system

Abstract

An object of a system according to an embodiment is to unitarily manage and efficiently analyze analog and digital deposit and withdrawal records.SOLUTION: A system according to an embodiment includes a smart wallet, an API cooperation unit, and a generation AI. The smart wallet provides an analog deposit and withdrawal record. The API cooperation unit acquires a digital deposit and withdrawal record. The generation AI categorizes and analyzes the deposit and withdrawal records acquired by the smart wallet and the API cooperation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to centrally manage analog and digital deposit and withdrawal records and analyze them efficiently.

[0005] The system according to the embodiment aims to centrally manage analog and digital deposit and withdrawal records and analyze them efficiently. [Means for solving the problem]

[0006] The system according to the embodiment comprises a smart wallet, an API linkage unit, and a generation AI. The smart wallet records analog deposits and withdrawals. The API linkage unit acquires digital deposit and withdrawal records. The generation AI categorizes and analyzes the deposit and withdrawal records acquired by the smart wallet and API linkage unit. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage analog and digital deposit and withdrawal records and analyze them efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The deposit and withdrawal record management system according to an embodiment of the present invention is a system that centrally manages analog and digital deposit and withdrawal records. This system is equipped with a smart wallet, an API linkage unit, and a generation AI. As a result, the deposit and withdrawal record management system centrally manages analog and digital deposit and withdrawal records, enabling efficient tracking of expenditures.

[0029] A deposit and withdrawal record management system according to an embodiment includes a smart wallet, an API integration unit, and a generation AI. The smart wallet records analog deposits and withdrawals. For example, the smart wallet has a built-in sensor that automatically records cash deposits and withdrawals. For example, when cash is inserted into the smart wallet, the sensor detects the movement and records it as a deposit. Similarly, when cash is withdrawn, it is recorded as a withdrawal. Furthermore, the smart wallet is equipped with a GPS and can acquire location information. Based on this location information, the generation AI infers where and how the money was spent. For example, if the location information of a specific store is recorded, the expenditure is categorized as spending at that store. The smart wallet connects with an app via Bluetooth and transmits deposit and withdrawal records to the app in real time. The API integration unit acquires digital deposit and withdrawal records. For example, the app connects with various electronic money systems and financial institutions via API to automatically acquire digital deposit and withdrawal records. For example, the API integration unit acquires electronic money usage history and bank account transaction history via API and consolidates them in the app. This allows users to check all deposit and withdrawal records in a single app. The generation AI categorizes and analyzes the deposit and withdrawal records acquired by the smart wallet and API integration unit. For example, the generation AI automatically categorizes and analyzes the aggregated deposit and withdrawal records. The generation AI displays the records by category, such as food, transportation, and entertainment expenses. The generation AI also provides monthly spending trends and savings advice. The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI generates analysis results based on those prompts. As a result, the deposit and withdrawal record management system according to the embodiment centrally manages analog and digital deposit and withdrawal records, enabling efficient tracking of spending. For example, both cash and electronic money spending can be checked in a single app, making household management easier. Furthermore, analysis by the generation AI can reduce wasteful spending and enable efficient asset management.

[0030] Smart wallets can increase the number of sensors and identify the type of banknote or coin. For example, smart wallets add high-precision optical sensors to identify the type of banknote or coin. For example, they analyze the design of the banknote or the shape of the coin to accurately identify it. Smart wallets also have built-in RFID sensors to identify the type of banknote or coin by reading the chip embedded in the banknote or coin. For example, they can identify banknotes with specific RFID tags. Smart wallets also add weight sensors to identify the type of coin by measuring its weight. For example, they can accurately sort coins of different weights. This makes it possible to identify banknotes and coins.

[0031] Smart wallets can add voice recognition functionality, allowing users to record the purpose of deposits and withdrawals by voice. For example, smart wallets can incorporate a voice recognition chip, allowing users to record the purpose of deposits and withdrawals by voice. For example, simply say "food" to set the category. Smart wallets can also integrate voice recognition functionality, allowing users to enter details of deposits and withdrawals by voice command. For example, they can say, "Record 1,000 yen as food." Smart wallets can also incorporate a voice assistant, allowing the assistant to confirm and suggest when users record the purpose of deposits and withdrawals by voice. For example, they can ask, "Is this expense for food?" This allows users to record the purpose of deposits and withdrawals by voice.

