generation AI collects a user's energy consumption data and proposes eco-friendly cash flows. For example, it recommends the purchase of energy-efficient home appliances. The cash flow generation unit also analyzes the energy consumption data and provides specific advice to optimize the user's energy usage. For example, it makes suggestions to avoid peak electricity consumption hours. The cash flow generation unit also monitors the user's energy consumption data in real time and dynamically adjusts the eco-friendly cash flows. For example, it makes suggestions for saving energy if energy consumption increases. This allows the system to propose eco-friendly cash flows based on the user's energy consumption data.

[0036] The cash flow generation unit can input travel planning data and propose a cash flow that optimizes travel expenses. For example, the cash flow generation unit uses a generation AI to collect a user's travel planning data and generate a cash flow that optimizes travel expenses. For example, it makes suggestions to optimize accommodation and transportation expenses at the travel destination. The cash flow generation unit also analyzes the travel planning data and provides specific advice to optimize the user's travel expenses. For example, it recommends traveling during the off-season. The cash flow generation unit also monitors the user's travel planning data in real time and dynamically adjusts the cash flow that optimizes travel expenses. For example, it makes suggestions to adjust the budget in response to price fluctuations at the travel destination. This makes it possible to propose a cash flow that optimizes travel expenses based on the user's travel planning data.

[0037] The cash flow generation unit can analyze education expense data and generate a cash flow that optimizes education expenses. For example, the generation AI collects the user's education expense data and generates a cash flow that optimizes education expenses. For example, it suggests the use of scholarships or educational loans. The cash flow generation unit also analyzes the education expense data and provides specific advice to optimize the user's education expenses. For example, it suggests ways to save on education expenses. The cash flow generation unit also monitors the user's education expense data in real time and dynamically adjusts the cash flow that optimizes education expenses. For example, it makes suggestions to adjust the budget in accordance with fluctuations in education expenses. This makes it possible to generate a cash flow that optimizes education expenses based on the user's education expense data.

[0038] The plan review unit can import occupational data and review the plan in accordance with career changes. For example, the generation AI collects the user's occupational data and reviews the plan in accordance with career changes. For example, the plan review unit proposes a plan that takes into account income changes due to promotion or job change. The plan review unit also analyzes the occupational data and proposes a plan review in accordance with the user's career goals. For example, it adds a skill acquisition plan for career advancement to the plan. The plan review unit also monitors the user's occupational data in real time and dynamically reviews the plan in accordance with career changes. For example, it proposes a life plan that matches working hours at a new workplace. This makes it possible to review the plan in accordance with changes in the user's career.

[0039] The plan review unit can analyze family composition data and review the plan in accordance with changes in the family. For example, the generation AI collects the user's family composition data and reviews the plan in accordance with changes in the family. For example, the plan review unit proposes a plan that takes into account increases in expenses due to the addition of a new family member. The plan review unit also analyzes the family composition data and proposes a plan review in accordance with the user's family goals. For example, the plan review unit adds a savings plan that takes into account children's education expenses to the plan. The plan review unit also monitors the user's family composition data in real time and dynamically reviews the plan in accordance with changes in the family. For example, the plan proposes a life plan in accordance with the family's health condition. This makes it possible to review the plan in accordance with changes in the user's family.

[0040] The plan review unit can import housing data and review the plan in accordance with changes in housing. For example, the plan review unit uses a generation AI to collect the user's housing data and review the plan in accordance with changes in housing. For example, it proposes a plan that takes into account increases in expenses due to moving. The plan review unit also analyzes the housing data and proposes a review of the plan in accordance with the user's housing goals. For example, it adds a plan to purchase a new home to the plan. The plan review unit also monitors the user's housing data in real time and dynamically reviews the plan in accordance with changes in housing. For example, it proposes a life plan that takes into account the maintenance costs of a new home. This makes it possible to review the plan in accordance with changes in the user's housing.

