System

Abstract

An object of a system according to an embodiment is to efficiently distribute funds for an activity in which a user applies passion.SOLUTION: A system includes an enthusiasm item selection part, a saving target setting part, an expenditure analysis part, a fund distribution proposal part, and a target achievement support part. The enthusiastic item selection unit selects an item to which the user enthusiasts. The accumulation goal setting unit sets an accumulation goal for the passion item selected by the passion item selection unit. The expenditure analysis unit analyzes the user's daily expenditure data. The budget-allocation-proposing section proposes a budget allocation plan for passionate items based on the expense data. The goal achievement support unit supports achievement of the saving goal.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 of making it difficult to efficiently allocate funds to activities that users are passionate about.

[0005] The system according to the embodiment aims to efficiently allocate funds to activities that users are passionate about. [Means for solving the problem]

[0006] The system according to the embodiment includes a passion item selection unit, a savings goal setting unit, an expenditure analysis unit, a fund allocation suggestion unit, and a goal achievement support unit. The passion item selection unit selects an item that the user is passionate about. The savings goal setting unit sets a savings goal for the passion item selected by the passion item selection unit. The expenditure analysis unit analyzes the user's daily expenditure data. The fund allocation suggestion unit suggests a fund allocation plan for the passion item based on the expenditure data. The goal achievement support unit supports the achievement of the savings goal. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently allocate funds to activities that users are passionate about. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A planning AI system according to an embodiment of the present invention is a system for allocating funds to activities that a user is passionate about. This system allows a user to select an item that they are passionate about, set a savings goal for that item, analyze their daily expenses, propose an optimal fund allocation plan, and guide them to achieve their investment goal. In this way, the planning AI system can efficiently allocate funds to activities that the user is passionate about.

[0029] A planning AI system according to an embodiment includes a passion item selection unit, a savings goal setting unit, an expenditure analysis unit, a fund allocation suggestion unit, and a goal achievement support unit. The passion item selection unit selects an item that a user is passionate about. For example, the user can select items such as travel, hobbies, and leisure. The savings goal setting unit sets a savings goal for the passion item selected by the passion item selection unit. For example, the user sets a specific goal such as "save 1 million yen to travel to Europe in a year." The expenditure analysis unit analyzes the user's daily expenditure data. For example, it identifies how much money the user spends on each item each day. The fund allocation suggestion unit proposes a fund allocation plan for the passion item based on the expenditure data. For example, it identifies items on which the user is wasting money and suggests ways to reduce those expenses. The goal achievement support unit supports the achievement of the savings goal. For example, it periodically reports the user's progress and provides advice to maintain motivation toward achieving the goal. This allows the planning AI system according to an embodiment to efficiently allocate funds to activities that the user is passionate about.

[0030] The passion item selection unit can analyze the user's past behavioral history or social media posts and automatically suggest potential passion items. For example, the passion item selection unit analyzes the user's past purchasing history and suggests passion items based on frequently purchased products and services. For example, camping and hiking are suggested for a user who purchases a lot of outdoor equipment. The passion item selection unit can also analyze the content of social media posts and suggest passion items based on the hobbies and interests that the user frequently mentions. For example, travel is suggested for a user who posts a lot of travel photos. The passion item selection unit can also analyze the user's past event participation history and suggest passion items based on the type and frequency of events attended. For example, music-related items are suggested for a user who frequently attends music festivals. In this way, potential passion items for the user can be automatically suggested.

[0031] The savings goal setting unit can dynamically set an optimal savings goal by reflecting market trends or price fluctuations related to the passion item selected by the user in real time. For example, the savings goal setting unit analyzes market trends related to the passion item selected by the user and reflects price fluctuations in real time. For example, the savings goal setting unit adjusts the savings goal based on price fluctuations of airfare and accommodation costs at a travel destination. The savings goal setting unit also collects price fluctuation data on the passion item selected by the user and suggests the optimal purchase timing. For example, the savings goal setting unit predicts when the price of a hobby gadget will fall and sets a savings goal based on that period. The savings goal setting unit also builds a system that dynamically adjusts the user's savings goal based on market trends. For example, if the price of a popular travel destination rises, the savings goal setting unit suggests increasing the savings goal. This allows market trends and price fluctuations to be reflected in real time and an optimal savings goal to be set.