[0032] Smart wallets have added biometric authentication functions, allowing users to recognize deposits and withdrawals using their fingerprints or face authentication. Smart wallets, for example, have a built-in fingerprint sensor, allowing users to make deposits and withdrawals using fingerprint authentication. For example, identity is verified by scanning a fingerprint. Smart wallets also have a built-in facial authentication camera, allowing users to make deposits and withdrawals using facial authentication. For example, authentication is completed by simply pointing their face toward the camera. Smart wallets also have added iris authentication sensors, allowing users to make deposits and withdrawals using iris authentication. For example, authentication is completed by simply bringing their eyes close to the sensor. This allows deposits and withdrawals to be recognized using biometric authentication.

[0033] Smart wallets can use sensors to display the cash balance in the wallet in real time. For example, a smart wallet may have a built-in weight sensor that measures the weight of cash and displays the balance in real time. For example, it may measure the weight of coins and calculate the balance. A smart wallet may also have an additional optical sensor that counts the number of bills and displays the balance in real time. For example, it may automatically recognize the number of bills. A smart wallet may also have a built-in RFID sensor that reads bills and coins with RFID tags and displays the balance in real time. For example, it may scan the tag to update the balance. This allows the cash balance to be displayed in real time.

[0034] API integration can obtain transaction history not only for electronic money and bank accounts, but also for credit cards and debit cards. For example, API integration can automatically obtain credit card transaction history and aggregate it into an app. For example, it can obtain transaction data using a card company's API. API integration can also obtain debit card transaction history and aggregate it into an app. For example, it can obtain debit card transaction data using a bank's API. API integration can also obtain all transaction history for electronic money, bank accounts, credit cards, and debit cards and manage it centrally. For example, it can integrate multiple APIs to aggregate data. This makes it possible to obtain credit card and debit card transaction history as well.

[0035] API integration can perform a detailed analysis of a user's purchasing history and identify individual spending patterns. For example, API integration can analyze purchasing history and identify a user's spending patterns. For example, it can find patterns where high spending occurs on specific days of the week or at specific times of the day. API integration can also classify purchasing history by category and perform a detailed analysis of a user's spending trends. For example, it can identify spending patterns by dividing the spending into categories such as food, transportation, and entertainment. API integration can also analyze purchasing history over time to identify seasonal spending patterns. For example, it can find trends where certain expenses increase in summer or winter. This makes it possible to identify individual spending patterns.

[0036] API integration allows users to share their purchasing history with other household management apps, enabling comprehensive household management. API integration, for example, allows users to share their purchasing history with other household management apps, enabling comprehensive household management. For example, data is synchronized between multiple apps. API integration also allows users to integrate with other household management apps to build a system that centrally manages users' purchasing history. For example, data is shared using an API. API integration also allows users to share their purchasing history with other household management apps in real time, supporting comprehensive household management. For example, data is updated instantly through an API. This allows comprehensive household management.

[0037] API integration can provide customized savings advice based on a user's purchasing history. For example, API integration analyzes purchasing history and provides customized savings advice to the user. For example, it can suggest ways to reduce spending in specific categories. API integration can also automatically generate individual savings plans based on the user's purchasing history and provide them within the app. For example, it can set monthly savings goals. API integration can also analyze purchasing history and build a system that provides users with optimal savings advice in real time. For example, it can provide advice based on specific spending patterns. This makes it possible to provide customized savings advice.

[0038] The generation AI can perform a detailed analysis of the user's spending patterns and make future spending predictions. For example, the generation AI analyzes the user's past spending data and makes future spending predictions. For example, it makes predictions based on monthly spending trends. The generation AI can also perform a detailed analysis of the user's spending patterns and predict future spending in specific categories. For example, it can predict food expenses and transportation expenses. The generation AI can also make future spending predictions based on the user's spending data and display them within the app. For example, it can provide a spending forecast for the next month. This makes it possible to predict future spending.