[0041] The plan review unit can analyze vehicle data and review the plan in accordance with changes in the vehicle. For example, the generation AI collects the user's vehicle data and reviews the plan in accordance with changes in the vehicle. For example, the plan review unit proposes a plan that takes into account increases in expenses associated with purchasing a new vehicle. The plan review unit also analyzes the vehicle data and proposes a plan review in accordance with the user's vehicle goals. For example, the plan review unit generates a cash flow that takes into account vehicle maintenance costs and fuel efficiency. The plan review unit also monitors the user's vehicle data in real time and dynamically reviews the plan in accordance with changes in the vehicle. For example, the plan review unit proposes a life plan that takes into account insurance premiums for a new vehicle. This makes it possible to review the plan in accordance with changes in the user's vehicle.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The life plan generation unit can also suggest specific events and activities based on the user's hobbies and interests. For example, if the user is interested in music, it can provide information about nearby concerts and music festivals. If the user is interested in sports, it can provide ticket information for local sporting events and games. Furthermore, if the user is interested in art and culture, it can provide information about special exhibitions at art galleries and museums. This can further enrich the life plan based on the user's hobbies and interests.

[0044] The life plan generation unit can input health data and propose a life plan according to the user's health condition. For example, it can propose an exercise plan to a user who is not getting enough exercise. It can also provide a plan that includes a meal plan to a user who needs to manage their weight. Furthermore, it can also propose a plan that prioritizes rest if the user's health condition deteriorates. In this way, it is possible to propose a life plan according to the user's health condition.

[0045] The life plan generation unit can analyze social media activity and generate a life plan based on the user's interests. For example, if the user is interested in traveling, it can incorporate travel plans into the plan. If the user is interested in cooking, it can also suggest that the user take a cooking class. Furthermore, if the user discovers a new hobby, it can add that activity to the plan. In this way, a life plan can be generated based on the user's interests.

[0046] The life plan generation unit can input the pet's health data and simultaneously generate a life plan for the pet. For example, it can propose an exercise plan or a diet plan for the pet. It can also provide a plan that emphasizes health management for elderly pets. It can also incorporate activities that the user and the pet can enjoy together into the plan. This allows a life plan for the pet to be simultaneously generated based on the user's pet's health data.

[0047] The life plan generation unit can analyze data on hobbies and leisure activities and propose a life plan based on the user's hobbies. For example, if the user is interested in music, it can suggest participation in a music event. If the user likes outdoor activities, it can also provide plans for camping and hiking. Furthermore, if a new hobby is discovered, it can also add that activity to the plan. This makes it possible to propose a life plan based on the user's hobbies and leisure activities.

[0048] The cash flow generation unit can analyze purchase history and make specific suggestions to reduce wasteful spending. For example, it can suggest canceling unnecessary subscription services. It can also suggest reviewing expensive items that are frequently purchased. It can also display a warning if spending in a specific category increases. This makes it possible to make specific suggestions to reduce wasteful spending based on the user's purchase history.

[0049] The cash flow generation unit can input energy consumption data and propose eco-friendly cash flows. For example, it can recommend the purchase of energy-efficient home appliances. It can also make suggestions to avoid peak power consumption times. It can also make suggestions for saving energy when energy consumption increases. In this way, it is possible to propose eco-friendly cash flows based on the user's energy consumption data.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The lifestyle data collection unit collects lifestyle data of the user, such as daily activity data, health data, purchase history, and income and expenditure data. Step 2: The life plan generation unit analyzes the life data collected by the life data collection unit and generates a life plan. For example, the generation AI considers the balance between the user's income and expenses and proposes specific savings and investment plans. The generation AI generates the life plan using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The cash flow generation unit generates cash flow from income and expenditure data based on the life plan generated by the life plan generation unit. For example, the generation AI analyzes monthly income and expenditure details and calculates monthly income and expenditure balances. The generation AI also predicts future income and expenditures and suggests how the user should manage their finances. Step 4: The Plan Review Unit reviews and modifies existing plans manually and automatically. For example, when a user inputs new information, such as changes in income or family structure, the Generative AI reevaluates the existing plan and suggests necessary changes. Users can also manually make changes.