[0032] The expenditure analysis unit can analyze the user's expenditure data, identify spending patterns by season or event, and build a prediction model. The expenditure analysis unit, for example, analyzes the user's past expenditure data and identifies spending patterns by season. For example, it identifies trends in which certain expenditures increase in summer or winter. The expenditure analysis unit also collects expenditure data by event and builds a prediction model. For example, it identifies patterns in which expenditures increase during events such as Christmas and birthdays. The expenditure analysis unit also develops a model to predict future expenditures based on spending patterns by season or event. For example, it predicts expenditures for the next event based on past data. This makes it possible to identify spending patterns by season or event and build a prediction model.

[0033] The fund allocation suggestion unit can simulate an optimal fund allocation plan based on the user's expenditure data and savings goals and present multiple scenarios. The fund allocation suggestion unit, for example, simulates multiple fund allocation scenarios based on the user's expenditure data and savings goals. For example, it compares different savings methods and investment strategies and presents the optimal plan. The fund allocation suggestion unit also proposes multiple fund allocation plans to the user based on the simulation results. For example, it presents a short-term savings plan and a long-term investment plan. The fund allocation suggestion unit also analyzes the user's expenditure data and builds a system that simulates an optimal fund allocation plan. For example, it proposes an optimal savings method based on spending patterns. This makes it possible to simulate an optimal fund allocation plan and present multiple scenarios.

[0034] The goal achievement support unit can periodically report the progress of the user toward their savings goal and provide advice for achieving the goal. For example, the goal achievement support unit can display success stories and reviews of other users for the passion item selected by the user to serve as reference for goal setting. For example, it can introduce the experiences and savings methods of users who have traveled to the same destination. The goal achievement support unit can also collect reviews of the passion item selected by the user and preferentially display highly rated items. For example, it can suggest optimal items based on reviews of hobby tools. The goal achievement support unit can also provide reference information for setting specific savings goals based on the success stories of other users. For example, it can introduce the savings plans of users who have achieved the same goal. In this way, it is possible to periodically report the progress toward the savings goal and provide advice to maintain motivation.

[0035] The passion item selection unit automatically suggests sub-items related to the passion item selected by the user, thereby supporting more specific goal setting. For example, the passion item selection unit automatically suggests tourist spots and activities related to the travel destination selected by the user. For example, it introduces popular tourist spots at the travel destination and local activities. The passion item selection unit also suggests sub-items related to hobbies to support specific goal setting. For example, for a user whose hobby is music, it suggests instruments and lessons. The passion item selection unit also suggests sub-items related to leisure activities to support the user's goal setting. For example, for a user whose hobby is camping, it introduces camping equipment and recommended campsites. In this way, it is possible to suggest sub-items related to the passion item selected by the user, thereby supporting specific goal setting.

[0036] The expenditure analysis unit can compare the user's expenditure data with the data of other users to set a benchmark and evaluate the efficiency of expenditure. The expenditure analysis unit, for example, compares the user's expenditure data with the data of other users to evaluate the efficiency of expenditure. For example, it determines whether the user is wasteful compared to users with the same income level. The expenditure analysis unit also sets a benchmark and builds a system to evaluate the user's expenditure pattern. For example, it identifies areas where savings can be made by comparing with average expenditure. The expenditure analysis unit also suggests efficient spending methods based on the expenditure data of other users. For example, it suggests saving methods by referring to the spending patterns of users with the same hobbies. In this way, the user's expenditure data can be compared with the data of other users to evaluate the efficiency of expenditure.

[0037] The fund allocation proposal unit can make proposals based on the user's expenditure data, taking into account fund allocation plans that have been successful for other users. The fund allocation proposal unit, for example, takes into account fund allocation plans that have been successful for other users, and proposes an optimal plan to the user. For example, the proposal is made based on a plan that has been successful for users with the same income level. The fund allocation proposal unit also builds a system that proposes fund allocation plans based on the user's expenditure data, taking into account the success stories of other users. For example, it introduces plans of users who have achieved the same goal. The fund allocation proposal unit also proposes specific fund allocation plans based on the success stories of other users. For example, it proposes a plan taking into account the success stories of users who have the same passion item. This makes it possible to make proposals based on fund allocation plans that have been successful for other users.

