system

Abstract

We provide the system. [Solution] A means for inputting user living expense data using an information processing device and storing said data in a database, A means for classifying living expense data stored by an information processing device into categories and analyzing spending trends, A means of generating proposals for optimal economic activities by utilizing external information based on analysis results using an information processing device, A means for communicating a proposal generated by an information processing device to the user and obtaining the user's approval of the proposal, A means of automating payments based on user-approved proposals using an information processing device, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 There is a problem that many individuals cannot efficiently manage their expenses and select the optimal payment method or timing in their daily lives. As a result, savings that could be made are overlooked, and economic benefits are not fully enjoyed. Also, collecting and processing such information appropriately is time-consuming, so there is a problem of a large burden on users. 【Means for Solving the Problems】 【0005】 This invention provides a system that uses an information processing device to input user spending data and store it in a database. This system has the function of classifying the stored spending data by category and analyzing spending trends. Furthermore, based on the analysis results, it can generate suggestions for optimal economic activities using external information and communicate them to the user. If the user approves the suggestion, the system automates the specified payments, thereby achieving efficient spending management. 【0006】 An "information processing device" refers to a combination of hardware and software that performs data input from a user, data processing, and output. 【0007】 "Living expense data" refers to information that details all financial transactions an individual makes in their daily life, including purchases, services used, payment methods, dates, and amounts. 【0008】 A "database" refers to an information system that stores data in a structured format and allows for quick access, management, and updating as needed. 【0009】 "Category classification" refers to the process of grouping data with similar characteristics and features, and consolidating the data belonging to each category. 【0010】 "Spending trends" refer to the tendencies and changes in spending that can be derived from past recorded spending patterns. 【0011】 "External information" refers to additional information obtained from sources other than the user's spending data, and includes market-related information such as discounts, campaigns, and promotions. 【0012】 "Optimal economic activity suggestions" refer to recommendations that, based on individual user data and external information, indicate the most economical and effective ways and timings for spending. 【0013】 "Automating payments" refers to a process where payments are executed based on set conditions without requiring manual intervention. [Brief explanation of the drawing] 【0014】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined. 【Embodiments for Carrying Out the Invention】 【0015】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like. 【0018】 In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0020】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0021】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 As shown in Figure 1, the 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. 【0025】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0026】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0027】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0029】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0032】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0033】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0034】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0035】 One embodiment of the present invention is an information processing system that efficiently collects and analyzes a user's living expenses data and provides optimal payment suggestions and automated payments. This system operates around an information processing device and supports a series of processes from user input to output. 【0036】 First, the user enters their personal spending data into the terminal. This data includes the date, purchased items, payment method, and amount. The terminal formats the entered data and sends it to the server. The server stores the received data in a database and categorizes it. 【0037】 Next, the server analyzes spending data categorized by type to reveal past spending trends. Furthermore, the server acquires discount and campaign data as external information via the network. This enables the server to suggest optimal economic activities that link the user's spending patterns with market opportunities. 【0038】 The server notifies the user of the generated proposal via the terminal. The user can review the proposal on the terminal and approve or modify its contents. If the user approves the proposal, the automated payment process begins. 【0039】 As a concrete example, consider a user who wants to reduce their monthly grocery spending. This system analyzes past grocery spending and suggests discount campaigns that take place on specific days of the week. It can also indicate that paying by credit card is the most effective method. Once the user approves this suggestion, the system automates payments on those days from then on, achieving the user's desired savings. 【0040】 Thus, the system realized by the present invention reduces the user's financial burden by streamlining the user's living expenses and providing an optimized payment plan. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The user enters their living expense data into the terminal. This includes the payment date, purchased items, payment method used, and amount. The terminal formats the entered data and prepares it for transmission to the server. 【0044】 Step 2: 【0045】 The terminal sends the formatted data to the server. The server stores the received data in a database. During storage, it checks the consistency and completeness of the data and verifies that there are no inconsistencies. 【0046】 Step 3: 【0047】 The server retrieves the stored data and classifies it by category. Expenditure data is divided into categories such as food, transportation, and entertainment, and the expenditure amount for each category is aggregated. 【0048】 Step 4: 【0049】 The server analyzes spending trends using data categorized by type. It compares spending over the past few months to identify seasonal variations and specific spending patterns. 【0050】 Step 5: 【0051】 The server retrieves discount and promotional information from external sources via the network. This information is then combined with the user's spending data to generate suggestions for optimal economic activity. 【0052】 Step 6: 【0053】 The server sends the generated suggestions to the terminal. The terminal notifies the user, and the suggestions are displayed on the dashboard. 【0054】 Step 7: 【0055】 The user reviews the proposal on their device and approves, modifies, or rejects it. If approved, that information is sent to the server. 【0056】 Step 8: 【0057】 If the user approves the proposal, the server will configure the automated payment settings. According to the settings, payments will be automatically executed when the conditions are met. 【0058】 Step 9: 【0059】 After the server completes the payment, it sends a notification to the user reporting their point acquisition status and payment history. 【0060】 (Example 1) 【0061】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0062】 In today's busy lifestyle, efficiently managing personal spending and engaging in optimal economic activities is a challenging task. Furthermore, conventional technologies have been insufficient in extracting useful information from vast amounts of data and presenting it to users as concrete suggestions. 【0063】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0064】 In this invention, the server includes means for receiving expenditure data from users and storing it in a storage medium, means for classifying the stored expenditure data and analyzing expenditure trends, and means for generating suggestions for optimal economic activities based on the analysis results and utilizing external information. This makes it possible to automate personal expenditure management and suggest optimal economic activities to users. 【0065】 An "information processing device" is an electronic computing device used for inputting, processing, storing, and communicating data. 【0066】 "User" refers to an individual who uses this system to manage their own spending data. 【0067】 "Expenditure data" refers to information that includes records of financial payments made in a user's life. 【0068】 A "storage medium" is a physical or virtual device used to store digital data. 【0069】 "Classification" refers to the process of dividing data into categories based on specific criteria. 【0070】 "Spending trends" refer to a series of patterns or trends in change that can be derived from past spending data. 【0071】 "Analysis" is the act of examining data in detail to clarify its meaning and relationships. 【0072】 "External information" refers to additional data or knowledge obtained from outside the system. 【0073】 "Proposals for economic activity" refer to suggestions that show users feasible ways to save money and manage their spending. 【0074】 "Transaction automation" is a function that automatically executes pre-configured processes based on approved proposals. 【0075】 This invention is an information processing system for users to efficiently manage their living expense data and perform optimal economic activities. The system consists of terminals, servers, and a network connecting them. 【0076】 First, the user uses a terminal to input their daily living expense data. The terminal formats the input data and sends it to the server via the internet. The terminal has an application installed that provides basic data input and transmission functions. 【0077】 The server stores the received spending data in a storage medium. Next, the server categorizes this data and analyzes spending trends using data analysis software such as Python's Pandas. This identifies the user's past spending patterns. 【0078】 Furthermore, the server acquires external information through the network. This external information includes discount and promotional information. The server combines this information with user spending data and uses a generative AI model to create optimal suggestions for economic activity. 【0079】 The server sends the generated proposal to the user's device. The user can review the proposal on the device and approve or modify it. Once the proposal is approved, the server executes payment automation and completes the transaction on behalf of the user. 【0080】 For example, if a user wants to optimize their monthly grocery spending, the system will analyze past grocery spending data and suggest an optimal shopping schedule utilizing specific discount campaigns. This suggestion may also recommend a specific payment method. An example of a prompt might be, "Analyze my past spending data and suggest the most effective ways to save money." 【0081】 This format allows users to automate expense management and reduce their financial burden. 【0082】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0083】 Step 1: 【0084】 The user enters their personal spending data into the terminal. The input data includes date, purchased items, payment method, and amount. The terminal converts this data into a predetermined format (e.g., JSON). As output, it generates formatted data and prepares it for transmission to the server. 【0085】 Step 2: 【0086】 The terminal sends formatted data to the server via the internet. This transmission is performed using HTTP or a RESTful API. The input is formatted data, and the output generates data packets that arrive at the server. This process allows the server to receive the data. 【0087】 Step 3: 【0088】 The server stores the received expenditure data in a storage medium. A database management system (e.g., MySQL®) is used to reliably store the data. The input is formatted data sent from the terminal, and the output is continuous data storage. 【0089】 Step 4: 【0090】 The server categorizes the recorded spending data. It uses machine learning algorithms and predefined rules to separate the data into categories such as "groceries," "transportation," and "entertainment." The input is the raw recorded data, and the output is the generated categorized data. 【0091】 Step 5: 【0092】 The server analyzes classified spending data and calculates spending trends. Using the Python Pandas library, it identifies spending patterns for each category based on historical data. The input is classified data, and the output is a spending trend report. 【0093】 Step 6: 【0094】 The server retrieves discount and campaign information from external information sources. It uses scraping tools and public APIs to collect real-time market data. The input is an information request from an external source, and the output is actionable market information. 【0095】 Step 7: 【0096】 The server integrates the user's spending trends with external information and uses a generative AI model to create optimal economic activity suggestions. Using spending trend reports and external information as input, it generates a suggested plan as output. This provides specific savings strategies and purchasing methods. 【0097】 Step 8: 【0098】 The server sends the generated proposal to the terminal and notifies the user. The user reviews the proposal on the terminal and approves or modifies it through the UI. The proposed plan is used as input, and the notification displayed in the user interface is generated as output. 【0099】 Step 9: 【0100】 The user approves the proposal on their device. To approve, they press a confirmation button to send their approval to the server. The input is the proposal content, and the output is the approval status sent to the server. 【0101】 Step 10: 【0102】 The server automates payments based on user-approved proposals. It initiates transactions via the payment service's API and automates payments according to pre-configured settings. Inputs include approval status and proposal details, and the output generates an automated transaction record. 【0103】 (Application Example 1) 【0104】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0105】 Traditional spending management systems simply record and display user spending data, lacking the ability to efficiently optimize or automate payments. Furthermore, they insufficiently support users in maximizing their financial benefits by leveraging optimal market discount information, resulting in users being unable to effectively manage their own spending. 【0106】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0107】 In this invention, the server includes means for inputting user living expense data and storing it in a data storage device, means for classifying the stored living expense data according to classification criteria and analyzing spending trends, and means for generating optimal economic activity suggestions by utilizing external information based on the analysis results. This makes it possible to rationalize the user's economic behavior and maximize optimal payments and economic benefits by utilizing special offer information. 【0108】 An "information processing device" is a device that processes data received from a user and generates specific deliverables. 【0109】 "Living expense data" refers to information about a user's daily spending, including the date of purchase, item, payment method, and amount. 【0110】 A "data storage device" is a storage system for saving personal spending data. 【0111】 "Classifying according to classification criteria" is a method of organizing and classifying data based on specific categories or attributes. 【0112】 "Analyzing spending trends" is the process of analyzing past spending data to clarify users' consumption patterns and trends. 【0113】 "External information" refers to additional information that the system obtains through the internet or other networks without the user having to obtain it themselves. 【0114】 "Generating economic activity suggestions" is the process of creating optimal spending and saving plans for users and making suggestions to encourage their use. 【0115】 "Proposal notification" refers to the act of transmitting a proposal generated by an information processing device to the user's terminal. 【0116】 "Special sale information" refers to discounted products and promotional offers available in the market. 【0117】 "Optimizing economic benefits" means implementing suggestions and actions that allow users to obtain more value with less spending. 【0118】 The system for carrying out this invention consists of a user terminal, a server, and a data storage device. The user terminal provides an interface for inputting living expense data, which includes a smartphone application or a desktop application. The living expense data entered by the user is transmitted to the data storage device. 【0119】 The server runs on a cloud platform such as Amazon Web Services (AWS®) and is implemented using programming languages ​​such as Python and Node.js. Amazon RDS is used for data storage. The server classifies the received living expense data according to classification criteria and analyzes spending trends using libraries such as pandas and NumPy. 【0120】 The server also continuously retrieves external information, particularly commercial discount information, using Web APIs. This information is combined with the user's past spending data to generate suggestions for optimal economic activity. 【0121】 The generated proposal is notified to the user's device via Firebase Cloud Messaging or similar means. The user reviews it and approves or modifies it. If the proposal is approved, the server initiates the automated payment process, which is executed automatically according to the set schedule for the specific payment. 