[0039] The generation AI can automatically generate a customized savings plan based on the user's spending data. For example, the generation AI analyzes the user's spending data and automatically generates a customized savings plan. For example, it suggests ways to reduce spending in specific categories. The generation AI also sets individual savings goals based on the user's spending data and suggests ways to achieve them. For example, it sets monthly savings goals and provides a specific action plan. The generation AI also updates the savings plan based on the user's spending data in real time and provides it within the app. For example, it adjusts the savings plan according to changes in spending patterns. This makes it possible to automatically generate a customized savings plan.

[0040] The generation AI can provide customized investment advice based on the user's spending data. For example, the generation AI analyzes the user's spending data and provides customized investment advice. For example, it suggests optimal investment destinations based on spending patterns. The generation AI also automatically generates an individual investment plan based on the user's spending data and provides it within the app. For example, it suggests an investment portfolio based on risk tolerance. The generation AI also updates investment advice based on the user's spending data in real time and provides it within the app. For example, it adjusts the investment plan according to market fluctuations. This makes it possible to provide customized investment advice.

[0041] The generation AI can propose customized insurance plans based on the user's expenditure data. For example, the generation AI analyzes the user's expenditure data and proposes customized insurance plans. For example, it proposes the optimal insurance product based on spending patterns. The generation AI also automatically generates individual insurance plans based on the user's expenditure data and provides them within the app. For example, it proposes insurance plans according to life stages. The generation AI also updates insurance plans based on the user's expenditure data in real time and provides them within the app. For example, it adjusts the insurance plan according to changes in spending patterns. This makes it possible to propose customized insurance plans.

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

[0043] The cash flow record management system can also collect a user's health data and analyze the correlation between spending and health status. For example, it can collect heart rate, step count, and sleep data from a smartwatch or fitness tracker to find correlations between spending patterns and health status. It can also provide spending advice based on the user's health status. For example, it can recommend relaxation-related spending during times of high stress. It can also use the health data to suggest spending plans that will help improve the user's health. For example, it can recommend gym memberships or healthy food purchases. This allows users to live a balanced life in terms of both health and finances.

[0044] The deposit and withdrawal record management system can also provide customized advertisements based on a user's purchase history. For example, it can display advertisements related to products and services that the user frequently purchases. It can also analyze purchase history and provide advertisements based on the user's interests. For example, a user who purchases many products in a particular category can be provided with information about new products and sales related to that category. It can also display advertisements at specific times based on the user's purchase history. For example, it can display relevant advertisements immediately after payday or before a specific event. This allows users to efficiently find products and services that suit them.

[0045] The cash flow record management system can also suggest customized travel plans based on the user's purchasing history. For example, it can suggest the next travel destination based on the places the user has visited in the past and the travel-related products they have purchased. It can also analyze the purchasing history and provide travel plans that suit the user's preferences. For example, it can suggest travel destinations rich in nature to a user who loves nature. It can also support the management of expenses during travel based on the user's purchasing history. For example, it can set a budget and track expenses at the travel destination. This allows the user to efficiently find a travel plan that suits them and manage their expenses during the trip.

[0046] The cash record management system can also suggest customized education plans based on the user's purchasing history. For example, it can suggest the next course to study based on the user's past purchases of education-related products and services. It can also analyze the purchasing history to provide education plans based on the user's interests. For example, if a user is interested in a particular field, it can suggest courses and learning materials related to that field. It can also support education-related expenditure management based on the user's purchasing history. For example, it can set a budget for education expenses and track expenditures. This allows users to efficiently find an education plan that suits them and manage their education-related expenditures.