[0052] (Example 2) A life tuning system according to an embodiment of the present invention is a system that automatically generates a life plan and cash flow on behalf of a user and manually and automatically reviews and modifies existing plans. This allows the life tuning system to analyze the user's lifestyle data and propose and generate optimal life plans and cash flows. Existing plans can also be manually and automatically reviewed and modified.

[0053] A life tuning system according to an embodiment includes a life data collection unit, a life plan generation unit, a cash flow generation unit, and a plan review unit. The life data collection unit collects a user's life data. For example, it can collect daily activity data, health data, purchase history, etc. The life data collection unit can also collect the user's income and expenditure data. The life plan generation unit analyzes the life data collected by the life data collection unit and generates a life plan. For example, the generation AI considers the balance between the user's income and expenditure and proposes specific savings plans and investment plans. The generation AI generates the life plan using a text generation AI (e.g., LLM). The generation AI can also generate the life plan using a multimodal generation AI. The cash flow generation unit generates a cash flow based on the income and expenditure data in accordance with the life plan generated by the life plan generation unit. For example, the generation AI analyzes details of monthly income and expenditure and calculates the monthly income and expenditure balance. The generation AI also predicts future income and expenditure and suggests how the user should manage their finances. The plan review unit manually and automatically reviews and modifies existing plans. For example, when a user inputs new information such as fluctuations in income or changes in family structure, the generation AI reevaluates the existing plan based on that information and proposes necessary changes. The user can also manually make changes. This allows the life tuning system according to the embodiment to automatically generate a life plan and cash flow based on the user's lifestyle data and to manually and automatically review and modify the existing plan.

[0054] The life plan generation unit can analyze emotional data and adjust the life plan based on emotional fluctuations. For example, the generation AI in the life plan generation unit collects the user's emotional data and analyzes emotional fluctuations. For example, if the user is feeling stressed, the generation AI can incorporate relaxing activities into the life plan based on that data. The life plan generation unit also analyzes the user's emotional data in real time and dynamically adjusts the life plan according to emotional fluctuations. For example, when the user is feeling positive, the life plan generation unit can set challenging goals. The life plan generation unit also suggests a life plan that is optimal for the user's emotional state based on the emotional data. For example, if the user is feeling down, the life plan generation unit can add activities to lift their mood to the plan. This makes it possible to adjust the life plan based on the user's emotional fluctuations.

[0055] The life plan generation unit can import health data and propose a life plan based on the user's health condition. For example, the generation AI in the life plan generation unit collects the user's health data from a fitness tracker and generates a life plan based on the user's health condition. For example, it proposes an exercise plan for a user who is not getting enough exercise. The life plan generation unit also analyzes the health data and proposes a life plan based on the user's health goals. For example, it provides a plan that includes a meal plan for a user who needs weight management. The life plan generation unit also monitors the user's health data in real time and dynamically adjusts the life plan according to changes in the user's health condition. For example, if the user's health condition worsens, it proposes a plan that prioritizes rest. This makes it possible to propose a life plan based on the user's health condition.

[0056] The life plan generation unit can analyze social media activity and generate a life plan based on the user's interests. For example, the generation AI analyzes the user's social media activity and generates a life plan based on the user's interests. For example, if the user is interested in traveling, it incorporates travel plans into the plan. The life plan generation unit also analyzes social media posts and suggests a life plan based on the user's interests. For example, if the user is interested in cooking, it suggests taking a cooking class. The life plan generation unit also monitors the user's social media activity in real time and dynamically adjusts the life plan according to changes in the user's interests. For example, if a new hobby is discovered, it adds that activity to the plan. This makes it possible to generate a life plan based on the user's interests.