【0122】 As a concrete example, a new campaign might be announced that allows users to earn more points when purchasing a product on certain days of the week. An example of a prompt for the generating AI model is: "Generate efficient suggestions for how users can save money on their weekday lunches. Provide advice based on the user's past payment data and the latest discount information." 【0123】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0124】 Step 1: 【0125】 The user enters their living expense data using a terminal. This data includes the purchase date, item name, payment method, and amount. The terminal formats this data and prepares it for transmission to the data storage device. 【0126】 Step 2: 【0127】 The server stores the living expense data received from the terminal in a data storage device. The server classifies the received data based on date, category, etc., and generates a list of data categorized by type using the pandas library. Based on this list, it analyzes spending trends and understands the user's consumption patterns. 【0128】 Step 3: 【0129】 The server uses external APIs to retrieve currently available discounts and promotional information. This information is then analyzed in conjunction with the user's past spending patterns to generate optimal economic activity suggestions. The NumPy library is used to calculate multiple possibilities and select the most relevant information. 【0130】 Step 4: 【0131】 The server uses Firebase Cloud Messaging to notify the user's device of the generated economic activity proposals. The user can review the proposals and either approve them or send feedback for revisions. 【0132】 Step 5: 【0133】 If the user approves the proposal, the server will initiate an automated payment process based on that information. Using various APIs, it will integrate with credit card and electronic payment systems and process payments according to the required schedule. At this point, the input is the user's approved proposal, and the output is the automated payment process. 【0134】 Step 6: 【0135】 The server reports to the user's terminal that the payment process is complete. It sends a payment confirmation message and updates the data storage device for analysis of the next spending pattern. 【0136】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0137】 This invention combines an information processing system that optimizes the management of a user's living expenses with an emotion engine that analyzes the user's emotions. By comprehensively analyzing the user's spending behavior and emotional state, this system can provide more personalized spending suggestions. 【0138】 This system begins with the user entering their living expenses data via a terminal. The entered data is formatted by the terminal and sent to the server. The server stores the received data in a database and categorizes it. Next, the server analyzes spending trends using historical data. 【0139】 Furthermore, a key feature of this invention is the incorporation of an emotion engine. This emotion engine analyzes the user's current emotional state through user input data, voice, and facial recognition. The server considers this emotional data together with spending data to generate spending suggestions tailored to the user's psychological tendencies. This makes it possible to provide advice, for example, to curb impulsive purchases that are more likely to occur when stress levels are high. 【0140】 The generated proposal is sent to the user via their device. The user reviews the proposal and approves or modifies it according to their circumstances. If approved, the server sets up automated payments based on the proposal. It can also continuously monitor the user's emotional state and update the proposal as needed. 【0141】 For example, if a user tends to feel stressed on weekends, the emotion engine will detect this and suggest low-cost activities that are effective for stress relief. This suggestion takes into account the user's spending trends and current emotional state, thereby increasing user satisfaction while reducing unnecessary spending. 【0142】 Thus, the present invention realizes a system that supports users' economic behavior from an emotional perspective as well, providing a new dimension to expenditure management. 【0143】 The following describes the processing flow. 【0144】 Step 1: 【0145】 The user enters their living expense data into the terminal. The terminal formats the data and prepares it for transmission to the server. The data includes the date of the expense, the item, the payment method, and the amount. 【0146】 Step 2: 【0147】 The terminal processes the data and sends it to the server. The server stores the received data in a database and categorizes it. An automated algorithm is used for classification. 【0148】 Step 3: 【0149】 The server analyzes past spending data to identify spending trends. It extracts seasonal variations and personal patterns from the data, thereby understanding the user's consumption behavior. 【0150】 Step 4: 【0151】 To understand the user's emotional state, the device captures voice and facial expressions and sends them to the emotion engine. The emotion engine analyzes the data to obtain the current emotional state. 【0152】 Step 5: 【0153】 The server analyzes spending data along with emotional states to generate optimal spending suggestions tailored to those emotions. These suggestions help curb impulsive purchases during stressful times and extra spending during moments of special joy. 【0154】 Step 6: 【0155】 The server sends the proposal to the terminal. The terminal notifies the user, and the proposal is displayed on the dashboard. The user can review the proposal and approve or modify it. 【0156】 Step 7: 【0157】 If the proposal is approved, the server will automate the payment process. Based on the automation settings, it will manage payments according to the configured conditions. 【0158】 Step 8: 【0159】 The server continuously monitors the user's emotional state and updates suggestions as needed. It optimizes suggestions based on emotional changes to support the user's economic behavior. 【0160】 Step 9: 【0161】 After the payment is completed, the server notifies the user of the result, reporting on the points earned and offering future suggestions. This allows users to manage their financial activities more effectively. 【0162】 (Example 2) 【0163】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0164】 In modern society, individual spending is diverse, making it difficult to provide consistent spending management and advice that addresses emotional fluctuations. Furthermore, advice based solely on simple economic data fails to consider an individual's psychological state, making effective spending management challenging. 【0165】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0166】 In this invention, the server includes means for inputting user spending data and storing it in a memory device, means for classifying the stored data into general-purpose data and analyzing spending trends, and means for acquiring the user's voice and images and performing sentiment analysis. This makes it possible to make suggestions that comprehensively consider individual spending trends and emotional states. 【0167】 An "information processing device" is a series of devices or systems that perform data input from users, data analysis, and proposal generation. 【0168】 A "user" is an individual who uses this system to input their living expense data and receives expense suggestions based on that data. 【0169】 "Living expenses data" refers to data that users input as a record of various expenses in their daily lives, including information on food expenses and utility bills. 【0170】 A "storage device" is a device used to temporarily or permanently store data on living expenses and analysis results, and includes database servers, etc. 【0171】 "General-purpose data" refers to general data that can be classified into various categories and is suitable for a wide range of uses rather than specific applications. 【0172】 "Spending trends" refer to spending patterns and economic behavior flows derived from past living expense data. 【0173】 "Emotional analysis" refers to the process of evaluating or estimating a user's psychological state using their voice and image data. 【0174】 "Recommendation generation" is the process of creating spending-related advice and plans for users based on analyzed data and emotional states. 【0175】 This invention is an information processing system that manages a user's living expenses and provides personalized suggestions through sentiment analysis. This system is implemented using a user's terminal, such as a smartphone or computer, and a server located in the cloud or locally. 【0176】 The terminal is responsible for inputting user spending data. This input is performed via a touchscreen display or keyboard, and the entered data is formatted on the terminal. The formatted data is then sent from the terminal to the server via the internet. For security reasons, it is recommended to use a secure communication method such as the HTTPS protocol. 【0177】 The server stores the received living expense data in a database. This database is built using a database management system such as SQL and is designed to efficiently classify and search user-specific data. On the server, the stored data is classified by category, and spending trends are analyzed using AI algorithms. The analysis employs statistical methods using historical spending data and machine learning models. 【0178】 Furthermore, the device collects the user's voice and image data. This data is sent to a server with the user's permission. On the server, an emotion analysis engine analyzes this data to evaluate the user's emotional state. This analysis utilizes voice recognition software and facial expression analysis tools. 【0179】 The server integrates spending data and sentiment data and uses a generative AI model to generate spending suggestions tailored to the user. The generation process uses prompts to instruct the AI ​​model; for example, "Consider the user's current sentiment state and spending tendencies, and suggest an appropriate spending plan." 【0180】 The generated proposals are notified to the user via their device, and the user can review them. If the proposal is approved, the server can automate payments or set up recurring expenses based on the proposal. 【0181】 For example, on weekends when users are more likely to feel stressed, the emotion analysis engine can suggest low-cost relaxing activities, helping to reduce unnecessary expenses. This makes it possible to improve user satisfaction while also providing economic benefits. 【0182】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0183】 Step 1: 【0184】 Users input their daily living expense data into a terminal. The input is in text format and includes expense details, amounts, and categories. The terminal formats the input data into a predetermined format and sends the result to the server. This process prepares the formatted data for reception on the server. 【0185】 Step 2: 【0186】 The server receives data sent from the terminal and stores it in the database. The database stores living expense data, which is stored using SQL queries. At this stage, the data is recorded and processed into a usable state, ready to be organized by category. 【0187】 Step 3: 【0188】 The server analyzes the stored spending data and categorizes it. Here, historical spending data is used to analyze user spending trends. Statistical analysis is performed using machine learning algorithms to calculate peak spending and average spending amounts. This analysis helps identify patterns in the user's economic activity and generates a trend report. 【0189】 Step 4: 【0190】 Users input their own voice and image data through a device. This data is used to evaluate their emotional state. The device sends the voice and image data to a server, and the results are input into an emotion analysis engine. 【0191】 Step 5: 【0192】 The server uses an emotion analysis engine to analyze transmitted audio and image data and identify the user's emotional state. Speech recognition software and facial expression analysis tools are used to determine the user's psychological state (e.g., stress, reassurance). The analysis results are output as an emotion report. 【0193】 Step 6: 【0194】 The server uses a generative AI model to integrate data from spending trend reports and sentiment reports to generate spending suggestions tailored to the user. The prompt "Consider the user's current sentiment state and spending trends, and suggest an appropriate spending plan" is used to instruct the AI ​​model to generate suggestions. These suggestions may include advice on reducing unnecessary spending and useful shopping lists. 【0195】 Step 7: 【0196】 The server sends the generated spending proposals to the terminal, which then notifies the user. The user reviews the proposals and approves or modifies them. Approved proposals are recorded by the server, and payment automation is set up based on the proposals. This creates an environment where users can engage in spending behavior in accordance with the proposals. 【0197】 (Application Example 2) 【0198】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0199】 In modern times, optimizing users' spending habits is a crucial issue, but few systems take into account the user's emotional state during this process. As a result, users risk making inefficient spending decisions influenced by their emotions. Furthermore, current spending management systems fail to offer spending suggestions tailored to the user's emotional state, making it difficult to increase user satisfaction. New technologies are needed to address this issue. 【0200】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0201】 In this invention, the server includes means for inputting user living expense data via an information processing device and storing the data in a database; means for classifying the stored living expense data by category and analyzing spending trends via the information processing device; and means for analyzing the user's emotional state from voice and images using an emotion analysis engine and utilizing that data. This makes it possible to make optimal spending suggestions that take into account the user's emotional state, effectively support the user's economic activities, and suppress unnecessary spending. 【0202】 An "information processing device" is a device that collects, stores, classifies, analyzes, and communicates data, and it handles user spending data. 【0203】 A "database" is a management system for storing living expense data collected and stored by information processing devices. 【0204】 "Spending trends" refer to tendencies and patterns derived from a user's past spending data, and are analytical results that can be used to plan future spending. 【0205】 A "emotion analysis engine" is a system that analyzes a user's emotional state from their voice and image data, and is used to optimize spending suggestions. 【0206】 "External information" refers to data from outside the user's network, such as market trends and economic indicators, that information processing devices utilize to optimize the user's spending activities. 【0207】 "Methods for automating payments" refer to systems that efficiently handle the settlement process for living expenses based on proposals approved by the user. 【0208】 The system that realizes this application example is built primarily using smartphones and servers. The smartphone is a device that provides an interface for users to input their living expenses data. Smartphones are equipped with functions that analyze emotions from voice and image data using software such as Apple's Core ML or Google's TENSORFLOW Lite. 【0209】 When a user enters spending data using their smartphone, that data is transmitted to a database via the internet and stored on a server. The server categorizes the entered data and analyzes past data to identify spending trends. 【0210】 The server has an integrated emotion analysis engine that analyzes the user's emotional state based on voice and image data acquired from the user. Combining this analysis result with spending trends, the server utilizes a generative AI model to create personalized spending suggestions for each user. 【0211】 As a concrete example, consider a user who wants to engage in relaxation activities on the weekend. If this user is particularly stressed, the system uses data obtained from its emotion analysis engine to transmit that information to the server, which then suggests activities and products that can help reduce stress. These suggestions are sent from the server to the user's device, where the user can review them and approve or modify them as needed. 【0212】 An example of a prompt might be: "Consider the user's current emotional state and spending data, and generate suggestions for the most suitable activities from a stress-relieving and spending control perspective." This prompt allows the system to provide the user with optimal suggestions and support efficient spending management. 【0213】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0214】 Step 1: 【0215】 Users input their spending data using their smartphones. The entered data is temporarily stored in the device's local storage via the user interface. The data is then transmitted to a server via the internet using a secure protocol. The entered data includes items purchased, amounts, dates, and categories. 【0216】 Step 2: 【0217】 The server stores the received living expense data in a database. The server categorizes the data and analyzes spending trends by comparing it with past data. This analysis uses SQL queries to calculate average spending and standard scores for each category and detects abnormal spending. 【0218】 Step 3: 【0219】 The user's voice and image data are captured by their smartphone and input into an emotion analysis engine. The analysis engine uses a machine learning model (e.g., TensorFlow Lite) to analyze this data and estimate the user's emotional state. The output emotion data includes happiness levels, stress levels, excitement levels, and more. 【0220】 Step 4: 【0221】 The server integrates spending trends and sentiment data, leveraging a generative AI model to generate personalized spending suggestions. Based on prompts, the server activates the AI ​​model to calculate the best suggestions for the user. The generated suggestions include spending reduction strategies and relaxation methods, and are created based on the example prompts mentioned earlier. 