[0047] The deposit and withdrawal record management system can also suggest customized hobby plans based on the user's purchasing history. For example, it can suggest new hobbies based on hobby-related products and services the user has purchased in the past. It can also analyze the purchasing history and provide hobby plans based on the user's interests. For example, it can suggest new activities and events related to a particular hobby to a user who purchases many products related to that hobby. It can also support hobby-related expenditure management based on the user's purchasing history. For example, it can set a budget for hobby expenses and track expenditures. This allows the user to efficiently find a hobby plan that suits them and manage their hobby-related expenditures.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The smart wallet records analog deposits and withdrawals. For example, a smart wallet has a built-in sensor that automatically records cash deposits and withdrawals. When cash is inserted, the sensor detects the movement and records it as a deposit, and when cash is removed, it records it as a withdrawal. Furthermore, the smart wallet is equipped with a GPS that can acquire location information. Based on this location information, the generation AI infers where and what the money was spent on. For example, if the location information of a specific store is recorded, it is categorized as spending at that store. The smart wallet connects to the app via Bluetooth and sends deposit and withdrawal records to the app in real time. Step 2: The API integration unit obtains digital deposit and withdrawal records. For example, the app can connect with various electronic money and financial institutions via API to automatically obtain digital deposit and withdrawal records. The API integration unit can obtain, for example, electronic money usage history and bank account transaction history through API and consolidate them in the app. This allows users to check all deposit and withdrawal records in a single app. Step 3: The generation AI categorizes and analyzes the deposit and withdrawal records acquired by the smart wallet and API integration unit. For example, the generation AI automatically categorizes and analyzes based on the aggregated deposit and withdrawal records. The generation AI displays the records by category, such as food expenses, transportation expenses, and entertainment expenses. The generation AI also provides monthly spending trends and savings advice. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates analysis results based on the prompt.

[0050] (Example 2) The deposit and withdrawal record management system according to an embodiment of the present invention is a system that centrally manages analog and digital deposit and withdrawal records. This system is equipped with a smart wallet, an API linkage unit, and a generation AI. As a result, the deposit and withdrawal record management system centrally manages analog and digital deposit and withdrawal records, enabling efficient tracking of expenditures.

[0051] A deposit and withdrawal record management system according to an embodiment includes a smart wallet, an API integration unit, and a generation AI. The smart wallet records analog deposits and withdrawals. For example, the smart wallet has a built-in sensor that automatically records cash deposits and withdrawals. For example, when cash is inserted into the smart wallet, the sensor detects the movement and records it as a deposit. Similarly, when cash is withdrawn, it is recorded as a withdrawal. Furthermore, the smart wallet is equipped with a GPS and can acquire location information. Based on this location information, the generation AI infers where and how the money was spent. For example, if the location information of a specific store is recorded, the expenditure is categorized as spending at that store. The smart wallet connects with an app via Bluetooth and transmits deposit and withdrawal records to the app in real time. The API integration unit acquires digital deposit and withdrawal records. For example, the app connects with various electronic money systems and financial institutions via API to automatically acquire digital deposit and withdrawal records. For example, the API integration unit acquires electronic money usage history and bank account transaction history via API and consolidates them in the app. This allows users to check all deposit and withdrawal records in a single app. The generation AI categorizes and analyzes the deposit and withdrawal records acquired by the smart wallet and API integration unit. For example, the generation AI automatically categorizes and analyzes the aggregated deposit and withdrawal records. The generation AI displays the records by category, such as food, transportation, and entertainment expenses. The generation AI also provides monthly spending trends and savings advice. The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI generates analysis results based on those prompts. As a result, the deposit and withdrawal record management system according to the embodiment centrally manages analog and digital deposit and withdrawal records, enabling efficient tracking of spending. For example, both cash and electronic money spending can be checked in a single app, making household management easier. Furthermore, analysis by the generation AI can reduce wasteful spending and enable efficient asset management.

[0052] Smart wallets can increase the number of sensors and identify the type of banknote or coin. For example, smart wallets add high-precision optical sensors to identify the type of banknote or coin. For example, they analyze the design of the banknote or the shape of the coin to accurately identify it. Smart wallets also have built-in RFID sensors to identify the type of banknote or coin by reading the chip embedded in the banknote or coin. For example, they can identify banknotes with specific RFID tags. Smart wallets also add weight sensors to identify the type of coin by measuring its weight. For example, they can accurately sort coins of different weights. This makes it possible to identify banknotes and coins.

[0053] Smart wallets can add voice recognition functionality, allowing users to record the purpose of deposits and withdrawals by voice. For example, smart wallets can incorporate a voice recognition chip, allowing users to record the purpose of deposits and withdrawals by voice. For example, simply say "food" to set the category. Smart wallets can also integrate voice recognition functionality, allowing users to enter details of deposits and withdrawals by voice command. For example, they can say, "Record 1,000 yen as food." Smart wallets can also incorporate a voice assistant, allowing the assistant to confirm and suggest when users record the purpose of deposits and withdrawals by voice. For example, they can ask, "Is this expense for food?" This allows users to record the purpose of deposits and withdrawals by voice.