[0057] The life plan generation unit can take in the pet's health data and simultaneously generate a life plan for the pet. For example, the life plan generation unit uses a generation AI to collect the user's pet's health data and generate a life plan for the pet. For example, it proposes exercise plans and dietary plans for the pet. The life plan generation unit also analyzes the pet's health data and proposes a life plan based on the pet's health condition. For example, if the pet is elderly, it will provide a plan that emphasizes health management. The life plan generation unit also integrates the user's and pet's health data and adjusts the life plans of both. For example, it incorporates activities that the user and pet can enjoy together into the plan. This allows the pet's life plan to be simultaneously generated based on the user's pet's health data.

[0058] The life plan generation unit can analyze data on hobbies and leisure activities and propose a life plan based on the hobbies. For example, the generation AI collects data on the user's hobbies and leisure activities and generates a life plan based on the hobbies. For example, if the user is interested in music, it suggests attending a music event. The life plan generation unit also analyzes data on hobbies and leisure activities and proposes a life plan based on the user's interests. For example, if the user likes outdoor activities, it provides a camping or hiking plan. The life plan generation unit also monitors data on the user's hobbies and leisure activities in real time and dynamically adjusts the life plan according to changes in interests. For example, if a new hobby is discovered, it adds that activity to the plan. This makes it possible to propose a life plan based on the user's hobbies and leisure activities.

[0059] The life plan generation unit uses the emotion estimation function to analyze the emotional response of the user when viewing a life plan and can propose a plan that elicits positive emotions. The life plan generation unit, for example, uses the emotion estimation function to analyze the emotional response of the user when viewing a life plan in real time. For example, if the user feels positive emotions when viewing a plan, the life plan generation unit preferentially proposes that plan. The life plan generation unit also generates a life plan that elicits positive emotions based on the user's emotional response data. For example, the life plan generation unit proposes a plan that includes many activities that the user can enjoy. The life plan generation unit also collects emotion estimation data in real time and dynamically adjusts the life plan according to the user's emotional response. For example, the life plan generation unit modifies a plan that elicits negative emotions and changes it to a plan that elicits positive emotions. This makes it possible to propose a plan that elicits positive emotions when the user views the life plan.

[0060] The cash flow generation unit can analyze purchase history and make specific suggestions to reduce wasteful spending. For example, the cash flow generation unit uses a generation AI to analyze a user's purchase history and make specific suggestions to reduce wasteful spending. For example, it may suggest canceling unnecessary subscription services. The cash flow generation unit also analyzes the user's spending patterns based on the purchase history and provides specific advice for saving money. For example, it may suggest reviewing expensive items that are frequently purchased. The cash flow generation unit also monitors the user's purchase history in real time and dynamically makes suggestions to reduce wasteful spending. For example, it may display a warning if spending in a specific category increases. This makes it possible to make specific suggestions to reduce wasteful spending based on the user's purchase history.

[0061] The cash flow generation unit can incorporate energy consumption data and propose eco-friendly cash flows. For example, the generation AI collects a user's energy consumption data and proposes eco-friendly cash flows. For example, it recommends the purchase of energy-efficient home appliances. The cash flow generation unit also analyzes the energy consumption data and provides specific advice to optimize the user's energy usage. For example, it makes suggestions to avoid peak electricity consumption hours. The cash flow generation unit also monitors the user's energy consumption data in real time and dynamically adjusts the eco-friendly cash flows. For example, it makes suggestions for saving energy if energy consumption increases. This allows the system to propose eco-friendly cash flows based on the user's energy consumption data.

[0062] The cash flow generation unit can input travel planning data and propose a cash flow that optimizes travel expenses. For example, the cash flow generation unit uses a generation AI to collect a user's travel planning data and generate a cash flow that optimizes travel expenses. For example, it makes suggestions to optimize accommodation and transportation expenses at the travel destination. The cash flow generation unit also analyzes the travel planning data and provides specific advice to optimize the user's travel expenses. For example, it recommends traveling during the off-season. The cash flow generation unit also monitors the user's travel planning data in real time and dynamically adjusts the cash flow that optimizes travel expenses. For example, it makes suggestions to adjust the budget in response to price fluctuations at the travel destination. This makes it possible to propose a cash flow that optimizes travel expenses based on the user's travel planning data.