【0222】 Step 5: 【0223】 The server generates a proposal and sends it to the user's smartphone. The user reviews the proposal via their device and approves or modifies it. If approved, the proposal is reflected in the automated payment system, and payment is processed automatically. 【0224】 Step 6: 【0225】 The device continuously monitors the user's emotional state and periodically sends this data to the server. The server updates suggestions in real time and generates new suggestions as needed. This continuous monitoring allows the user to receive dynamically optimized spending suggestions. 【0226】 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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. 【0227】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0228】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0229】 [Second Embodiment] 【0230】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0231】 As shown in Figure 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. 【0232】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0233】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0234】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0235】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0236】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0237】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0238】 The specific processing program 56 is an example of a "program" relating to the technology of this 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 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. 【0239】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0240】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0241】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0242】 One embodiment of the present invention is an information processing system that efficiently collects and analyzes a user's living expenses data and provides optimal payment suggestions and automated payments. This system operates around an information processing device and supports a series of processes from user input to output. 【0243】 First, the user enters their personal spending data into the terminal. This data includes the date, purchased items, payment method, and amount. The terminal formats the entered data and sends it to the server. The server stores the received data in a database and categorizes it. 【0244】 Next, the server analyzes spending data categorized by type to reveal past spending trends. Furthermore, the server acquires discount and campaign data as external information via the network. This enables the server to suggest optimal economic activities that link the user's spending patterns with market opportunities. 【0245】 The server notifies the user of the generated proposal via the terminal. The user can review the proposal on the terminal and approve or modify its contents. If the user approves the proposal, the automated payment process begins. 【0246】 As a concrete example, consider a user who wants to reduce their monthly grocery spending. This system analyzes past grocery spending and suggests discount campaigns that take place on specific days of the week. It can also indicate that paying by credit card is the most effective method. Once the user approves this suggestion, the system automates payments on those days from then on, achieving the user's desired savings. 【0247】 Thus, the system realized by the present invention reduces the user's financial burden by streamlining the user's living expenses and providing an optimized payment plan. 【0248】 The following describes the processing flow. 【0249】 Step 1: 【0250】 The user enters their living expense data into the terminal. This includes the payment date, purchased items, payment method used, and amount. The terminal formats the entered data and prepares it for transmission to the server. 【0251】 Step 2: 【0252】 The terminal sends the formatted data to the server. The server stores the received data in a database. During storage, it checks the consistency and completeness of the data and verifies that there are no inconsistencies. 【0253】 Step 3: 【0254】 The server retrieves the stored data and classifies it by category. Expenditure data is divided into categories such as food, transportation, and entertainment, and the expenditure amount for each category is aggregated. 【0255】 Step 4: 【0256】 The server analyzes spending trends using data categorized by type. It compares spending over the past few months to identify seasonal variations and specific spending patterns. 【0257】 Step 5: 【0258】 The server retrieves discount and promotional information from external sources via the network. This information is then combined with the user's spending data to generate suggestions for optimal economic activity. 【0259】 Step 6: 【0260】 The server sends the generated suggestions to the terminal. The terminal notifies the user, and the suggestions are displayed on the dashboard. 【0261】 Step 7: 【0262】 The user reviews the proposal on their device and approves, modifies, or rejects it. If approved, that information is sent to the server. 【0263】 Step 8: 【0264】 If the user approves the proposal, the server will configure the automated payment settings. According to the settings, payments will be automatically executed when the conditions are met. 【0265】 Step 9: 【0266】 After the server completes the payment, it sends a notification to the user reporting their point acquisition status and payment history. 【0267】 (Example 1) 【0268】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0269】 In today's busy lifestyle, efficiently managing personal spending and engaging in optimal economic activities is a challenging task. Furthermore, conventional technologies have been insufficient in extracting useful information from vast amounts of data and presenting it to users as concrete suggestions. 【0270】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0271】 In this invention, the server includes means for receiving expenditure data from users and storing it in a storage medium, means for classifying the stored expenditure data and analyzing expenditure trends, and means for generating suggestions for optimal economic activities based on the analysis results and utilizing external information. This makes it possible to automate personal expenditure management and suggest optimal economic activities to users. 【0272】 An "information processing device" is an electronic computing device used for inputting, processing, storing, and communicating data. 【0273】 "User" refers to an individual who uses this system to manage their own spending data. 【0274】 "Expenditure data" refers to information that includes records of financial payments made in a user's life. 【0275】 A "storage medium" is a physical or virtual device used to store digital data. 【0276】 "Classification" refers to the process of dividing data into categories based on specific criteria. 【0277】 "Spending tendency" refers to a series of patterns and trends of change derived based on past spending data. 【0278】 "Analysis" refers to the act of examining data in detail to clarify its meaning and relationships. 【0279】 "External information" refers to additional data and knowledge obtained from outside the system. 【0280】 "Proposals for economic activities" refer to the content of proposals that show users feasible methods of saving and spending management. 【0281】 "Automated transaction" refers to a function that automatically executes preset processes based on approved proposals. 【0282】 This invention is an information processing system for users to efficiently manage their living expense data and execute optimal economic activities. The system is composed of terminals, servers, and the network connecting them. 【0283】 First, the user uses the terminal to input daily living expense data. The terminal formats the input data and sends it to the server via the Internet. An application with basic data input and transmission functions is installed on the terminal. 【0284】 The server stores the received expense data in a storage medium. Next, the server classifies this data by category and analyzes the spending tendency using data analysis software such as Python's Pandas. This identifies the user's past spending patterns. 【0285】 Furthermore, the server obtains external information through the network. This external information includes discount and campaign information. The server combines this information with the user's expenditure data and uses a generated AI model to create an optimal proposal for economic activities. 【0286】 The server sends the generated proposal to the terminal. The user can view the proposal on the terminal and approve or modify it. Once the proposal is approved, the server executes payment automation and conducts transactions on behalf of the user. 【0287】 As a specific example, if the user wants to optimize their monthly food expenses, the system analyzes past food expenditure data and proposes an optimal purchase schedule using specific discount campaigns. Additionally, this proposal may also recommend a specific payment method. An example of a prompt sentence is "Please analyze the past expenditure data and propose the most effective savings method." 【0288】 In this form, the user can automate expenditure management and reduce economic burdens. 【0289】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0290】 Step 1: 【0291】 The user inputs their living expenditure data into the terminal. The input data includes the date, purchased item, payment method, and amount. The terminal converts this data into a predetermined format (e.g., JSON format). As output, it generates formatted data and prepares to send it to the server. 【0292】 Step 2: 【0293】 The terminal sends formatted data to the server via the internet. This transmission is performed using HTTP or a RESTful API. The input is formatted data, and the output generates data packets that arrive at the server. This process allows the server to receive the data. 【0294】 Step 3: 【0295】 The server stores the received expenditure data in a storage medium. A database management system (e.g., MySQL) is used to ensure stable data storage. Input is formatted data sent from the terminal, and continuous data storage is performed as output. 【0296】 Step 4: 【0297】 The server categorizes the recorded spending data. It uses machine learning algorithms and predefined rules to separate the data into categories such as "groceries," "transportation," and "entertainment." The input is the raw recorded data, and the output is the generated categorized data. 【0298】 Step 5: 【0299】 The server analyzes classified spending data and calculates spending trends. Using the Python Pandas library, it identifies spending patterns for each category based on historical data. The input is classified data, and the output is a spending trend report. 【0300】 Step 6: 【0301】 The server retrieves discount and campaign information from external information sources. It uses scraping tools and public APIs to collect real-time market data. The input is an information request from an external source, and the output is actionable market information. 【0302】 Step 7: 【0303】 The server integrates the user's spending trends and external information, and uses a generative AI model to create proposals for optimal economic activities. Using the spending trend report and external information as input, it generates a proposal plan as output. This proposes specific cost-saving measures and purchasing methods. 【0304】 Step 8: 【0305】 The server sends the generated proposal to the terminal and notifies the user. The user checks the proposal content on the terminal and approves or modifies it through the UI. Using the proposal plan as input, it generates a notification displayed on the user interface as output. 【0306】 Step 9: 【0307】 The user approves the proposal on the terminal. When approving, the user conveys the approval intention to the server by pressing the confirmation button. The input is the proposal content, and as output, it sends the approval status to the server. 【0308】 Step 10: 【0309】 The server automates payments based on the proposal approved by the user. It starts a transaction through the API of the payment service and automates the payment according to the preset content. The input is the approval status and the proposal content, and as output, it generates an automated transaction record. 【0310】 (Application Example 1) 【0311】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0312】 Traditional spending management systems simply record and display user spending data, lacking the ability to efficiently optimize or automate payments. Furthermore, they insufficiently support users in maximizing their financial benefits by leveraging optimal market discount information, resulting in users being unable to effectively manage their own spending. 【0313】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0314】 In this invention, the server includes means for inputting user living expense data and storing it in a data storage device, means for classifying the stored living expense data according to classification criteria and analyzing spending trends, and means for generating optimal economic activity suggestions by utilizing external information based on the analysis results. This makes it possible to rationalize the user's economic behavior and maximize optimal payments and economic benefits by utilizing special offer information. 【0315】 An "information processing device" is a device that processes data received from a user and generates specific deliverables. 【0316】 "Living expense data" refers to information about a user's daily spending, including the date of purchase, item, payment method, and amount. 【0317】 A "data storage device" is a storage system for saving personal spending data. 【0318】 "Classifying according to classification criteria" is a method of organizing and classifying data based on specific categories or attributes. 【0319】 "Analyzing spending trends" is the process of analyzing past spending data to clarify users' consumption patterns and trends. 【0320】 "External information" refers to additional information that the system obtains through the internet or other networks without the user having to obtain it themselves. 【0321】 "Generating economic activity suggestions" is the process of creating optimal spending and saving plans for users and making suggestions to encourage their use. 【0322】 "Proposal notification" refers to the act of transmitting a proposal generated by an information processing device to the user's terminal. 【0323】 "Special sale information" refers to discounted products and promotional offers available in the market. 【0324】 "Optimizing economic benefits" means implementing suggestions and actions that allow users to obtain more value with less spending. 【0325】 The system for carrying out this invention consists of a user terminal, a server, and a data storage device. The user terminal provides an interface for inputting living expense data, which includes a smartphone application or a desktop application. The living expense data entered by the user is transmitted to the data storage device. 【0326】 The server runs on a cloud platform such as Amazon Web Services (AWS) and is implemented using programming languages ​​such as Python and Node.js. Amazon RDS is used for data storage. The server classifies the received living expense data according to classification criteria and analyzes spending trends using libraries such as pandas and NumPy. 【0327】 The server also continuously retrieves external information, particularly commercial discount information, using Web APIs. This information is combined with the user's past spending data to generate suggestions for optimal economic activity. 【0328】 The generated proposal is notified to the user's device via Firebase Cloud Messaging or similar means. The user reviews it and approves or modifies it. If the proposal is approved, the server initiates the automated payment process, which is executed automatically according to the set schedule for the specific payment. 【0329】 As a concrete example, a new campaign might be announced that allows users to earn more points when purchasing a product on certain days of the week. An example of a prompt for the generating AI model is: "Generate efficient suggestions for how users can save money on their weekday lunches. Provide advice based on the user's past payment data and the latest discount information." 【0330】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0331】 Step 1: 【0332】 The user enters their living expense data using a terminal. This data includes the purchase date, item name, payment method, and amount. The terminal formats this data and prepares it for transmission to the data storage device. 【0333】 Step 2: 【0334】 The server stores the living expense data received from the terminal in a data storage device. The server classifies the received data based on date, category, etc., and generates a list of data categorized by type using the pandas library. Based on this list, it analyzes spending trends and understands the user's consumption patterns. 【0335】 Step 3: 【0336】 The server uses external APIs to retrieve currently available discounts and promotional information. This information is then analyzed in conjunction with the user's past spending patterns to generate optimal economic activity suggestions. The NumPy library is used to calculate multiple possibilities and select the most relevant information. 【0337】 Step 4: 【0338】 The server uses Firebase Cloud Messaging to notify the user's device of the generated economic activity proposals. The user can review the proposals and either approve them or send feedback for revisions. 【0339】 Step 5: 【0340】 If the user approves the proposal, the server will initiate an automated payment process based on that information. Using various APIs, it will integrate with credit card and electronic payment systems and process payments according to the required schedule. At this point, the input is the user's approved proposal, and the output is the automated payment process. 【0341】 Step 6: 【0342】 The server reports to the user's terminal that the payment process is complete. It sends a payment confirmation message and updates the data storage device for analysis of the next spending pattern. 