[0038] The expenditure analysis unit can automatically classify the user's expenditure data by category and visualize the expenditure trends for each category. For example, the expenditure analysis unit automatically classifies the user's expenditure data by category and visualizes the expenditure trends for each category using graphs and charts. For example, it classifies the data into categories such as food expenses, transportation expenses, and entertainment expenses. The expenditure analysis unit also builds a system that classifies the expenditure data by category and visualizes the expenditure ratio for each category. For example, it displays the expenditure ratio using a pie chart or bar graph. The expenditure analysis unit also visualizes the expenditure trends for each category, making it easier for the user to identify wasteful spending. For example, it displays an alert if there is a lot of spending in a particular category. This makes it possible to automatically classify the expenditure data by category and visualize the expenditure trends.

[0039] The fund allocation suggestion unit can compare fund allocation plans for different passion items based on the user's expenditure data and provide support for selecting the optimal plan. The fund allocation suggestion unit, for example, compares fund allocation plans for different passion items based on the user's expenditure data and provides support for selecting the optimal plan. For example, it compares whether funds should be allocated to travel or hobbies. The fund allocation suggestion unit also builds a system that compares fund allocation plans for different passion items and proposes the optimal plan to the user. For example, it presents multiple plans and allows the user to select one. The fund allocation suggestion unit also analyzes the user's expenditure data and provides advice for comparing fund allocation plans for different passion items. For example, it compares the advantages and disadvantages of each plan and provides suggestions. This makes it possible to compare fund allocation plans for different passion items and provide support for selecting the optimal plan.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The planning AI system can also include a health management unit that acquires the user's health data and adjusts the financial allocation plan based on the user's health condition. For example, it can analyze the user's exercise and sleep data and suggest expenditures for maintaining health. The health management unit can also collect the user's dietary data and suggest food budget allocations that take nutritional balance into consideration. Furthermore, the health management unit can monitor the user's stress level and recommend expenditures related to relaxation and mental care. This makes it possible to provide an optimal financial allocation plan based on the user's health condition.

[0042] The planning AI system can further include a social collaboration unit that analyzes the user's social network and sets joint goals with friends and family. For example, when planning a trip together with friends, each person's savings goals can be integrated and managed. The social collaboration unit can also collect spending data for the entire family and propose a financial allocation plan to help everyone achieve their goals. Furthermore, the social collaboration unit can provide advice based on the success stories of the user's friends and family, supporting the achievement of joint goals. This makes it possible to utilize the user's social network to provide a more effective financial allocation plan.

[0043] The planning AI system can further include an ecology unit that acquires the user's environmental data and proposes an environmentally friendly financial allocation plan. For example, it can analyze the user's energy consumption data and propose spending that supports an eco-friendly lifestyle. The ecology unit can also collect the user's mobility data and recommend spending that will reduce the user's carbon footprint, such as using public transportation. Furthermore, the ecology unit can prioritize environmentally friendly products and services based on the user's consumption data. This can increase the user's environmental awareness and provide a financial allocation plan that supports sustainable living.

[0044] The planning AI system can further include a learning support unit that acquires the user's learning data and proposes a funding allocation plan for skill development and career advancement. For example, it can analyze the user's learning history and interests to propose an optimal learning plan. The learning support unit can also recommend expenditures for acquiring necessary skills and qualifications based on the user's career goals. Furthermore, the learning support unit can monitor the user's learning progress and provide advice to maintain motivation. This makes it possible to provide a funding allocation plan that supports the user's skill development and career advancement.