[0063] The cash flow generation unit can analyze education expense data and generate a cash flow that optimizes education expenses. For example, the generation AI collects the user's education expense data and generates a cash flow that optimizes education expenses. For example, it suggests the use of scholarships or educational loans. The cash flow generation unit also analyzes the education expense data and provides specific advice to optimize the user's education expenses. For example, it suggests ways to save on education expenses. The cash flow generation unit also monitors the user's education expense data in real time and dynamically adjusts the cash flow that optimizes education expenses. For example, it makes suggestions to adjust the budget in accordance with fluctuations in education expenses. This makes it possible to generate a cash flow that optimizes education expenses based on the user's education expense data.

[0064] The cash flow generation unit can use the emotion estimation function to analyze the emotional response of the user when viewing the cash flow and propose a cash flow that elicits positive emotions. The cash flow generation unit, for example, uses the emotion estimation function to analyze the emotional response of the user when viewing the cash flow in real time. For example, if the user feels positive emotions when viewing the cash flow, the cash flow generation unit preferentially proposes that plan. The cash flow generation unit also generates a cash flow that elicits positive emotions based on the user's emotional response data. For example, it proposes a savings plan that gives the user a sense of security. The cash flow generation unit also collects emotion estimation data in real time and dynamically adjusts the cash flow according to the user's emotional response. For example, it modifies a plan that elicits negative emotions and changes it to a plan that elicits positive emotions. This makes it possible to propose a cash flow that elicits positive emotions when the user views the cash flow.

[0065] The plan review unit can analyze emotional data and revise existing plans based on emotional fluctuations. For example, the plan review unit uses a generation AI to collect the user's emotional data and analyze emotional fluctuations. For example, if the user is feeling stressed, the plan review unit revises the existing plan based on that data and adds activities that will help them relax. The plan review unit also analyzes the user's emotional data in real time and dynamically revises existing plans in accordance with emotional fluctuations. For example, when the user is feeling positive, it sets challenging goals. The plan review unit also suggests revising existing plans that are optimal for the user's emotional state based on the emotional data. For example, if the user is feeling down, it adds activities to the plan that will lift their mood. This makes it possible to revise existing plans based on the user's emotional fluctuations.

[0066] The plan review unit can import occupational data and review the plan in accordance with career changes. For example, the generation AI collects the user's occupational data and reviews the plan in accordance with career changes. For example, the plan review unit proposes a plan that takes into account income changes due to promotion or job change. The plan review unit also analyzes the occupational data and proposes a plan review in accordance with the user's career goals. For example, it adds a skill acquisition plan for career advancement to the plan. The plan review unit also monitors the user's occupational data in real time and dynamically reviews the plan in accordance with career changes. For example, it proposes a life plan that matches working hours at a new workplace. This makes it possible to review the plan in accordance with changes in the user's career.

[0067] The plan review unit can analyze family composition data and review the plan in accordance with changes in the family. For example, the generation AI collects the user's family composition data and reviews the plan in accordance with changes in the family. For example, the plan review unit proposes a plan that takes into account increases in expenses due to the addition of a new family member. The plan review unit also analyzes the family composition data and proposes a plan review in accordance with the user's family goals. For example, the plan review unit adds a savings plan that takes into account children's education expenses to the plan. The plan review unit also monitors the user's family composition data in real time and dynamically reviews the plan in accordance with changes in the family. For example, the plan proposes a life plan in accordance with the family's health condition. This makes it possible to review the plan in accordance with changes in the user's family.