【0343】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0344】 This invention combines an information processing system that optimizes the management of a user's living expenses with an emotion engine that analyzes the user's emotions. By comprehensively analyzing the user's spending behavior and emotional state, this system can provide more personalized spending suggestions. 【0345】 This system begins with the user entering their living expenses data via a terminal. The entered data is formatted by the terminal and sent to the server. The server stores the received data in a database and categorizes it. Next, the server analyzes spending trends using historical data. 【0346】 Furthermore, a key feature of this invention is the incorporation of an emotion engine. This emotion engine analyzes the user's current emotional state through user input data, voice, and facial recognition. The server considers this emotional data together with spending data to generate spending suggestions tailored to the user's psychological tendencies. This makes it possible to provide advice, for example, to curb impulsive purchases that are more likely to occur when stress levels are high. 【0347】 The generated proposal is sent to the user via their device. The user reviews the proposal and approves or modifies it according to their circumstances. If approved, the server sets up automated payments based on the proposal. It can also continuously monitor the user's emotional state and update the proposal as needed. 【0348】 For example, if a user tends to feel stressed on weekends, the emotion engine will detect this and suggest low-cost activities that are effective for stress relief. This suggestion takes into account the user's spending trends and current emotional state, thereby increasing user satisfaction while reducing unnecessary spending. 【0349】 Thus, the present invention realizes a system that supports users' economic behavior from an emotional perspective as well, providing a new dimension to expenditure management. 【0350】 The following describes the processing flow. 【0351】 Step 1: 【0352】 The user enters their living expense data into the terminal. The terminal formats the data and prepares it for transmission to the server. The data includes the date of the expense, the item, the payment method, and the amount. 【0353】 Step 2: 【0354】 The terminal processes the data and sends it to the server. The server stores the received data in a database and categorizes it. An automated algorithm is used for classification. 【0355】 Step 3: 【0356】 The server analyzes past spending data to identify spending trends. It extracts seasonal variations and personal patterns from the data, thereby understanding the user's consumption behavior. 【0357】 Step 4: 【0358】 To understand the user's emotional state, the device captures voice and facial expressions and sends them to the emotion engine. The emotion engine analyzes the data to obtain the current emotional state. 【0359】 Step 5: 【0360】 The server analyzes spending data along with emotional states to generate optimal spending suggestions tailored to those emotions. These suggestions help curb impulsive purchases during stressful times and extra spending during moments of special joy. 【0361】 Step 6: 【0362】 The server sends the proposal to the terminal. The terminal notifies the user, and the proposal is displayed on the dashboard. The user can review the proposal and approve or modify it. 【0363】 Step 7: 【0364】 If the proposal is approved, the server will automate the payment process. Based on the automation settings, it will manage payments according to the configured conditions. 【0365】 Step 8: 【0366】 The server continuously monitors the user's emotional state and updates suggestions as needed. It optimizes suggestions based on emotional changes to support the user's economic behavior. 【0367】 Step 9: 【0368】 After the payment is completed, the server notifies the user of the result, reporting on the points earned and offering future suggestions. This allows users to manage their financial activities more effectively. 【0369】 (Example 2) 【0370】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0371】 In modern society, individual spending is diverse, making it difficult to provide consistent spending management and advice that addresses emotional fluctuations. Furthermore, advice based solely on simple economic data fails to consider an individual's psychological state, making effective spending management challenging. 【0372】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0373】 In this invention, the server includes means for inputting user spending data and storing it in a memory device, means for classifying the stored data into general-purpose data and analyzing spending trends, and means for acquiring the user's voice and images and performing sentiment analysis. This makes it possible to make suggestions that comprehensively consider individual spending trends and emotional states. 【0374】 An "information processing device" is a series of devices or systems that perform data input from users, data analysis, and proposal generation. 【0375】 A "user" is an individual who uses this system to input their living expense data and receives expense suggestions based on that data. 【0376】 "Living expenses data" refers to data that users input as a record of various expenses in their daily lives, including information on food expenses and utility bills. 【0377】 A "storage device" is a device used to temporarily or permanently store data on living expenses and analysis results, and includes database servers, etc. 【0378】 "General-purpose data" refers to general data that can be classified into various categories and is suitable for a wide range of uses rather than specific applications. 【0379】 "Spending trends" refer to spending patterns and economic behavior flows derived from past living expense data. 【0380】 "Emotional analysis" refers to the process of evaluating or estimating a user's psychological state using their voice and image data. 【0381】 "Recommendation generation" is the process of creating spending-related advice and plans for users based on analyzed data and emotional states. 【0382】 This invention is an information processing system that manages a user's living expenses and provides personalized suggestions through sentiment analysis. This system is implemented using a user's terminal, such as a smartphone or computer, and a server located in the cloud or locally. 【0383】 The terminal is responsible for inputting user spending data. This input is performed via a touchscreen display or keyboard, and the entered data is formatted on the terminal. The formatted data is then sent from the terminal to the server via the internet. For security reasons, it is recommended to use a secure communication method such as the HTTPS protocol. 【0384】 The server stores the received living expense data in a database. This database is built using a database management system such as SQL and is designed to efficiently classify and search user-specific data. On the server, the stored data is classified by category, and spending trends are analyzed using AI algorithms. The analysis employs statistical methods using historical spending data and machine learning models. 【0385】 Furthermore, the device collects the user's voice and image data. This data is sent to a server with the user's permission. On the server, an emotion analysis engine analyzes this data to evaluate the user's emotional state. This analysis utilizes voice recognition software and facial expression analysis tools. 【0386】 The server integrates spending data and sentiment data and uses a generative AI model to generate spending suggestions tailored to the user. The generation process uses prompts to instruct the AI ​​model; for example, "Consider the user's current sentiment state and spending tendencies, and suggest an appropriate spending plan." 【0387】 The generated proposals are notified to the user via their device, and the user can review them. If the proposal is approved, the server can automate payments or set up recurring expenses based on the proposal. 【0388】 For example, on weekends when users are more likely to feel stressed, the emotion analysis engine can suggest low-cost relaxing activities, helping to reduce unnecessary expenses. This makes it possible to improve user satisfaction while also providing economic benefits. 【0389】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0390】 Step 1: 【0391】 Users input their daily living expense data into a terminal. The input is in text format and includes expense details, amounts, and categories. The terminal formats the input data into a predetermined format and sends the result to the server. This process prepares the formatted data for reception on the server. 【0392】 Step 2: 【0393】 The server receives data sent from the terminal and stores it in the database. The database stores living expense data, which is stored using SQL queries. At this stage, the data is recorded and processed into a usable state, ready to be organized by category. 【0394】 Step 3: 【0395】 The server analyzes the stored spending data and categorizes it. Here, historical spending data is used to analyze user spending trends. Statistical analysis is performed using machine learning algorithms to calculate peak spending and average spending amounts. This analysis helps identify patterns in the user's economic activity and generates a trend report. 【0396】 Step 4: 【0397】 Users input their own voice and image data through a device. This data is used to evaluate their emotional state. The device sends the voice and image data to a server, and the results are input into an emotion analysis engine. 【0398】 Step 5: 【0399】 The server uses an emotion analysis engine to analyze transmitted audio and image data and identify the user's emotional state. Speech recognition software and facial expression analysis tools are used to determine the user's psychological state (e.g., stress, reassurance). The analysis results are output as an emotion report. 【0400】 Step 6: 【0401】 The server uses a generative AI model to integrate data from spending trend reports and sentiment reports to generate spending suggestions tailored to the user. The prompt "Consider the user's current sentiment state and spending trends, and suggest an appropriate spending plan" is used to instruct the AI ​​model to generate suggestions. These suggestions may include advice on reducing unnecessary spending and useful shopping lists. 【0402】 Step 7: 【0403】 The server sends the generated spending proposals to the terminal, which then notifies the user. The user reviews the proposals and approves or modifies them. Approved proposals are recorded by the server, and payment automation is set up based on the proposals. This creates an environment where users can engage in spending behavior in accordance with the proposals. 【0404】 (Application Example 2) 【0405】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0406】 In modern times, optimizing users' spending habits is a crucial issue, but few systems take into account the user's emotional state during this process. As a result, users risk making inefficient spending decisions influenced by their emotions. Furthermore, current spending management systems fail to offer spending suggestions tailored to the user's emotional state, making it difficult to increase user satisfaction. New technologies are needed to address this issue. 【0407】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0408】 In this invention, the server includes means for inputting user living expense data via an information processing device and storing the data in a database; means for classifying the stored living expense data by category and analyzing spending trends via the information processing device; and means for analyzing the user's emotional state from voice and images using an emotion analysis engine and utilizing that data. This makes it possible to make optimal spending suggestions that take into account the user's emotional state, effectively support the user's economic activities, and suppress unnecessary spending. 【0409】 An "information processing device" is a device that collects, stores, classifies, analyzes, and communicates data, and it handles user spending data. 【0410】 A "database" is a management system for storing living expense data collected and stored by information processing devices. 【0411】 "Spending trends" refer to tendencies and patterns derived from a user's past spending data, and are analytical results that can be used to plan future spending. 【0412】 A "emotion analysis engine" is a system that analyzes a user's emotional state from their voice and image data, and is used to optimize spending suggestions. 【0413】 "External information" refers to data from outside the user's network, such as market trends and economic indicators, that information processing devices utilize to optimize the user's spending activities. 【0414】 "Methods for automating payments" refer to systems that efficiently handle the settlement process for living expenses based on proposals approved by the user. 【0415】 The system that realizes this application example is built primarily using smartphones and servers. The smartphone is a device that provides an interface for users to input data on their living expenses. Smartphones are equipped with functions that analyze emotions from voice and image data using software such as Apple's Core ML or Google's TensorFlow Lite. 【0416】 When a user enters spending data using their smartphone, that data is transmitted to a database via the internet and stored on a server. The server categorizes the entered data and analyzes past data to identify spending trends. 【0417】 The server has an integrated emotion analysis engine that analyzes the user's emotional state based on voice and image data acquired from the user. Combining this analysis result with spending trends, the server utilizes a generative AI model to create personalized spending suggestions for each user. 【0418】 As a concrete example, consider a user who wants to engage in relaxation activities on the weekend. If this user is particularly stressed, the system uses data obtained from its emotion analysis engine to transmit that information to the server, which then suggests activities and products that can help reduce stress. These suggestions are sent from the server to the user's device, where the user can review them and approve or modify them as needed. 【0419】 An example of a prompt might be: "Consider the user's current emotional state and spending data, and generate suggestions for the most suitable activities from a stress-relieving and spending control perspective." This prompt allows the system to provide the user with optimal suggestions and support efficient spending management. 【0420】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0421】 Step 1: 【0422】 Users input their spending data using their smartphones. The entered data is temporarily stored in the device's local storage via the user interface. The data is then transmitted to a server via the internet using a secure protocol. The entered data includes items purchased, amounts, dates, and categories. 【0423】 Step 2: 【0424】 The server stores the received living expense data in a database. The server categorizes the data and analyzes spending trends by comparing it with past data. This analysis uses SQL queries to calculate average spending and standard scores for each category and detects abnormal spending. 【0425】 Step 3: 【0426】 The user's voice and image data are captured by their smartphone and input into an emotion analysis engine. The analysis engine uses a machine learning model (e.g., TensorFlow Lite) to analyze this data and estimate the user's emotional state. The output emotion data includes happiness levels, stress levels, excitement levels, and more. 【0427】 Step 4: 【0428】 The server integrates spending trends and sentiment data, leveraging a generative AI model to generate personalized spending suggestions. Based on prompts, the server activates the AI ​​model to calculate the best suggestions for the user. The generated suggestions include spending reduction strategies and relaxation methods, and are created based on the example prompts mentioned earlier. 【0429】 Step 5: 【0430】 The server generates a proposal and sends it to the user's smartphone. The user reviews the proposal via their device and approves or modifies it. If approved, the proposal is reflected in the automated payment system, and payment is processed automatically. 【0431】 Step 6: 【0432】 The device continuously monitors the user's emotional state and periodically sends this data to the server. The server updates suggestions in real time and generates new suggestions as needed. This continuous monitoring allows the user to receive dynamically optimized spending suggestions. 【0433】 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0434】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0435】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0436】 [Third Embodiment] 【0437】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0438】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0439】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0440】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0441】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0442】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0443】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0444】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0445】 The specific processing program 56 is an example of a "program" relating to the technology of this 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 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. 【0446】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0447】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0448】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0449】 One embodiment of the present invention is an information processing system that efficiently collects and analyzes a user's living expenses data and provides optimal payment suggestions and automated payments. This system operates around an information processing device and supports a series of processes from user input to output. 