[0054] A smart wallet can use its emotion estimation function to record the user's emotions when withdrawing cash, allowing it to analyze spending based on emotions. For example, a smart wallet may have a built-in camera and analyze the user's facial expressions to estimate emotions. For example, it may detect smiles or wrinkles between the eyebrows and record emotions. A smart wallet may also have an added voice analysis function to estimate emotions from the user's tone of voice and phrasing. For example, it may analyze the pitch and speed of the voice to record emotions. A smart wallet may also have a built-in biosensor and measure the user's heart rate and galvanic skin response to estimate emotions. For example, it may detect an increase in heart rate and record emotions. This makes it possible to analyze spending based on emotions.

[0055] Smart wallets have added biometric authentication functions, allowing users to recognize deposits and withdrawals using their fingerprints or face authentication. Smart wallets, for example, have a built-in fingerprint sensor, allowing users to make deposits and withdrawals using fingerprint authentication. For example, identity is verified by scanning a fingerprint. Smart wallets also have a built-in facial authentication camera, allowing users to make deposits and withdrawals using facial authentication. For example, authentication is completed by simply pointing their face toward the camera. Smart wallets also have added iris authentication sensors, allowing users to make deposits and withdrawals using iris authentication. For example, authentication is completed by simply bringing their eyes close to the sensor. This allows deposits and withdrawals to be recognized using biometric authentication.

[0056] Smart wallets can use sensors to display the cash balance in the wallet in real time. For example, a smart wallet may have a built-in weight sensor that measures the weight of cash and displays the balance in real time. For example, it may measure the weight of coins and calculate the balance. A smart wallet may also have an additional optical sensor that counts the number of bills and displays the balance in real time. For example, it may automatically recognize the number of bills. A smart wallet may also have a built-in RFID sensor that reads bills and coins with RFID tags and displays the balance in real time. For example, it may scan the tag to update the balance. This allows the cash balance to be displayed in real time.

[0057] A smart wallet can use its emotion estimation function to record the emotions felt when a user opens the wallet and make suggestions to elicit positive emotions. For example, a smart wallet may have a built-in camera and analyze the user's facial expressions to estimate emotions. For example, it may detect a smile or a surprised expression and record the emotion. A smart wallet may also have an added voice analysis function to estimate emotions from the user's tone of voice and phrasing. For example, it may analyze the pitch and speed of the voice to record the emotion. A smart wallet may also have a built-in biosensor and measure the user's heart rate and galvanic skin response to estimate emotions. For example, it may detect changes in heart rate and record the emotion. This enables suggestions to elicit positive emotions.

[0058] API integration can obtain transaction history not only for electronic money and bank accounts, but also for credit cards and debit cards. For example, API integration can automatically obtain credit card transaction history and aggregate it into an app. For example, it can obtain transaction data using a card company's API. API integration can also obtain debit card transaction history and aggregate it into an app. For example, it can obtain debit card transaction data using a bank's API. API integration can also obtain all transaction history for electronic money, bank accounts, credit cards, and debit cards and manage it centrally. For example, it can integrate multiple APIs to aggregate data. This makes it possible to obtain credit card and debit card transaction history as well.

[0059] API integration can perform a detailed analysis of a user's purchasing history and identify individual spending patterns. For example, API integration can analyze purchasing history and identify a user's spending patterns. For example, it can find patterns where high spending occurs on specific days of the week or at specific times of the day. API integration can also classify purchasing history by category and perform a detailed analysis of a user's spending trends. For example, it can identify spending patterns by dividing the spending into categories such as food, transportation, and entertainment. API integration can also analyze purchasing history over time to identify seasonal spending patterns. For example, it can find trends where certain expenses increase in summer or winter. This makes it possible to identify individual spending patterns.