[0068] The plan review unit can import housing data and review the plan in accordance with changes in housing. For example, the plan review unit uses a generation AI to collect the user's housing data and review the plan in accordance with changes in housing. For example, it proposes a plan that takes into account increases in expenses due to moving. The plan review unit also analyzes the housing data and proposes a review of the plan in accordance with the user's housing goals. For example, it adds a plan to purchase a new home to the plan. The plan review unit also monitors the user's housing data in real time and dynamically reviews the plan in accordance with changes in housing. For example, it proposes a life plan that takes into account the maintenance costs of a new home. This makes it possible to review the plan in accordance with changes in the user's housing.

[0069] The plan review unit can analyze vehicle data and review the plan in accordance with changes in the vehicle. For example, the generation AI collects the user's vehicle data and reviews the plan in accordance with changes in the vehicle. For example, the plan review unit proposes a plan that takes into account increases in expenses associated with purchasing a new vehicle. The plan review unit also analyzes the vehicle data and proposes a plan review in accordance with the user's vehicle goals. For example, the plan review unit generates a cash flow that takes into account vehicle maintenance costs and fuel efficiency. The plan review unit also monitors the user's vehicle data in real time and dynamically reviews the plan in accordance with changes in the vehicle. For example, the plan review unit proposes a life plan that takes into account insurance premiums for a new vehicle. This makes it possible to review the plan in accordance with changes in the user's vehicle.

[0070] The plan review unit uses the emotion estimation function to analyze the emotional response of the user when they review a plan and can suggest revisions that will elicit positive emotions. The plan review unit, for example, uses the emotion estimation function to analyze the emotional response of the user when they review a plan in real time. For example, if the user feels positive emotions when looking at a revised plan, the plan review unit preferentially suggests that plan. The plan review unit also suggests plan revisions that will elicit positive emotions based on the user's emotional response data. For example, it suggests revisions that will give the user a sense of security. The plan review unit also collects emotion estimation data in real time and dynamically adjusts the plan review according to the user's emotional response. For example, it modifies a plan that elicits negative emotions and changes it to a plan that elicits positive emotions. This makes it possible to suggest revisions that will elicit positive emotions when the user reviews a plan.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The life plan generation unit can also suggest specific events and activities based on the user's hobbies and interests. For example, if the user is interested in music, it can provide information about nearby concerts and music festivals. If the user is interested in sports, it can provide ticket information for local sporting events and games. Furthermore, if the user is interested in art and culture, it can provide information about special exhibitions at art galleries and museums. This can further enrich the life plan based on the user's hobbies and interests.

[0073] The life plan generation unit can analyze the user's emotional data and adjust the life plan based on the user's emotional fluctuations. For example, if the user is feeling stressed, the life plan can incorporate relaxing activities into the life plan based on the data. Also, when the user is feeling positive, it can set challenging goals. Furthermore, if the user is feeling depressed, it can add activities to lift their spirits to the plan. In this way, the life plan can be adjusted based on the user's emotional fluctuations.

[0074] The life plan generation unit can input health data and propose a life plan according to the user's health condition. For example, it can propose an exercise plan to a user who is not getting enough exercise. It can also provide a plan that includes a meal plan to a user who needs to manage their weight. Furthermore, it can also propose a plan that prioritizes rest if the user's health condition deteriorates. In this way, it is possible to propose a life plan according to the user's health condition.

[0075] The life plan generation unit can analyze social media activity and generate a life plan based on the user's interests. For example, if the user is interested in traveling, it can incorporate travel plans into the plan. If the user is interested in cooking, it can also suggest that the user take a cooking class. Furthermore, if the user discovers a new hobby, it can add that activity to the plan. In this way, a life plan can be generated based on the user's interests.

[0076] The life plan generation unit can input the pet's health data and simultaneously generate a life plan for the pet. For example, it can propose an exercise plan or a diet plan for the pet. It can also provide a plan that emphasizes health management for elderly pets. It can also incorporate activities that the user and the pet can enjoy together into the plan. This allows a life plan for the pet to be simultaneously generated based on the user's pet's health data.