【0450】 First, the user enters their personal spending data into the terminal. This data includes the date, purchased items, payment method, and amount. The terminal formats the entered data and sends it to the server. The server stores the received data in a database and categorizes it. 【0451】 Next, the server analyzes spending data categorized by type to reveal past spending trends. Furthermore, the server acquires discount and campaign data as external information via the network. This enables the server to suggest optimal economic activities that link the user's spending patterns with market opportunities. 【0452】 The server notifies the user of the generated proposal via the terminal. The user can review the proposal on the terminal and approve or modify its contents. If the user approves the proposal, the automated payment process begins. 【0453】 As a concrete example, consider a user who wants to reduce their monthly grocery spending. This system analyzes past grocery spending and suggests discount campaigns that take place on specific days of the week. It can also indicate that paying by credit card is the most effective method. Once the user approves this suggestion, the system automates payments on those days from then on, achieving the user's desired savings. 【0454】 Thus, the system realized by the present invention reduces the user's financial burden by streamlining the user's living expenses and providing an optimized payment plan. 【0455】 The following describes the processing flow. 【0456】 Step 1: 【0457】 The user enters their living expense data into the terminal. This includes the payment date, purchased items, payment method used, and amount. The terminal formats the entered data and prepares it for transmission to the server. 【0458】 Step 2: 【0459】 The terminal sends the formatted data to the server. The server stores the received data in a database. During storage, it checks the consistency and completeness of the data and verifies that there are no inconsistencies. 【0460】 Step 3: 【0461】 The server retrieves the stored data and classifies it by category. Expenditure data is divided into categories such as food, transportation, and entertainment, and the expenditure amount for each category is aggregated. 【0462】 Step 4: 【0463】 The server analyzes spending trends using data categorized by type. It compares spending over the past few months to identify seasonal variations and specific spending patterns. 【0464】 Step 5: 【0465】 The server retrieves discount and promotional information from external sources via the network. This information is then combined with the user's spending data to generate suggestions for optimal economic activity. 【0466】 Step 6: 【0467】 The server sends the generated suggestions to the terminal. The terminal notifies the user, and the suggestions are displayed on the dashboard. 【0468】 Step 7: 【0469】 The user reviews the proposal on their device and approves, modifies, or rejects it. If approved, that information is sent to the server. 【0470】 Step 8: 【0471】 If the user approves the proposal, the server will configure the automated payment settings. According to the settings, payments will be automatically executed when the conditions are met. 【0472】 Step 9: 【0473】 After the server completes the payment, it sends a notification to the user reporting their point acquisition status and payment history. 【0474】 (Example 1) 【0475】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0476】 In today's busy lifestyle, efficiently managing personal spending and engaging in optimal economic activities is a challenging task. Furthermore, conventional technologies have been insufficient in extracting useful information from vast amounts of data and presenting it to users as concrete suggestions. 【0477】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0478】 In this invention, the server includes means for receiving expenditure data from users and storing it in a storage medium, means for classifying the stored expenditure data and analyzing expenditure trends, and means for generating suggestions for optimal economic activities based on the analysis results and utilizing external information. This makes it possible to automate personal expenditure management and suggest optimal economic activities to users. 【0479】 An "information processing device" is an electronic computing device used for inputting, processing, storing, and communicating data. 【0480】 "User" refers to an individual who uses this system to manage their own spending data. 【0481】 "Expenditure data" refers to information that includes records of financial payments made in a user's life. 【0482】 A "storage medium" is a physical or virtual device used to store digital data. 【0483】 "Classification" refers to the process of dividing data into categories based on specific criteria. 【0484】 "Spending trends" refer to a series of patterns or trends in change that can be derived from past spending data. 【0485】 "Analysis" is the act of examining data in detail to clarify its meaning and relationships. 【0486】 "External information" refers to additional data or knowledge obtained from outside the system. 【0487】 "Proposals for economic activity" refer to suggestions that show users feasible ways to save money and manage their spending. 【0488】 "Transaction automation" is a function that automatically executes pre-configured processes based on approved proposals. 【0489】 This invention is an information processing system for users to efficiently manage their living expense data and perform optimal economic activities. The system consists of terminals, servers, and a network connecting them. 【0490】 First, the user uses a terminal to input their daily living expense data. The terminal formats the input data and sends it to the server via the internet. The terminal has an application installed that provides basic data input and transmission functions. 【0491】 The server stores the received spending data in a storage medium. Next, the server categorizes this data and analyzes spending trends using data analysis software such as Python's Pandas. This identifies the user's past spending patterns. 【0492】 Furthermore, the server acquires external information through the network. This external information includes discount and promotional information. The server combines this information with user spending data and uses a generative AI model to create optimal suggestions for economic activity. 【0493】 The server sends the generated proposal to the user's device. The user can review the proposal on the device and approve or modify it. Once the proposal is approved, the server executes payment automation and completes the transaction on behalf of the user. 【0494】 For example, if a user wants to optimize their monthly grocery spending, the system will analyze past grocery spending data and suggest an optimal shopping schedule utilizing specific discount campaigns. This suggestion may also recommend a specific payment method. An example of a prompt might be, "Analyze my past spending data and suggest the most effective ways to save money." 【0495】 This format allows users to automate expense management and reduce their financial burden. 【0496】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0497】 Step 1: 【0498】 The user enters their personal spending data into the terminal. The input data includes date, purchased items, payment method, and amount. The terminal converts this data into a predetermined format (e.g., JSON). As output, it generates formatted data and prepares it for transmission to the server. 【0499】 Step 2: 【0500】 The terminal sends formatted data to the server via the internet. This transmission is performed using HTTP or a RESTful API. The input is formatted data, and the output generates data packets that arrive at the server. This process allows the server to receive the data. 【0501】 Step 3: 【0502】 The server stores the received expenditure data in a storage medium. A database management system (e.g., MySQL) is used to ensure stable data storage. Input is formatted data sent from the terminal, and continuous data storage is performed as output. 【0503】 Step 4: 【0504】 The server categorizes the recorded spending data. It uses machine learning algorithms and predefined rules to separate the data into categories such as "groceries," "transportation," and "entertainment." The input is the raw recorded data, and the output is the generated categorized data. 【0505】 Step 5: 【0506】 The server analyzes classified spending data and calculates spending trends. Using the Python Pandas library, it identifies spending patterns for each category based on historical data. The input is classified data, and the output is a spending trend report. 【0507】 Step 6: 【0508】 The server retrieves discount and campaign information from external information sources. It uses scraping tools and public APIs to collect real-time market data. The input is an information request from an external source, and the output is actionable market information. 【0509】 Step 7: 【0510】 The server integrates the user's spending trends with external information and uses a generative AI model to create optimal economic activity suggestions. Using spending trend reports and external information as input, it generates a suggested plan as output. This provides specific savings strategies and purchasing methods. 【0511】 Step 8: 【0512】 The server sends the generated proposal to the terminal and notifies the user. The user reviews the proposal on the terminal and approves or modifies it through the UI. The proposed plan is used as input, and the notification displayed in the user interface is generated as output. 【0513】 Step 9: 【0514】 The user approves the proposal on their device. To approve, they press a confirmation button to send their approval to the server. The input is the proposal content, and the output is the approval status sent to the server. 【0515】 Step 10: 【0516】 The server automates payments based on user-approved proposals. It initiates transactions via the payment service's API and automates payments according to pre-configured settings. Inputs include approval status and proposal details, and the output generates an automated transaction record. 【0517】 (Application Example 1) 【0518】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0519】 Traditional spending management systems simply record and display user spending data, lacking the ability to efficiently optimize or automate payments. Furthermore, they insufficiently support users in maximizing their financial benefits by leveraging optimal market discount information, resulting in users being unable to effectively manage their own spending. 【0520】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0521】 In this invention, the server includes means for inputting user living expense data and storing it in a data storage device, means for classifying the stored living expense data according to classification criteria and analyzing spending trends, and means for generating optimal economic activity suggestions by utilizing external information based on the analysis results. This makes it possible to rationalize the user's economic behavior and maximize optimal payments and economic benefits by utilizing special offer information. 【0522】 An "information processing device" is a device that processes data received from a user and generates specific deliverables. 【0523】 "Living expense data" refers to information about a user's daily spending, including the date of purchase, item, payment method, and amount. 【0524】 A "data storage device" is a storage system for saving personal spending data. 【0525】 "Classifying according to classification criteria" is a method of organizing and classifying data based on specific categories or attributes. 【0526】 "Analyzing spending trends" is the process of analyzing past spending data to clarify users' consumption patterns and trends. 【0527】 "External information" refers to additional information that the system obtains through the internet or other networks without the user having to obtain it themselves. 【0528】 "Generating economic activity suggestions" is the process of creating optimal spending and saving plans for users and making suggestions to encourage their use. 【0529】 "Proposal notification" refers to the act of transmitting a proposal generated by an information processing device to the user's terminal. 【0530】 "Special sale information" refers to discounted products and promotional offers available in the market. 【0531】 "Optimizing economic benefits" means implementing suggestions and actions that allow users to obtain more value with less spending. 【0532】 The system for carrying out this invention consists of a user terminal, a server, and a data storage device. The user terminal provides an interface for inputting living expense data, which includes a smartphone application or a desktop application. The living expense data entered by the user is transmitted to the data storage device. 【0533】 The server runs on a cloud platform such as Amazon Web Services (AWS) and is implemented using programming languages ​​such as Python and Node.js. Amazon RDS is used for data storage. The server classifies the received living expense data according to classification criteria and analyzes spending trends using libraries such as pandas and NumPy. 【0534】 The server also continuously retrieves external information, particularly commercial discount information, using Web APIs. This information is combined with the user's past spending data to generate suggestions for optimal economic activity. 【0535】 The generated proposal is notified to the user's device via Firebase Cloud Messaging or similar means. The user reviews it and approves or modifies it. If the proposal is approved, the server initiates the automated payment process, which is executed automatically according to the set schedule for the specific payment. 【0536】 As a concrete example, a new campaign might be announced that allows users to earn more points when purchasing a product on certain days of the week. An example of a prompt for the generating AI model is: "Generate efficient suggestions for how users can save money on their weekday lunches. Provide advice based on the user's past payment data and the latest discount information." 【0537】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0538】 Step 1: 【0539】 The user enters their living expense data using a terminal. This data includes the purchase date, item name, payment method, and amount. The terminal formats this data and prepares it for transmission to the data storage device. 【0540】 Step 2: 【0541】 The server stores the living expense data received from the terminal in a data storage device. The server classifies the received data based on date, category, etc., and generates a list of data categorized by type using the pandas library. Based on this list, it analyzes spending trends and understands the user's consumption patterns. 【0542】 Step 3: 【0543】 The server uses external APIs to retrieve currently available discounts and promotional information. This information is then analyzed in conjunction with the user's past spending patterns to generate optimal economic activity suggestions. The NumPy library is used to calculate multiple possibilities and select the most relevant information. 【0544】 Step 4: 【0545】 The server uses Firebase Cloud Messaging to notify the user's device of the generated economic activity proposals. The user can review the proposals and either approve them or send feedback for revisions. 【0546】 Step 5: 【0547】 If the user approves the proposal, the server will initiate an automated payment process based on that information. Using various APIs, it will integrate with credit card and electronic payment systems and process payments according to the required schedule. At this point, the input is the user's approved proposal, and the output is the automated payment process. 【0548】 Step 6: 【0549】 The server reports to the user's terminal that the payment process is complete. It sends a payment confirmation message and updates the data storage device for analysis of the next spending pattern. 【0550】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0551】 This invention combines an information processing system that optimizes the management of a user's living expenses with an emotion engine that analyzes the user's emotions. By comprehensively analyzing the user's spending behavior and emotional state, this system can provide more personalized spending suggestions. 【0552】 This system begins with the user entering their living expenses data via a terminal. The entered data is formatted by the terminal and sent to the server. The server stores the received data in a database and categorizes it. Next, the server analyzes spending trends using historical data. 【0553】 Furthermore, a key feature of this invention is the incorporation of an emotion engine. This emotion engine analyzes the user's current emotional state through user input data, voice, and facial recognition. The server considers this emotional data together with spending data to generate spending suggestions tailored to the user's psychological tendencies. This makes it possible to provide advice, for example, to curb impulsive purchases that are more likely to occur when stress levels are high. 【0554】 The generated proposal is sent to the user via their device. The user reviews the proposal and approves or modifies it according to their circumstances. If approved, the server sets up automated payments based on the proposal. It can also continuously monitor the user's emotional state and update the proposal as needed. 【0555】 For example, if a user tends to feel stressed on weekends, the emotion engine will detect this and suggest low-cost activities that are effective for stress relief. This suggestion takes into account the user's spending trends and current emotional state, thereby increasing user satisfaction while reducing unnecessary spending. 【0556】 Thus, the present invention realizes a system that supports users' economic behavior from an emotional perspective as well, providing a new dimension to expenditure management. 【0557】 The following describes the processing flow. 