[0060] The emotion estimation function records a user's emotions during digital transactions and can perform emotion-based spending analysis. For example, the emotion estimation function analyzes a user's facial expressions with a camera during digital transactions to estimate and record their emotions. For example, emotions are recorded by analyzing facial expressions while shopping online. The emotion estimation function also analyzes a user's tone of voice and choice of words during digital transactions to estimate and record their emotions. For example, emotions are analyzed through a voice assistant. The emotion estimation function also measures a user's heart rate and galvanic skin response during digital transactions to estimate and record their emotions. For example, emotions are analyzed using a wearable device. This makes it possible to perform emotion-based spending analysis.

[0061] API integration allows users to share their purchasing history with other household management apps, enabling comprehensive household management. API integration, for example, allows users to share their purchasing history with other household management apps, enabling comprehensive household management. For example, data is synchronized between multiple apps. API integration also allows users to integrate with other household management apps to build a system that centrally manages users' purchasing history. For example, data is shared using an API. API integration also allows users to share their purchasing history with other household management apps in real time, supporting comprehensive household management. For example, data is updated instantly through an API. This allows comprehensive household management.

[0062] API integration can provide customized savings advice based on a user's purchasing history. For example, API integration analyzes purchasing history and provides customized savings advice to the user. For example, it can suggest ways to reduce spending in specific categories. API integration can also automatically generate individual savings plans based on the user's purchasing history and provide them within the app. For example, it can set monthly savings goals. API integration can also analyze purchasing history and build a system that provides users with optimal savings advice in real time. For example, it can provide advice based on specific spending patterns. This makes it possible to provide customized savings advice.

[0063] The emotion estimation function can record the emotions a user feels when using specific electronic money and make suggestions to elicit positive emotions. For example, the emotion estimation function analyzes the user's facial expressions with a camera when using electronic money and estimates and records their emotions. For example, it analyzes facial expressions when making payments and records their emotions. The emotion estimation function also analyzes the user's tone of voice and choice of words when using electronic money and estimates and records their emotions. For example, it analyzes emotions through a voice assistant. The emotion estimation function also measures the user's heart rate and galvanic skin response when using electronic money and estimates and records their emotions. For example, it analyzes emotions using a wearable device. This makes it possible to make suggestions to elicit positive emotions.

[0064] The generation AI can perform a detailed analysis of the user's spending patterns and make future spending predictions. For example, the generation AI analyzes the user's past spending data and makes future spending predictions. For example, it makes predictions based on monthly spending trends. The generation AI can also perform a detailed analysis of the user's spending patterns and predict future spending in specific categories. For example, it can predict food expenses and transportation expenses. The generation AI can also make future spending predictions based on the user's spending data and display them within the app. For example, it can provide a spending forecast for the next month. This makes it possible to predict future spending.

[0065] The generation AI can automatically generate a customized savings plan based on the user's spending data. For example, the generation AI analyzes the user's spending data and automatically generates a customized savings plan. For example, it suggests ways to reduce spending in specific categories. The generation AI also sets individual savings goals based on the user's spending data and suggests ways to achieve them. For example, it sets monthly savings goals and provides a specific action plan. The generation AI also updates the savings plan based on the user's spending data in real time and provides it within the app. For example, it adjusts the savings plan according to changes in spending patterns. This makes it possible to automatically generate a customized savings plan.

[0066] The emotion estimation function can analyze a user's emotions regarding spending and make spending improvement suggestions based on their emotions. For example, the emotion estimation function analyzes a user's emotions regarding spending and makes spending improvement suggestions based on their emotions. For example, it suggests spending methods that elicit positive emotions. The emotion estimation function also analyzes a user's emotions regarding spending in real time and provides saving advice based on their emotions. For example, it makes suggestions to reduce expenses that cause negative emotions. The emotion estimation function also automatically generates spending improvement suggestions based on their emotions based on the user's emotional data regarding spending. For example, it prioritizes improvements to spending categories with high emotion scores. This makes it possible to make spending improvement suggestions based on emotions.

[0067] The generation AI can provide customized investment advice based on the user's spending data. For example, the generation AI analyzes the user's spending data and provides customized investment advice. For example, it suggests optimal investment destinations based on spending patterns. The generation AI also automatically generates an individual investment plan based on the user's spending data and provides it within the app. For example, it suggests an investment portfolio based on risk tolerance. The generation AI also updates investment advice based on the user's spending data in real time and provides it within the app. For example, it adjusts the investment plan according to market fluctuations. This makes it possible to provide customized investment advice.