[0045] The planning AI system may further include an event support unit that proposes a funding allocation plan for participating in specific events or activities based on the user's hobbies and interests. For example, the event support unit may analyze events and activities in which the user is interested and propose participation fees and related expenses. The event support unit may also propose optimal events and activities based on the user's past participation history. Furthermore, the event support unit may provide an efficient funding allocation plan taking into account the user's schedule. This makes it possible to provide an optimal funding allocation plan for participating in specific events and activities based on the user's hobbies and interests.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The passion item selection unit selects an item that the user is passionate about. For example, the user can select an item such as travel, hobbies, or leisure. Step 2: The savings goal setting unit sets a savings goal for the passion item selected by the passion item selection unit. For example, the user sets a specific goal such as "save 1 million yen to travel to Europe in one year." Step 3: The expenditure analysis unit analyzes the user's daily expenditure data, for example, to understand how much money the user spends on each item each day. Step 4: The investment allocation suggestion module proposes an investment plan for passion items based on the spending data. For example, it identifies items on which the user is wasting money and suggests ways to reduce those expenses. Step 5: The goal achievement support unit supports the achievement of the savings goal, for example, by periodically reporting the progress to the user and providing advice to maintain motivation to achieve the goal.

[0048] (Example 2) A planning AI system according to an embodiment of the present invention is a system for allocating funds to activities that a user is passionate about. This system allows a user to select an item that they are passionate about, set a savings goal for that item, analyze their daily expenses, propose an optimal fund allocation plan, and guide them to achieve their investment goal. In this way, the planning AI system can efficiently allocate funds to activities that the user is passionate about.

[0049] A planning AI system according to an embodiment includes a passion item selection unit, a savings goal setting unit, an expenditure analysis unit, a fund allocation suggestion unit, and a goal achievement support unit. The passion item selection unit selects an item that a user is passionate about. For example, the user can select items such as travel, hobbies, and leisure. The savings goal setting unit sets a savings goal for the passion item selected by the passion item selection unit. For example, the user sets a specific goal such as "save 1 million yen to travel to Europe in a year." The expenditure analysis unit analyzes the user's daily expenditure data. For example, it identifies how much money the user spends on each item each day. The fund allocation suggestion unit proposes a fund allocation plan for the passion item based on the expenditure data. For example, it identifies items on which the user is wasting money and suggests ways to reduce those expenses. The goal achievement support unit supports the achievement of the savings goal. For example, it periodically reports the user's progress and provides advice to maintain motivation toward achieving the goal. This allows the planning AI system according to an embodiment to efficiently allocate funds to activities that the user is passionate about.

[0050] The passion item selection unit can analyze the user's past behavioral history or social media posts and automatically suggest potential passion items. For example, the passion item selection unit analyzes the user's past purchasing history and suggests passion items based on frequently purchased products and services. For example, camping and hiking are suggested for a user who purchases a lot of outdoor equipment. The passion item selection unit can also analyze the content of social media posts and suggest passion items based on the hobbies and interests that the user frequently mentions. For example, travel is suggested for a user who posts a lot of travel photos. The passion item selection unit can also analyze the user's past event participation history and suggest passion items based on the type and frequency of events attended. For example, music-related items are suggested for a user who frequently attends music festivals. In this way, potential passion items for the user can be automatically suggested.

[0051] The savings goal setting unit can dynamically set an optimal savings goal by reflecting market trends or price fluctuations related to the passion item selected by the user in real time. For example, the savings goal setting unit analyzes market trends related to the passion item selected by the user and reflects price fluctuations in real time. For example, the savings goal setting unit adjusts the savings goal based on price fluctuations of airfare and accommodation costs at a travel destination. The savings goal setting unit also collects price fluctuation data on the passion item selected by the user and suggests the optimal purchase timing. For example, the savings goal setting unit predicts when the price of a hobby gadget will fall and sets a savings goal based on that period. The savings goal setting unit also builds a system that dynamically adjusts the user's savings goal based on market trends. For example, if the price of a popular travel destination rises, the savings goal setting unit suggests increasing the savings goal. This allows market trends and price fluctuations to be reflected in real time and an optimal savings goal to be set.

[0052] The expenditure analysis unit can analyze the user's expenditure data, identify spending patterns by season or event, and build a prediction model. The expenditure analysis unit, for example, analyzes the user's past expenditure data and identifies spending patterns by season. For example, it identifies trends in which certain expenditures increase in summer or winter. The expenditure analysis unit also collects expenditure data by event and builds a prediction model. For example, it identifies patterns in which expenditures increase during events such as Christmas and birthdays. The expenditure analysis unit also develops a model to predict future expenditures based on spending patterns by season or event. For example, it predicts expenditures for the next event based on past data. This makes it possible to identify spending patterns by season or event and build a prediction model.