【0558】 Step 1: 【0559】 The user enters their living expense data into the terminal. The terminal formats the data and prepares it for transmission to the server. The data includes the date of the expense, the item, the payment method, and the amount. 【0560】 Step 2: 【0561】 The terminal processes the data and sends it to the server. The server stores the received data in a database and categorizes it. An automated algorithm is used for classification. 【0562】 Step 3: 【0563】 The server analyzes past spending data to identify spending trends. It extracts seasonal variations and personal patterns from the data, thereby understanding the user's consumption behavior. 【0564】 Step 4: 【0565】 To understand the user's emotional state, the device captures voice and facial expressions and sends them to the emotion engine. The emotion engine analyzes the data to obtain the current emotional state. 【0566】 Step 5: 【0567】 The server analyzes spending data along with emotional states to generate optimal spending suggestions tailored to those emotions. These suggestions help curb impulsive purchases during stressful times and extra spending during moments of special joy. 【0568】 Step 6: 【0569】 The server sends the proposal to the terminal. The terminal notifies the user, and the proposal is displayed on the dashboard. The user can review the proposal and approve or modify it. 【0570】 Step 7: 【0571】 If the proposal is approved, the server will automate the payment process. Based on the automation settings, it will manage payments according to the configured conditions. 【0572】 Step 8: 【0573】 The server continuously monitors the user's emotional state and updates suggestions as needed. It optimizes suggestions based on emotional changes to support the user's economic behavior. 【0574】 Step 9: 【0575】 After the payment is completed, the server notifies the user of the result, reporting on the points earned and offering future suggestions. This allows users to manage their financial activities more effectively. 【0576】 (Example 2) 【0577】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0578】 In modern society, individual spending is diverse, making it difficult to provide consistent spending management and advice that addresses emotional fluctuations. Furthermore, advice based solely on simple economic data fails to consider an individual's psychological state, making effective spending management challenging. 【0579】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0580】 In this invention, the server includes means for inputting user spending data and storing it in a memory device, means for classifying the stored data into general-purpose data and analyzing spending trends, and means for acquiring the user's voice and images and performing sentiment analysis. This makes it possible to make suggestions that comprehensively consider individual spending trends and emotional states. 【0581】 An "information processing device" is a series of devices or systems that perform data input from users, data analysis, and proposal generation. 【0582】 A "user" is an individual who uses this system to input their living expense data and receives expense suggestions based on that data. 【0583】 "Living expenses data" refers to data that users input as a record of various expenses in their daily lives, including information on food expenses and utility bills. 【0584】 A "storage device" is a device used to temporarily or permanently store data on living expenses and analysis results, and includes database servers, etc. 【0585】 "General-purpose data" refers to general data that can be classified into various categories and is suitable for a wide range of uses rather than specific applications. 【0586】 "Spending trends" refer to spending patterns and economic behavior flows derived from past living expense data. 【0587】 "Emotional analysis" refers to the process of evaluating or estimating a user's psychological state using their voice and image data. 【0588】 "Recommendation generation" is the process of creating spending-related advice and plans for users based on analyzed data and emotional states. 【0589】 This invention is an information processing system that manages a user's living expenses and provides personalized suggestions through sentiment analysis. This system is implemented using a user's terminal, such as a smartphone or computer, and a server located in the cloud or locally. 【0590】 The terminal is responsible for inputting user spending data. This input is performed via a touchscreen display or keyboard, and the entered data is formatted on the terminal. The formatted data is then sent from the terminal to the server via the internet. For security reasons, it is recommended to use a secure communication method such as the HTTPS protocol. 【0591】 The server stores the received living expense data in a database. This database is built using a database management system such as SQL and is designed to efficiently classify and search user-specific data. On the server, the stored data is classified by category, and spending trends are analyzed using AI algorithms. The analysis employs statistical methods using historical spending data and machine learning models. 【0592】 Furthermore, the device collects the user's voice and image data. This data is sent to a server with the user's permission. On the server, an emotion analysis engine analyzes this data to evaluate the user's emotional state. This analysis utilizes voice recognition software and facial expression analysis tools. 【0593】 The server integrates spending data and sentiment data and uses a generative AI model to generate spending suggestions tailored to the user. The generation process uses prompts to instruct the AI ​​model; for example, "Consider the user's current sentiment state and spending tendencies, and suggest an appropriate spending plan." 【0594】 The generated proposals are notified to the user via their device, and the user can review them. If the proposal is approved, the server can automate payments or set up recurring expenses based on the proposal. 【0595】 For example, on weekends when users are more likely to feel stressed, the emotion analysis engine can suggest low-cost relaxing activities, helping to reduce unnecessary expenses. This makes it possible to improve user satisfaction while also providing economic benefits. 【0596】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0597】 Step 1: 【0598】 Users input their daily living expense data into a terminal. The input is in text format and includes expense details, amounts, and categories. The terminal formats the input data into a predetermined format and sends the result to the server. This process prepares the formatted data for reception on the server. 【0599】 Step 2: 【0600】 The server receives data sent from the terminal and stores it in the database. The database stores living expense data, which is stored using SQL queries. At this stage, the data is recorded and processed into a usable state, ready to be organized by category. 【0601】 Step 3: 【0602】 The server analyzes the stored spending data and categorizes it. Here, historical spending data is used to analyze user spending trends. Statistical analysis is performed using machine learning algorithms to calculate peak spending and average spending amounts. This analysis helps identify patterns in the user's economic activity and generates a trend report. 【0603】 Step 4: 【0604】 Users input their own voice and image data through a device. This data is used to evaluate their emotional state. The device sends the voice and image data to a server, and the results are input into an emotion analysis engine. 【0605】 Step 5: 【0606】 The server uses an emotion analysis engine to analyze transmitted audio and image data and identify the user's emotional state. Speech recognition software and facial expression analysis tools are used to determine the user's psychological state (e.g., stress, reassurance). The analysis results are output as an emotion report. 【0607】 Step 6: 【0608】 The server uses a generative AI model to integrate data from spending trend reports and sentiment reports to generate spending suggestions tailored to the user. The prompt "Consider the user's current sentiment state and spending trends, and suggest an appropriate spending plan" is used to instruct the AI ​​model to generate suggestions. These suggestions may include advice on reducing unnecessary spending and useful shopping lists. 【0609】 Step 7: 【0610】 The server sends the generated spending proposals to the terminal, which then notifies the user. The user reviews the proposals and approves or modifies them. Approved proposals are recorded by the server, and payment automation is set up based on the proposals. This creates an environment where users can engage in spending behavior in accordance with the proposals. 【0611】 (Application Example 2) 【0612】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0613】 In modern times, optimizing users' spending habits is a crucial issue, but few systems take into account the user's emotional state during this process. As a result, users risk making inefficient spending decisions influenced by their emotions. Furthermore, current spending management systems fail to offer spending suggestions tailored to the user's emotional state, making it difficult to increase user satisfaction. New technologies are needed to address this issue. 【0614】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0615】 In this invention, the server includes means for inputting user living expense data via an information processing device and storing the data in a database; means for classifying the stored living expense data by category and analyzing spending trends via the information processing device; and means for analyzing the user's emotional state from voice and images using an emotion analysis engine and utilizing that data. This makes it possible to make optimal spending suggestions that take into account the user's emotional state, effectively support the user's economic activities, and suppress unnecessary spending. 【0616】 An "information processing device" is a device that collects, stores, classifies, analyzes, and communicates data, and it handles user spending data. 【0617】 A "database" is a management system for storing living expense data collected and stored by information processing devices. 【0618】 "Spending trends" refer to tendencies and patterns derived from a user's past spending data, and are analytical results that can be used to plan future spending. 【0619】 A "emotion analysis engine" is a system that analyzes a user's emotional state from their voice and image data, and is used to optimize spending suggestions. 【0620】 "External information" refers to data from outside the user's network, such as market trends and economic indicators, that information processing devices utilize to optimize the user's spending activities. 【0621】 "Methods for automating payments" refer to systems that efficiently handle the settlement process for living expenses based on proposals approved by the user. 【0622】 The system that realizes this application example is built primarily using smartphones and servers. The smartphone is a device that provides an interface for users to input data on their living expenses. Smartphones are equipped with functions that analyze emotions from voice and image data using software such as Apple's Core ML or Google's TensorFlow Lite. 【0623】 When a user enters spending data using their smartphone, that data is transmitted to a database via the internet and stored on a server. The server categorizes the entered data and analyzes past data to identify spending trends. 【0624】 The server has an integrated emotion analysis engine that analyzes the user's emotional state based on voice and image data acquired from the user. Combining this analysis result with spending trends, the server utilizes a generative AI model to create personalized spending suggestions for each user. 【0625】 As a concrete example, consider a user who wants to engage in relaxation activities on the weekend. If this user is particularly stressed, the system uses data obtained from its emotion analysis engine to transmit that information to the server, which then suggests activities and products that can help reduce stress. These suggestions are sent from the server to the user's device, where the user can review them and approve or modify them as needed. 【0626】 An example of a prompt might be: "Consider the user's current emotional state and spending data, and generate suggestions for the most suitable activities from a stress-relieving and spending control perspective." This prompt allows the system to provide the user with optimal suggestions and support efficient spending management. 【0627】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0628】 Step 1: 【0629】 Users input their spending data using their smartphones. The entered data is temporarily stored in the device's local storage via the user interface. The data is then transmitted to a server via the internet using a secure protocol. The entered data includes items purchased, amounts, dates, and categories. 【0630】 Step 2: 【0631】 The server stores the received living expense data in a database. The server categorizes the data and analyzes spending trends by comparing it with past data. This analysis uses SQL queries to calculate average spending and standard scores for each category and detects abnormal spending. 【0632】 Step 3: 【0633】 The user's voice and image data are captured by their smartphone and input into an emotion analysis engine. The analysis engine uses a machine learning model (e.g., TensorFlow Lite) to analyze this data and estimate the user's emotional state. The output emotion data includes happiness levels, stress levels, excitement levels, and more. 【0634】 Step 4: 【0635】 The server integrates spending trends and sentiment data, leveraging a generative AI model to generate personalized spending suggestions. Based on prompts, the server activates the AI ​​model to calculate the best suggestions for the user. The generated suggestions include spending reduction strategies and relaxation methods, and are created based on the example prompts mentioned earlier. 【0636】 Step 5: 【0637】 The server generates a proposal and sends it to the user's smartphone. The user reviews the proposal via their device and approves or modifies it. If approved, the proposal is reflected in the automated payment system, and payment is processed automatically. 【0638】 Step 6: 【0639】 The device continuously monitors the user's emotional state and periodically sends this data to the server. The server updates suggestions in real time and generates new suggestions as needed. This continuous monitoring allows the user to receive dynamically optimized spending suggestions. 【0640】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0641】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0642】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0643】 [Fourth Embodiment] 【0644】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0645】 As shown in Figure 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. 【0646】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0647】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0648】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0649】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0650】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0651】 The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0652】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0653】 The specific processing program 56 is an example of a "program" relating to the technology of this 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 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. 【0654】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0655】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0656】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0657】 One embodiment of the present invention is an information processing system that efficiently collects and analyzes a user's living expenses data and provides optimal payment suggestions and automated payments. This system operates around an information processing device and supports a series of processes from user input to output. 【0658】 First, the user enters their personal spending data into the terminal. This data includes the date, purchased items, payment method, and amount. The terminal formats the entered data and sends it to the server. The server stores the received data in a database and categorizes it. 【0659】 Next, the server analyzes spending data categorized by type to reveal past spending trends. Furthermore, the server acquires discount and campaign data as external information via the network. This enables the server to suggest optimal economic activities that link the user's spending patterns with market opportunities. 【0660】 The server notifies the user of the generated proposal via the terminal. The user can review the proposal on the terminal and approve or modify its contents. If the user approves the proposal, the automated payment process begins. 【0661】 As a concrete example, consider a user who wants to reduce their monthly grocery spending. This system analyzes past grocery spending and suggests discount campaigns that take place on specific days of the week. It can also indicate that paying by credit card is the most effective method. Once the user approves this suggestion, the system automates payments on those days from then on, achieving the user's desired savings. 【0662】 Thus, the system realized by the present invention reduces the user's financial burden by streamlining the user's living expenses and providing an optimized payment plan. 【0663】 The following describes the processing flow. 【0664】 Step 1: 【0665】 The user enters their living expense data into the terminal. This includes the payment date, purchased items, payment method used, and amount. The terminal formats the entered data and prepares it for transmission to the server. 【0666】 Step 2: 【0667】 The terminal sends the formatted data to the server. The server stores the received data in a database. During storage, it checks the consistency and completeness of the data and verifies that there are no inconsistencies. 【0668】 Step 3: 【0669】 The server retrieves the stored data and classifies it by category. Expenditure data is divided into categories such as food, transportation, and entertainment, and the expenditure amount for each category is aggregated. 