[0068] The generation AI can propose customized insurance plans based on the user's expenditure data. For example, the generation AI analyzes the user's expenditure data and proposes customized insurance plans. For example, it proposes the optimal insurance product based on spending patterns. The generation AI also automatically generates individual insurance plans based on the user's expenditure data and provides them within the app. For example, it proposes insurance plans according to life stages. The generation AI also updates insurance plans based on the user's expenditure data in real time and provides them within the app. For example, it adjusts the insurance plan according to changes in spending patterns. This makes it possible to propose customized insurance plans.

[0069] The emotion estimation function can analyze a user's emotions regarding spending and make spending suggestions that elicit positive emotions. For example, the emotion estimation function analyzes a user's emotions regarding spending and makes spending suggestions that elicit positive emotions. For example, it suggests spending methods with a high emotion score. The emotion estimation function also analyzes a user's emotions regarding spending in real time and provides spending advice that elicits positive emotions. For example, it prioritizes suggestions of spending categories with a high emotion score. The emotion estimation function also automatically generates spending suggestions that elicit positive emotions based on the user's emotional data regarding spending. For example, it prioritizes suggestions of spending methods with a high emotion score. This makes it possible to make spending suggestions that elicit positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The cash flow record management system can also collect a user's health data and analyze the correlation between spending and health status. For example, it can collect heart rate, step count, and sleep data from a smartwatch or fitness tracker to find correlations between spending patterns and health status. It can also provide spending advice based on the user's health status. For example, it can recommend relaxation-related spending during times of high stress. It can also use the health data to suggest spending plans that will help improve the user's health. For example, it can recommend gym memberships or healthy food purchases. This allows users to live a balanced life in terms of both health and finances.

[0072] The deposit and withdrawal record management system can also provide customized advertisements based on a user's purchase history. For example, it can display advertisements related to products and services that the user frequently purchases. It can also analyze purchase history and provide advertisements based on the user's interests. For example, a user who purchases many products in a particular category can be provided with information about new products and sales related to that category. It can also display advertisements at specific times based on the user's purchase history. For example, it can display relevant advertisements immediately after payday or before a specific event. This allows users to efficiently find products and services that suit them.

[0073] The cash flow record management system can also suggest customized travel plans based on the user's purchasing history. For example, it can suggest the next travel destination based on the places the user has visited in the past and the travel-related products they have purchased. It can also analyze the purchasing history and provide travel plans that suit the user's preferences. For example, it can suggest travel destinations rich in nature to a user who loves nature. It can also support the management of expenses during travel based on the user's purchasing history. For example, it can set a budget and track expenses at the travel destination. This allows the user to efficiently find a travel plan that suits them and manage their expenses during the trip.

[0074] The cash record management system can also suggest customized education plans based on the user's purchasing history. For example, it can suggest the next course to study based on the user's past purchases of education-related products and services. It can also analyze the purchasing history to provide education plans based on the user's interests. For example, if a user is interested in a particular field, it can suggest courses and learning materials related to that field. It can also support education-related expenditure management based on the user's purchasing history. For example, it can set a budget for education expenses and track expenditures. This allows users to efficiently find an education plan that suits them and manage their education-related expenditures.

[0075] The deposit and withdrawal record management system can also suggest customized hobby plans based on the user's purchasing history. For example, it can suggest new hobbies based on hobby-related products and services the user has purchased in the past. It can also analyze the purchasing history and provide hobby plans based on the user's interests. For example, it can suggest new activities and events related to a particular hobby to a user who purchases many products related to that hobby. It can also support hobby-related expenditure management based on the user's purchasing history. For example, it can set a budget for hobby expenses and track expenditures. This allows the user to efficiently find a hobby plan that suits them and manage their hobby-related expenditures.

[0076] The deposit and withdrawal record management system can further estimate the user's emotions and provide purchasing advice based on the emotions. For example, if the user is feeling stressed, it can suggest relaxation-related products and services. Also, if the user is feeling positive, it can provide purchasing advice to maintain those emotions. For example, it can suggest products related to hobbies and entertainment. Furthermore, it can suggest the best time to make a purchase based on the user's emotions. For example, it can advise the user to make a major purchase when they are relaxed. In this way, the user can receive purchasing advice based on their emotions.