[0053] The fund allocation suggestion unit can simulate an optimal fund allocation plan based on the user's expenditure data and savings goals and present multiple scenarios. The fund allocation suggestion unit, for example, simulates multiple fund allocation scenarios based on the user's expenditure data and savings goals. For example, it compares different savings methods and investment strategies and presents the optimal plan. The fund allocation suggestion unit also proposes multiple fund allocation plans to the user based on the simulation results. For example, it presents a short-term savings plan and a long-term investment plan. The fund allocation suggestion unit also analyzes the user's expenditure data and builds a system that simulates an optimal fund allocation plan. For example, it proposes an optimal savings method based on spending patterns. This makes it possible to simulate an optimal fund allocation plan and present multiple scenarios.

[0054] The goal achievement support unit can periodically report the progress of the user toward their savings goal and provide advice for achieving the goal. For example, the goal achievement support unit can display success stories and reviews of other users for the passion item selected by the user to serve as reference for goal setting. For example, it can introduce the experiences and savings methods of users who have traveled to the same destination. The goal achievement support unit can also collect reviews of the passion item selected by the user and preferentially display highly rated items. For example, it can suggest optimal items based on reviews of hobby tools. The goal achievement support unit can also provide reference information for setting specific savings goals based on the success stories of other users. For example, it can introduce the savings plans of users who have achieved the same goal. In this way, it is possible to periodically report the progress toward the savings goal and provide advice to maintain motivation.

[0055] The passion item selection unit automatically suggests sub-items related to the passion item selected by the user, thereby supporting more specific goal setting. For example, the passion item selection unit automatically suggests tourist spots and activities related to the travel destination selected by the user. For example, it introduces popular tourist spots at the travel destination and local activities. The passion item selection unit also suggests sub-items related to hobbies to support specific goal setting. For example, for a user whose hobby is music, it suggests instruments and lessons. The passion item selection unit also suggests sub-items related to leisure activities to support the user's goal setting. For example, for a user whose hobby is camping, it introduces camping equipment and recommended campsites. In this way, it is possible to suggest sub-items related to the passion item selected by the user, thereby supporting specific goal setting.

[0056] The expenditure analysis unit can compare the user's expenditure data with the data of other users to set a benchmark and evaluate the efficiency of expenditure. The expenditure analysis unit, for example, compares the user's expenditure data with the data of other users to evaluate the efficiency of expenditure. For example, it determines whether the user is wasteful compared to users with the same income level. The expenditure analysis unit also sets a benchmark and builds a system to evaluate the user's expenditure pattern. For example, it identifies areas where savings can be made by comparing with average expenditure. The expenditure analysis unit also suggests efficient spending methods based on the expenditure data of other users. For example, it suggests saving methods by referring to the spending patterns of users with the same hobbies. In this way, the user's expenditure data can be compared with the data of other users to evaluate the efficiency of expenditure.

[0057] The fund allocation proposal unit can make proposals based on the user's expenditure data, taking into account fund allocation plans that have been successful for other users. The fund allocation proposal unit, for example, takes into account fund allocation plans that have been successful for other users, and proposes an optimal plan to the user. For example, the proposal is made based on a plan that has been successful for users with the same income level. The fund allocation proposal unit also builds a system that proposes fund allocation plans based on the user's expenditure data, taking into account the success stories of other users. For example, it introduces plans of users who have achieved the same goal. The fund allocation proposal unit also proposes specific fund allocation plans based on the success stories of other users. For example, it proposes a plan taking into account the success stories of users who have the same passion item. This makes it possible to make proposals based on fund allocation plans that have been successful for other users.

[0058] The expenditure analysis unit uses the emotion estimation function to analyze the emotions of the user when making a specific expenditure, and can identify emotional wasteful spending and make suggestions to reduce it. The expenditure analysis unit, for example, analyzes the emotions of the user when making a expenditure in real time and identifies emotional wasteful spending. For example, it detects expenditures made during stress or impulse buying. The expenditure analysis unit also uses the emotion estimation function to collect emotional data when the user makes a specific expenditure and makes suggestions to reduce wasteful spending. For example, it provides advice to avoid emotional spending. The expenditure analysis unit also suggests specific methods to reduce emotional wasteful spending based on the user's emotional data. For example, it suggests ways to relax when feeling stressed. This makes it possible to identify emotional wasteful spending and make suggestions to reduce it.