【0670】 Step 4: 【0671】 The server analyzes spending trends using data categorized by type. It compares spending over the past few months to identify seasonal variations and specific spending patterns. 【0672】 Step 5: 【0673】 The server retrieves discount and promotional information from external sources via the network. This information is then combined with the user's spending data to generate suggestions for optimal economic activity. 【0674】 Step 6: 【0675】 The server sends the generated suggestions to the terminal. The terminal notifies the user, and the suggestions are displayed on the dashboard. 【0676】 Step 7: 【0677】 The user reviews the proposal on their device and approves, modifies, or rejects it. If approved, that information is sent to the server. 【0678】 Step 8: 【0679】 If the user approves the proposal, the server will configure the automated payment settings. According to the settings, payments will be automatically executed when the conditions are met. 【0680】 Step 9: 【0681】 After the server completes the payment, it sends a notification to the user reporting their point acquisition status and payment history. 【0682】 (Example 1) 【0683】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0684】 In today's busy lifestyle, efficiently managing personal spending and engaging in optimal economic activities is a challenging task. Furthermore, conventional technologies have been insufficient in extracting useful information from vast amounts of data and presenting it to users as concrete suggestions. 【0685】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0686】 In this invention, the server includes means for receiving expenditure data from users and storing it in a storage medium, means for classifying the stored expenditure data and analyzing expenditure trends, and means for generating suggestions for optimal economic activities based on the analysis results and utilizing external information. This makes it possible to automate personal expenditure management and suggest optimal economic activities to users. 【0687】 An "information processing device" is an electronic computing device used for inputting, processing, storing, and communicating data. 【0688】 "User" refers to an individual who uses this system to manage their own spending data. 【0689】 "Expenditure data" refers to information that includes records of financial payments made in a user's life. 【0690】 A "storage medium" is a physical or virtual device used to store digital data. 【0691】 "Classification" refers to the process of dividing data into categories based on specific criteria. 【0692】 "Spending trends" refer to a series of patterns or trends in change that can be derived from past spending data. 【0693】 "Analysis" is the act of examining data in detail to clarify its meaning and relationships. 【0694】 "External information" refers to additional data or knowledge obtained from outside the system. 【0695】 "Proposals for economic activity" refer to suggestions that show users feasible ways to save money and manage their spending. 【0696】 "Transaction automation" is a function that automatically executes pre-configured processes based on approved proposals. 【0697】 This invention is an information processing system for users to efficiently manage their living expense data and perform optimal economic activities. The system consists of terminals, servers, and a network connecting them. 【0698】 First, the user uses a terminal to input their daily living expense data. The terminal formats the input data and sends it to the server via the internet. The terminal has an application installed that provides basic data input and transmission functions. 【0699】 The server stores the received spending data in a storage medium. Next, the server categorizes this data and analyzes spending trends using data analysis software such as Python's Pandas. This identifies the user's past spending patterns. 【0700】 Furthermore, the server acquires external information through the network. This external information includes discount and promotional information. The server combines this information with user spending data and uses a generative AI model to create optimal suggestions for economic activity. 【0701】 The server sends the generated proposal to the user's device. The user can review the proposal on the device and approve or modify it. Once the proposal is approved, the server executes payment automation and completes the transaction on behalf of the user. 【0702】 For example, if a user wants to optimize their monthly grocery spending, the system will analyze past grocery spending data and suggest an optimal shopping schedule utilizing specific discount campaigns. This suggestion may also recommend a specific payment method. An example of a prompt might be, "Analyze my past spending data and suggest the most effective ways to save money." 【0703】 This format allows users to automate expense management and reduce their financial burden. 【0704】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0705】 Step 1: 【0706】 The user enters their personal spending data into the terminal. The input data includes date, purchased items, payment method, and amount. The terminal converts this data into a predetermined format (e.g., JSON). As output, it generates formatted data and prepares it for transmission to the server. 【0707】 Step 2: 【0708】 The terminal sends formatted data to the server via the internet. This transmission is performed using HTTP or a RESTful API. The input is formatted data, and the output generates data packets that arrive at the server. This process allows the server to receive the data. 【0709】 Step 3: 【0710】 The server stores the received expenditure data in a storage medium. A database management system (e.g., MySQL) is used to ensure stable data storage. Input is formatted data sent from the terminal, and continuous data storage is performed as output. 【0711】 Step 4: 【0712】 The server categorizes the recorded spending data. It uses machine learning algorithms and predefined rules to separate the data into categories such as "groceries," "transportation," and "entertainment." The input is the raw recorded data, and the output is the generated categorized data. 【0713】 Step 5: 【0714】 The server analyzes classified spending data and calculates spending trends. Using the Python Pandas library, it identifies spending patterns for each category based on historical data. The input is classified data, and the output is a spending trend report. 【0715】 Step 6: 【0716】 The server retrieves discount and campaign information from external information sources. It uses scraping tools and public APIs to collect real-time market data. The input is an information request from an external source, and the output is actionable market information. 【0717】 Step 7: 【0718】 The server integrates the user's spending trends with external information and uses a generative AI model to create optimal economic activity suggestions. Using spending trend reports and external information as input, it generates a suggested plan as output. This provides specific savings strategies and purchasing methods. 【0719】 Step 8: 【0720】 The server sends the generated proposal to the terminal and notifies the user. The user reviews the proposal on the terminal and approves or modifies it through the UI. The proposed plan is used as input, and the notification displayed in the user interface is generated as output. 【0721】 Step 9: 【0722】 The user approves the proposal on their device. To approve, they press a confirmation button to send their approval to the server. The input is the proposal content, and the output is the approval status sent to the server. 【0723】 Step 10: 【0724】 The server automates payments based on user-approved proposals. It initiates transactions via the payment service's API and automates payments according to pre-configured settings. Inputs include approval status and proposal details, and the output generates an automated transaction record. 【0725】 (Application Example 1) 【0726】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0727】 Traditional spending management systems simply record and display user spending data, lacking the ability to efficiently optimize or automate payments. Furthermore, they insufficiently support users in maximizing their financial benefits by leveraging optimal market discount information, resulting in users being unable to effectively manage their own spending. 【0728】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0729】 In this invention, the server includes means for inputting user living expense data and storing it in a data storage device, means for classifying the stored living expense data according to classification criteria and analyzing spending trends, and means for generating optimal economic activity suggestions by utilizing external information based on the analysis results. This makes it possible to rationalize the user's economic behavior and maximize optimal payments and economic benefits by utilizing special offer information. 【0730】 An "information processing device" is a device that processes data received from a user and generates specific deliverables. 【0731】 "Living expense data" refers to information about a user's daily spending, including the date of purchase, item, payment method, and amount. 【0732】 A "data storage device" is a storage system for saving personal spending data. 【0733】 "Classifying according to classification criteria" is a method of organizing and classifying data based on specific categories or attributes. 【0734】 "Analyzing spending trends" is the process of analyzing past spending data to clarify users' consumption patterns and trends. 【0735】 "External information" refers to additional information that the system obtains through the internet or other networks without the user having to obtain it themselves. 【0736】 "Generating economic activity suggestions" is the process of creating optimal spending and saving plans for users and making suggestions to encourage their use. 【0737】 "Proposal notification" refers to the act of transmitting a proposal generated by an information processing device to the user's terminal. 【0738】 "Special sale information" refers to discounted products and promotional offers available in the market. 【0739】 "Optimizing economic benefits" means implementing suggestions and actions that allow users to obtain more value with less spending. 【0740】 The system for carrying out this invention consists of a user terminal, a server, and a data storage device. The user terminal provides an interface for inputting living expense data, which includes a smartphone application or a desktop application. The living expense data entered by the user is transmitted to the data storage device. 【0741】 The server runs on a cloud platform such as Amazon Web Services (AWS) and is implemented using programming languages ​​such as Python and Node.js. Amazon RDS is used for data storage. The server classifies the received living expense data according to classification criteria and analyzes spending trends using libraries such as pandas and NumPy. 【0742】 The server also continuously retrieves external information, particularly commercial discount information, using Web APIs. This information is combined with the user's past spending data to generate suggestions for optimal economic activity. 【0743】 The generated proposal is notified to the user's device via Firebase Cloud Messaging or similar means. The user reviews it and approves or modifies it. If the proposal is approved, the server initiates the automated payment process, which is executed automatically according to the set schedule for the specific payment. 【0744】 As a concrete example, a new campaign might be announced that allows users to earn more points when purchasing a product on certain days of the week. An example of a prompt for the generating AI model is: "Generate efficient suggestions for how users can save money on their weekday lunches. Provide advice based on the user's past payment data and the latest discount information." 【0745】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0746】 Step 1: 【0747】 The user enters their living expense data using a terminal. This data includes the purchase date, item name, payment method, and amount. The terminal formats this data and prepares it for transmission to the data storage device. 【0748】 Step 2: 【0749】 The server stores the living expense data received from the terminal in a data storage device. The server classifies the received data based on date, category, etc., and generates a list of data categorized by type using the pandas library. Based on this list, it analyzes spending trends and understands the user's consumption patterns. 【0750】 Step 3: 【0751】 The server uses external APIs to retrieve currently available discounts and promotional information. This information is then analyzed in conjunction with the user's past spending patterns to generate optimal economic activity suggestions. The NumPy library is used to calculate multiple possibilities and select the most relevant information. 【0752】 Step 4: 【0753】 The server uses Firebase Cloud Messaging to notify the user's device of the generated economic activity proposals. The user can review the proposals and either approve them or send feedback for revisions. 【0754】 Step 5: 【0755】 If the user approves the proposal, the server will initiate an automated payment process based on that information. Using various APIs, it will integrate with credit card and electronic payment systems and process payments according to the required schedule. At this point, the input is the user's approved proposal, and the output is the automated payment process. 【0756】 Step 6: 【0757】 The server reports to the user's terminal that the payment process is complete. It sends a payment confirmation message and updates the data storage device for analysis of the next spending pattern. 【0758】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0759】 This invention combines an information processing system that optimizes the management of a user's living expenses with an emotion engine that analyzes the user's emotions. By comprehensively analyzing the user's spending behavior and emotional state, this system can provide more personalized spending suggestions. 【0760】 This system begins with the user entering their living expenses data via a terminal. The entered data is formatted by the terminal and sent to the server. The server stores the received data in a database and categorizes it. Next, the server analyzes spending trends using historical data. 【0761】 Furthermore, a key feature of this invention is the incorporation of an emotion engine. This emotion engine analyzes the user's current emotional state through user input data, voice, and facial recognition. The server considers this emotional data together with spending data to generate spending suggestions tailored to the user's psychological tendencies. This makes it possible to provide advice, for example, to curb impulsive purchases that are more likely to occur when stress levels are high. 【0762】 The generated proposal is sent to the user via their device. The user reviews the proposal and approves or modifies it according to their circumstances. If approved, the server sets up automated payments based on the proposal. It can also continuously monitor the user's emotional state and update the proposal as needed. 【0763】 For example, if a user tends to feel stressed on weekends, the emotion engine will detect this and suggest low-cost activities that are effective for stress relief. This suggestion takes into account the user's spending trends and current emotional state, thereby increasing user satisfaction while reducing unnecessary spending. 【0764】 Thus, the present invention realizes a system that supports users' economic behavior from an emotional perspective as well, providing a new dimension to expenditure management. 【0765】 The following describes the processing flow. 【0766】 Step 1: 【0767】 The user enters their living expense data into the terminal. The terminal formats the data and prepares it for transmission to the server. The data includes the date of the expense, the item, the payment method, and the amount. 【0768】 Step 2: 【0769】 The terminal processes the data and sends it to the server. The server stores the received data in a database and categorizes it. An automated algorithm is used for classification. 【0770】 Step 3: 【0771】 The server analyzes past spending data to identify spending trends. It extracts seasonal variations and personal patterns from the data, thereby understanding the user's consumption behavior. 【0772】 Step 4: 【0773】 To understand the user's emotional state, the device captures voice and facial expressions and sends them to the emotion engine. The emotion engine analyzes the data to obtain the current emotional state. 【0774】 Step 5: 【0775】 The server analyzes spending data along with emotional states to generate optimal spending suggestions tailored to those emotions. These suggestions help curb impulsive purchases during stressful times and extra spending during moments of special joy. 【0776】 Step 6: 【0777】 The server sends the proposal to the terminal. The terminal notifies the user, and the proposal is displayed on the dashboard. The user can review the proposal and approve or modify it. 【0778】 Step 7: 【0779】 If the proposal is approved, the server will automate the payment process. Based on the automation settings, it will manage payments according to the configured conditions. 【0780】 Step 8: 【0781】 The server continuously monitors the user's emotional state and updates suggestions as needed. It optimizes suggestions based on emotional changes to support the user's economic behavior. 【0782】 Step 9: 【0783】 After the payment is completed, the server notifies the user of the result, reporting on the points earned and offering future suggestions. This allows users to manage their financial activities more effectively. 【0784】 (Example 2) 【0785】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0786】 In modern society, individual spending is diverse, making it difficult to provide consistent spending management and advice that addresses emotional fluctuations. Furthermore, advice based solely on simple economic data fails to consider an individual's psychological state, making effective spending management challenging. 