[0077] The life plan generation unit can analyze data on hobbies and leisure activities and propose a life plan based on the user's hobbies. For example, if the user is interested in music, it can suggest participation in a music event. If the user likes outdoor activities, it can also provide plans for camping and hiking. Furthermore, if a new hobby is discovered, it can also add that activity to the plan. This makes it possible to propose a life plan based on the user's hobbies and leisure activities.

[0078] The life plan generation unit uses the emotion estimation function to analyze the emotional response of the user when viewing a life plan, and can propose plans that elicit positive emotions. For example, if the user feels positive emotions when viewing a plan, the unit will preferentially propose that plan. It can also propose plans that include many activities that the user will enjoy. Furthermore, it can also modify plans that elicit negative emotions and change them into plans that elicit positive emotions. This makes it possible to propose plans that elicit positive emotions when the user views a life plan.

[0079] The cash flow generation unit can analyze purchase history and make specific suggestions to reduce wasteful spending. For example, it can suggest canceling unnecessary subscription services. It can also suggest reviewing expensive items that are frequently purchased. It can also display a warning if spending in a specific category increases. This makes it possible to make specific suggestions to reduce wasteful spending based on the user's purchase history.

[0080] The cash flow generation unit can input energy consumption data and propose eco-friendly cash flows. For example, it can recommend the purchase of energy-efficient home appliances. It can also make suggestions to avoid peak power consumption times. It can also make suggestions for saving energy when energy consumption increases. In this way, it is possible to propose eco-friendly cash flows based on the user's energy consumption data.

[0081] The cash flow generation unit uses the emotion estimation function to analyze the emotional response of the user when looking at the cash flow, and can propose a cash flow that elicits positive emotions. For example, if the user feels positive emotions when looking at the cash flow, that plan is preferentially proposed. It can also propose a savings plan that gives the user a sense of security. Furthermore, it can also modify a plan that elicits negative emotions and change it to a plan that elicits positive emotions. In this way, it can propose a cash flow that elicits positive emotions when the user looks at the cash flow.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The lifestyle data collection unit collects lifestyle data of the user, such as daily activity data, health data, purchase history, and income and expenditure data. Step 2: The life plan generation unit analyzes the life data collected by the life data collection unit and generates a life plan. For example, the generation AI considers the balance between the user's income and expenses and proposes specific savings and investment plans. The generation AI generates the life plan using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The cash flow generation unit generates cash flow from income and expenditure data based on the life plan generated by the life plan generation unit. For example, the generation AI analyzes monthly income and expenditure details and calculates monthly income and expenditure balances. The generation AI also predicts future income and expenditures and suggests how the user should manage their finances. Step 4: The Plan Review Unit reviews and modifies existing plans manually and automatically. For example, when a user inputs new information, such as changes in income or family structure, the Generative AI reevaluates the existing plan and suggests necessary changes. Users can also manually make changes.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a lifestyle data collection unit that collects lifestyle data of a user; a life plan generation unit that analyzes the life data collected by the life data collection unit and generates a life plan; a cash flow generation unit that generates a cash flow based on income and expenditure data in accordance with the life plan generated by the life plan generation unit; and a plan review unit that manually and automatically reviews and modifies existing plans. A system characterized by:

2. The life plan generation unit Analyzing emotional data and adjusting the life plan based on emotional fluctuations 2. The system of claim 1.

3. The life plan generation unit Collect health data and propose the above-mentioned life plan according to health status 2. The system of claim 1.

4. The life plan generation unit Analyzing social media activity and generating the life plan based on the user's interests 2. The system of claim 1.

5. The life plan generation unit Importing pet health data and simultaneously generating a life plan for your pet 2. The system of claim 1.

6. The life plan generation unit Analyze data on hobbies and leisure activities and propose a life plan based on hobbies 2. The system of claim 1.

7. The life plan generation unit Analyze the emotional response of users when they view the life plan and propose a plan that elicits positive emotions.

2. The system of claim 1.

8. The cash flow generation unit Analyze purchasing history and provide specific suggestions to reduce waste 2. The system of claim 1.

Images