[0059] The fund allocation proposal unit can use the emotion estimation function to analyze the emotions the user has toward the proposed fund allocation plan and prioritize presenting the plan that elicits the most positive emotions. The fund allocation proposal unit, for example, analyzes the emotions the user has toward the proposed plan in real time and prioritizes presenting plans that elicit the strongest positive emotions. For example, it analyzes facial expressions and voices during the proposal. The fund allocation proposal unit also uses the emotion estimation function to collect emotional data the user has toward the proposed plan and dynamically presents the plan that elicits the most positive emotions. For example, it monitors changes in emotions during the proposal and presents the optimal plan. The fund allocation proposal unit also evaluates the proposed plan based on the user's emotional data and builds a system that prioritizes presenting the plan that elicits the most positive emotions. For example, it prioritizes displaying plans with high emotion scores. This allows the emotion the user has toward the proposed plan to be analyzed and the plan that elicits the most positive emotions to be presented preferentially.

[0060] The goal achievement support unit can use the emotion estimation function to analyze the user's emotions and support the achievement of savings goals based on those emotions. For example, the goal achievement support unit monitors the user's emotions in real time when selecting passion items and preferentially suggests items associated with strong positive emotions. For example, it analyzes facial expressions and voice during selection. The goal achievement support unit also uses the emotion estimation function to provide guidance to reduce stress and anxiety felt by the user during the selection process. For example, it displays relaxing music or messages. The goal achievement support unit also provides advice in real time to optimize the selection process based on the user's emotion data. For example, it provides appropriate support when the user is unsure of what to choose. In this way, it is possible to support the achievement of savings goals based on the user's emotions.

[0061] The expenditure analysis unit can automatically classify the user's expenditure data by category and visualize the expenditure trends for each category. For example, the expenditure analysis unit automatically classifies the user's expenditure data by category and visualizes the expenditure trends for each category using graphs and charts. For example, it classifies the data into categories such as food expenses, transportation expenses, and entertainment expenses. The expenditure analysis unit also builds a system that classifies the expenditure data by category and visualizes the expenditure ratio for each category. For example, it displays the expenditure ratio using a pie chart or bar graph. The expenditure analysis unit also visualizes the expenditure trends for each category, making it easier for the user to identify wasteful spending. For example, it displays an alert if there is a lot of spending in a particular category. This makes it possible to automatically classify the expenditure data by category and visualize the expenditure trends.

[0062] The fund allocation suggestion unit can compare fund allocation plans for different passion items based on the user's expenditure data and provide support for selecting the optimal plan. The fund allocation suggestion unit, for example, compares fund allocation plans for different passion items based on the user's expenditure data and provides support for selecting the optimal plan. For example, it compares whether funds should be allocated to travel or hobbies. The fund allocation suggestion unit also builds a system that compares fund allocation plans for different passion items and proposes the optimal plan to the user. For example, it presents multiple plans and allows the user to select one. The fund allocation suggestion unit also analyzes the user's expenditure data and provides advice for comparing fund allocation plans for different passion items. For example, it compares the advantages and disadvantages of each plan and provides suggestions. This makes it possible to compare fund allocation plans for different passion items and provide support for selecting the optimal plan.

[0063] The expenditure analysis unit can use the emotion estimation function to monitor the user's emotions in real time when recording expenses and suggest an expenditure management method that elicits positive emotions. For example, the expenditure analysis unit can monitor the user's emotions in real time when recording expenses and suggest an expenditure management method that elicits positive emotions. For example, it can display a positive message when recording expenses. The expenditure analysis unit can also use the emotion estimation function to collect emotional data when the user records expenses and provide advice to elicit positive emotions. For example, it can play relaxing music when recording expenses. The expenditure analysis unit can also make specific suggestions to optimize the expenditure management method based on the user's emotional data. For example, it can provide an interface that elicits positive emotions when recording expenses. This makes it possible to monitor the user's emotions in real time when recording expenses and suggest an expenditure management method that elicits positive emotions.