[0077] The deposit and withdrawal record management system can further estimate the user's emotions and provide saving advice based on the emotions. For example, if the user has negative emotions, advice to avoid wasteful spending is provided. Also, if the user has positive emotions, saving advice to maintain those emotions can be provided. For example, spending that brings out positive emotions can be recommended. Furthermore, the system can suggest the best time to save based on the user's emotions. For example, advice can be given to start saving when the user is relaxed. In this way, the user can receive saving advice based on their emotions.

[0078] The deposit and withdrawal record management system can further estimate the user's emotions and provide investment advice based on the emotions. For example, if the user feels that they are at risk, it can suggest safe investments. On the other hand, if the user has positive emotions, it can also suggest riskier investments. For example, it can recommend investments that are expected to have high returns. Furthermore, it can also suggest the timing of investments based on the user's emotions. For example, it can advise the user to start investing when they are relaxed. In this way, the user can receive investment advice based on their emotions.

[0079] The deposit and withdrawal record management system can further estimate the user's emotions and suggest insurance plans based on the emotions. For example, if the user is feeling anxious, it can suggest insurance products that provide a sense of security. Also, if the user has positive emotions, it can offer insurance plans that help maintain those emotions. For example, it can recommend health insurance or travel insurance. Furthermore, it can suggest the best time to purchase insurance based on the user's emotions. For example, it can advise the user to purchase insurance when they are relaxed. In this way, the user can receive insurance plans based on their emotions.

[0080] The deposit and withdrawal record management system can further estimate the user's emotions and suggest an education plan based on the emotions. For example, if the user is excited, it can suggest an education plan that makes use of that excitement. Also, if the user is relaxed, it can provide an education plan to maintain that state. For example, it can recommend an online course that allows the user to study in a relaxed state. Furthermore, it can suggest the best time to study based on the user's emotions. For example, it can advise the user to start studying when they are concentrating. In this way, the user can receive an education plan based on their emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The smart wallet records analog deposits and withdrawals. For example, a smart wallet has a built-in sensor that automatically records cash deposits and withdrawals. When cash is inserted, the sensor detects the movement and records it as a deposit, and when cash is removed, it records it as a withdrawal. Furthermore, the smart wallet is equipped with a GPS that can acquire location information. Based on this location information, the generation AI infers where and what the money was spent on. For example, if the location information of a specific store is recorded, it is categorized as spending at that store. The smart wallet connects to the app via Bluetooth and sends deposit and withdrawal records to the app in real time. Step 2: The API integration unit obtains digital deposit and withdrawal records. For example, the app can connect with various electronic money and financial institutions via API to automatically obtain digital deposit and withdrawal records. The API integration unit can obtain, for example, electronic money usage history and bank account transaction history through API and consolidate them in the app. This allows users to check all deposit and withdrawal records in a single app. Step 3: The generation AI categorizes and analyzes the deposit and withdrawal records acquired by the smart wallet and API integration unit. For example, the generation AI automatically categorizes and analyzes based on the aggregated deposit and withdrawal records. The generation AI displays the records by category, such as food expenses, transportation expenses, and entertainment expenses. The generation AI also provides monthly spending trends and savings advice. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates analysis results based on the prompt.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 smart wallet that records analog deposits and withdrawals, An API integration unit that acquires digital deposit and withdrawal records, and a generation AI that categorizes and analyzes the deposit and withdrawal records acquired by the smart wallet and the API linking unit. A system characterized by:

2. The smart wallet is Increase the number of sensors to identify bill types and coins 2. The system of claim 1.

3. The smart wallet is Add voice recognition functionality to allow users to record their deposit and withdrawal purposes by voice 2. The system of claim 1.

4. The smart wallet is Recording emotions when users withdraw cash and conducting spending analysis based on those emotions 2. The system of claim 1.

5. The smart wallet is Add biometric authentication functionality to recognize deposits and withdrawals using the user's fingerprint or face.

2. The system of claim 1.

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