[0064] The fund allocation proposal unit uses the emotion estimation function to monitor the emotions a user has toward a proposed fund allocation plan in real time and can revise the plan. The fund allocation proposal unit, for example, monitors the emotions a user has toward a proposed plan in real time and preferentially presents plans that evoke strong positive emotions. For example, it analyzes facial expressions and voices during the proposal. The fund allocation proposal unit also uses the emotion estimation function to collect emotional data about the user's emotions toward the proposed plan and dynamically presents the plan that elicits the most positive emotions. For example, it monitors changes in emotions during the proposal and presents the optimal plan. The fund allocation proposal unit also evaluates proposed plans based on the user's emotional data and builds a system that preferentially presents plans that elicit the most positive emotions. For example, it preferentially displays plans with high emotion scores. This allows the user's emotions about proposed plans to be monitored in real time and the plan to be revised.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The planning AI system can also include a health management unit that acquires the user's health data and adjusts the financial allocation plan based on the user's health condition. For example, it can analyze the user's exercise and sleep data and suggest expenditures for maintaining health. The health management unit can also collect the user's dietary data and suggest food budget allocations that take nutritional balance into consideration. Furthermore, the health management unit can monitor the user's stress level and recommend expenditures related to relaxation and mental care. This makes it possible to provide an optimal financial allocation plan based on the user's health condition.

[0067] The planning AI system can further include a social collaboration unit that analyzes the user's social network and sets joint goals with friends and family. For example, when planning a trip together with friends, each person's savings goals can be integrated and managed. The social collaboration unit can also collect spending data for the entire family and propose a financial allocation plan to help everyone achieve their goals. Furthermore, the social collaboration unit can provide advice based on the success stories of the user's friends and family, supporting the achievement of joint goals. This makes it possible to utilize the user's social network to provide a more effective financial allocation plan.

[0068] The planning AI system can further include an ecology unit that acquires the user's environmental data and proposes an environmentally friendly financial allocation plan. For example, it can analyze the user's energy consumption data and propose spending that supports an eco-friendly lifestyle. The ecology unit can also collect the user's mobility data and recommend spending that will reduce the user's carbon footprint, such as using public transportation. Furthermore, the ecology unit can prioritize environmentally friendly products and services based on the user's consumption data. This can increase the user's environmental awareness and provide a financial allocation plan that supports sustainable living.

[0069] The planning AI system can further include a learning support unit that acquires the user's learning data and proposes a funding allocation plan for skill development and career advancement. For example, it can analyze the user's learning history and interests to propose an optimal learning plan. The learning support unit can also recommend expenditures for acquiring necessary skills and qualifications based on the user's career goals. Furthermore, the learning support unit can monitor the user's learning progress and provide advice to maintain motivation. This makes it possible to provide a funding allocation plan that supports the user's skill development and career advancement.

[0070] The planning AI system may further include an event support unit that proposes a funding allocation plan for participating in specific events or activities based on the user's hobbies and interests. For example, the event support unit may analyze events and activities in which the user is interested and propose participation fees and related expenses. The event support unit may also propose optimal events and activities based on the user's past participation history. Furthermore, the event support unit may provide an efficient funding allocation plan taking into account the user's schedule. This makes it possible to provide an optimal funding allocation plan for participating in specific events and activities based on the user's hobbies and interests.

[0071] The planning AI system can further include an emotion support unit that analyzes the user's emotions and supports the achievement of savings goals based on the emotions. For example, the emotion support unit can monitor the user's emotions in real time when selecting passion items and prioritize items that evoke strong positive emotions. The emotion support unit can also provide guidance to reduce the stress and anxiety the user feels during the selection process. Furthermore, the emotion support unit can provide advice in real time to optimize the selection process based on the user's emotion data. This can support the achievement of savings goals based on the user's emotions.

[0072] The planning AI system may further include an emotion management unit that analyzes the user's emotions and suggests a spending management method based on the emotions. For example, the emotion management unit may monitor the user's emotions in real time when recording expenses and suggest a spending management method that elicits positive emotions. The emotion management unit may also collect emotional data when the user records expenses and provide advice on how to elicit positive emotions. Furthermore, the emotion management unit may make specific suggestions to optimize the spending management method based on the user's emotional data. This makes it possible to monitor the user's emotions in real time when recording expenses and suggest a spending management method that elicits positive emotions.

[0073] The planning AI system can further include an emotion allocation unit that analyzes the user's emotions and proposes a fund allocation plan based on the emotions. For example, the emotion allocation unit can analyze the user's emotions toward the proposed plans in real time and prioritize presenting plans that evoke strong positive emotions. The emotion allocation unit can also collect data on the user's emotions toward the proposed plans and dynamically present the plan that elicits the most positive emotions. Furthermore, the emotion allocation unit can construct a system that evaluates the proposed plans based on the user's emotion data and prioritizes presenting the plan that elicits the most positive emotions. This makes it possible to analyze the user's emotions toward the proposed plans and prioritize presenting the plan that elicits the most positive emotions.

[0074] The planning AI system may further include an emotion evaluation unit that analyzes the user's emotions and evaluates the efficiency of spending based on the emotions. For example, the emotion evaluation unit may analyze the user's emotions in real time when making a specific expenditure and identify emotional wasteful spending. The emotion evaluation unit may also collect emotional data when the user makes a specific expenditure and make suggestions for reducing wasteful spending. Furthermore, the emotion evaluation unit may suggest specific methods for reducing emotional wasteful spending based on the user's emotional data. This makes it possible to identify emotional wasteful spending and make suggestions for reducing it.

[0075] The planning AI system can further include an emotion modification unit that analyzes the user's emotions and modifies the fund allocation plan based on the emotions. For example, the emotion modification unit can monitor the user's emotions toward proposed plans in real time and prioritize presenting plans that evoke strong positive emotions. The emotion modification unit can also collect data on the user's emotions toward proposed plans and dynamically present plans that evoke the most positive emotions. Furthermore, the emotion modification unit can construct a system that evaluates proposed plans based on the user's emotion data and prioritizes presenting plans that evoke the most positive emotions. This allows the user's emotions toward proposed plans to be monitored in real time and the plans to be modified.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The passion item selection unit selects an item that the user is passionate about. For example, the user can select an item such as travel, hobbies, or leisure. Step 2: The savings goal setting unit sets a savings goal for the passion item selected by the passion item selection unit. For example, the user sets a specific goal such as "save 1 million yen to travel to Europe in one year." Step 3: The expenditure analysis unit analyzes the user's daily expenditure data, for example, to understand how much money the user spends on each item each day. Step 4: The investment allocation suggestion module proposes an investment plan for passion items based on the spending data. For example, it identifies items on which the user is wasting money and suggests ways to reduce those expenses. Step 5: The goal achievement support unit supports the achievement of the savings goal, for example, by periodically reporting the progress to the user and providing advice to maintain motivation to achieve the goal.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0091] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0099] The 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.

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

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

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

[0103] Fig. 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.

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

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

[0106] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0108] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] 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.

[0119] 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.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 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 planning AI system for allocating funds to activities that a user is passionate about, a passion item selection unit for selecting an item that the user is passionate about; a savings goal setting unit that sets a savings goal for the passion item selected by the passion item selection unit; an expenditure analysis unit that analyzes the user's daily expenditure data; a fund allocation proposal unit that proposes a fund allocation plan for the passion item based on the expenditure data; and a goal achievement support unit that supports the achievement of the savings goal. A system characterized by:

2. The passion item selection unit Analyzing the user's past behavioral history or social media posts to automatically suggest potential passion items 2. The system of claim 1.

3. The savings goal setting unit Dynamically setting optimal savings goals that reflect real-time market trends or price fluctuations related to the passion items selected by the user.

2. The system of claim 1.

4. The expenditure analysis unit Analyzing the spending data of the users to identify seasonal or event spending patterns and build predictive models 2. The system of claim 1.

5. The expenditure analysis unit Analyzing the emotions of the user when making a particular expenditure, identifying emotional wasteful spending, and making suggestions to reduce it 2. The system of claim 1.

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