【0787】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0788】 In this invention, the server includes means for inputting user spending data and storing it in a memory device, means for classifying the stored data into general-purpose data and analyzing spending trends, and means for acquiring the user's voice and images and performing sentiment analysis. This makes it possible to make suggestions that comprehensively consider individual spending trends and emotional states. 【0789】 An "information processing device" is a series of devices or systems that perform data input from users, data analysis, and proposal generation. 【0790】 A "user" is an individual who uses this system to input their living expense data and receives expense suggestions based on that data. 【0791】 "Living expenses data" refers to data that users input as a record of various expenses in their daily lives, including information on food expenses and utility bills. 【0792】 A "storage device" is a device used to temporarily or permanently store data on living expenses and analysis results, and includes database servers, etc. 【0793】 "General-purpose data" refers to general data that can be classified into various categories and is suitable for a wide range of uses rather than specific applications. 【0794】 "Spending trends" refer to spending patterns and economic behavior flows derived from past living expense data. 【0795】 "Emotional analysis" refers to the process of evaluating or estimating a user's psychological state using their voice and image data. 【0796】 "Recommendation generation" is the process of creating spending-related advice and plans for users based on analyzed data and emotional states. 【0797】 This invention is an information processing system that manages a user's living expenses and provides personalized suggestions through sentiment analysis. This system is implemented using a user's terminal, such as a smartphone or computer, and a server located in the cloud or locally. 【0798】 The terminal is responsible for inputting user spending data. This input is performed via a touchscreen display or keyboard, and the entered data is formatted on the terminal. The formatted data is then sent from the terminal to the server via the internet. For security reasons, it is recommended to use a secure communication method such as the HTTPS protocol. 【0799】 The server stores the received living expense data in a database. This database is built using a database management system such as SQL and is designed to efficiently classify and search user-specific data. On the server, the stored data is classified by category, and spending trends are analyzed using AI algorithms. The analysis employs statistical methods using historical spending data and machine learning models. 【0800】 Furthermore, the device collects the user's voice and image data. This data is sent to a server with the user's permission. On the server, an emotion analysis engine analyzes this data to evaluate the user's emotional state. This analysis utilizes voice recognition software and facial expression analysis tools. 【0801】 The server integrates spending data and sentiment data and uses a generative AI model to generate spending suggestions tailored to the user. The generation process uses prompts to instruct the AI ​​model; for example, "Consider the user's current sentiment state and spending tendencies, and suggest an appropriate spending plan." 【0802】 The generated proposals are notified to the user via their device, and the user can review them. If the proposal is approved, the server can automate payments or set up recurring expenses based on the proposal. 【0803】 For example, on weekends when users are more likely to feel stressed, the emotion analysis engine can suggest low-cost relaxing activities, helping to reduce unnecessary expenses. This makes it possible to improve user satisfaction while also providing economic benefits. 【0804】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0805】 Step 1: 【0806】 Users input their daily living expense data into a terminal. The input is in text format and includes expense details, amounts, and categories. The terminal formats the input data into a predetermined format and sends the result to the server. This process prepares the formatted data for reception on the server. 【0807】 Step 2: 【0808】 The server receives data sent from the terminal and stores it in the database. The database stores living expense data, which is stored using SQL queries. At this stage, the data is recorded and processed into a usable state, ready to be organized by category. 【0809】 Step 3: 【0810】 The server analyzes the stored spending data and categorizes it. Here, historical spending data is used to analyze user spending trends. Statistical analysis is performed using machine learning algorithms to calculate peak spending and average spending amounts. This analysis helps identify patterns in the user's economic activity and generates a trend report. 【0811】 Step 4: 【0812】 Users input their own voice and image data through a device. This data is used to evaluate their emotional state. The device sends the voice and image data to a server, and the results are input into an emotion analysis engine. 【0813】 Step 5: 【0814】 The server uses an emotion analysis engine to analyze transmitted audio and image data and identify the user's emotional state. Speech recognition software and facial expression analysis tools are used to determine the user's psychological state (e.g., stress, reassurance). The analysis results are output as an emotion report. 【0815】 Step 6: 【0816】 The server uses a generative AI model to integrate data from spending trend reports and sentiment reports to generate spending suggestions tailored to the user. The prompt "Consider the user's current sentiment state and spending trends, and suggest an appropriate spending plan" is used to instruct the AI ​​model to generate suggestions. These suggestions may include advice on reducing unnecessary spending and useful shopping lists. 【0817】 Step 7: 【0818】 The server sends the generated spending proposals to the terminal, which then notifies the user. The user reviews the proposals and approves or modifies them. Approved proposals are recorded by the server, and payment automation is set up based on the proposals. This creates an environment where users can engage in spending behavior in accordance with the proposals. 【0819】 (Application Example 2) 【0820】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0821】 In modern times, optimizing users' spending habits is a crucial issue, but few systems take into account the user's emotional state during this process. As a result, users risk making inefficient spending decisions influenced by their emotions. Furthermore, current spending management systems fail to offer spending suggestions tailored to the user's emotional state, making it difficult to increase user satisfaction. New technologies are needed to address this issue. 【0822】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0823】 In this invention, the server includes means for inputting user living expense data via an information processing device and storing the data in a database; means for classifying the stored living expense data by category and analyzing spending trends via the information processing device; and means for analyzing the user's emotional state from voice and images using an emotion analysis engine and utilizing that data. This makes it possible to make optimal spending suggestions that take into account the user's emotional state, effectively support the user's economic activities, and suppress unnecessary spending. 【0824】 An "information processing device" is a device that collects, stores, classifies, analyzes, and communicates data, and it handles user spending data. 【0825】 A "database" is a management system for storing living expense data collected and stored by information processing devices. 【0826】 "Spending trends" refer to tendencies and patterns derived from a user's past spending data, and are analytical results that can be used to plan future spending. 【0827】 A "emotion analysis engine" is a system that analyzes a user's emotional state from their voice and image data, and is used to optimize spending suggestions. 【0828】 "External information" refers to data from outside the user's network, such as market trends and economic indicators, that information processing devices utilize to optimize the user's spending activities. 【0829】 "Methods for automating payments" refer to systems that efficiently handle the settlement process for living expenses based on proposals approved by the user. 【0830】 The system that realizes this application example is built primarily using smartphones and servers. The smartphone is a device that provides an interface for users to input data on their living expenses. Smartphones are equipped with functions that analyze emotions from voice and image data using software such as Apple's Core ML or Google's TensorFlow Lite. 【0831】 When a user enters spending data using their smartphone, that data is transmitted to a database via the internet and stored on a server. The server categorizes the entered data and analyzes past data to identify spending trends. 【0832】 The server has an integrated emotion analysis engine that analyzes the user's emotional state based on voice and image data acquired from the user. Combining this analysis result with spending trends, the server utilizes a generative AI model to create personalized spending suggestions for each user. 【0833】 As a concrete example, consider a user who wants to engage in relaxation activities on the weekend. If this user is particularly stressed, the system uses data obtained from its emotion analysis engine to transmit that information to the server, which then suggests activities and products that can help reduce stress. These suggestions are sent from the server to the user's device, where the user can review them and approve or modify them as needed. 【0834】 An example of a prompt might be: "Consider the user's current emotional state and spending data, and generate suggestions for the most suitable activities from a stress-relieving and spending control perspective." This prompt allows the system to provide the user with optimal suggestions and support efficient spending management. 【0835】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0836】 Step 1: 【0837】 Users input their spending data using their smartphones. The entered data is temporarily stored in the device's local storage via the user interface. The data is then transmitted to a server via the internet using a secure protocol. The entered data includes items purchased, amounts, dates, and categories. 【0838】 Step 2: 【0839】 The server stores the received living expense data in a database. The server categorizes the data and analyzes spending trends by comparing it with past data. This analysis uses SQL queries to calculate average spending and standard scores for each category and detects abnormal spending. 【0840】 Step 3: 【0841】 The user's voice and image data are captured by their smartphone and input into an emotion analysis engine. The analysis engine uses a machine learning model (e.g., TensorFlow Lite) to analyze this data and estimate the user's emotional state. The output emotion data includes happiness levels, stress levels, excitement levels, and more. 【0842】 Step 4: 【0843】 The server integrates spending trends and sentiment data, leveraging a generative AI model to generate personalized spending suggestions. Based on prompts, the server activates the AI ​​model to calculate the best suggestions for the user. The generated suggestions include spending reduction strategies and relaxation methods, and are created based on the example prompts mentioned earlier. 【0844】 Step 5: 【0845】 The server generates a proposal and sends it to the user's smartphone. The user reviews the proposal via their device and approves or modifies it. If approved, the proposal is reflected in the automated payment system, and payment is processed automatically. 【0846】 Step 6: 【0847】 The device continuously monitors the user's emotional state and periodically sends this data to the server. The server updates suggestions in real time and generates new suggestions as needed. This continuous monitoring allows the user to receive dynamically optimized spending suggestions. 【0848】 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0849】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0850】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0851】 Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0852】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0853】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0854】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0855】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0856】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0857】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0858】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0859】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0860】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0861】 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. 【0862】 Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0863】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0864】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0865】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0866】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0867】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0868】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0869】 The following is further disclosed regarding the embodiments described above. 【0870】 (Claim 1) 【0871】 A means for inputting user living expense data using an information processing device and storing said data in a database, 【0872】 A means for classifying living expense data stored by an information processing device into categories and analyzing spending trends, 【0873】 A means of generating proposals for optimal economic activities by utilizing external information based on analysis results using an information processing device, 【0874】 A means for communicating a proposal generated by an information processing device to the user and obtaining the user's approval of the proposal, 【0875】 A means of automating payments based on user-approved proposals using an information processing device, 【0876】 A system that includes this. 【0877】 (Claim 2) 【0878】 The system according to claim 1, comprising an information processing device that determines discount campaigns and optimal payment schedules from the user's living expense data. 【0879】 (Claim 3) 【0880】 The system according to claim 1, comprising an information processing device that provides means for promoting point acquisition in conjunction with the user's payment history. 【0881】 "Example 1" 【0882】 (Claim 1) 【0883】 A means for receiving expenditure data from users using an information processing device and storing said data on a storage medium, 【0884】 A means for classifying expenditure data stored by an information processing device and analyzing expenditure trends, 【0885】 A means of generating proposals for optimal economic activities by utilizing external information based on analysis results using an information processing device, 【0886】 A means for transmitting a proposal generated by an information processing device to a user via a communication device and obtaining the user's confirmation of the proposal, 【0887】 A means of automating transactions based on proposals approved by users using an information processing device, 【0888】 A system that includes this. 【0889】 (Claim 2) 【0890】 The system according to claim 1, comprising an information processing device that provides means for formulating price reduction campaigns and optimal transaction plans from user spending data. 【0891】 (Claim 3) 【0892】 The system according to claim 1, comprising a means for promoting the acquisition of rewards in conjunction with the user's transaction history using an information processing device. 【0893】 "Application Example 1" 【0894】 (Claim 1) 【0895】 A means for inputting user living expense data using an information processing device and storing said data in a data storage device, 【0896】 A means for classifying living expenditure data stored by an information processing device according to classification criteria and analyzing expenditure trends, 【0897】 A means for generating proposals for optimal economic activities by utilizing external information based on analysis results using an information processing device, 【0898】 A means for communicating a proposal generated by an information processing device to the user and obtaining the user's approval of the proposal, 【0899】 A means of automating payments based on user-approved proposals using an information processing device, 【0900】 A means of sending a proposal notification to a user terminal using an information processing device, 【0901】 A means of optimizing economic benefits by combining user sales information and special offer information, 【0902】 A system that includes this. 【0903】 (Claim 2) 【0904】 The system according to claim 1, comprising an information processing device for determining discount campaigns and optimal payment plans from the user's living expense data. 【0905】 (Claim 3) 【0906】 The system according to claim 1, comprising means for promoting the acquisition of numerical values ​​in conjunction with the user's payment history using an information processing device. 【0907】 "Example 2 of combining an emotion engine" 【0908】 (Claim 1) 【0909】 A means for inputting living expense data from a user using an information processing device and storing said data in a storage device, 【0910】 A means for classifying living expense data stored by an information processing device into general-purpose data and analyzing spending trends, 【0911】 A means for generating proposals for optimal economic activities by utilizing external information based on analysis results using an information processing device, 【0912】 A means for presenting a proposal generated by an information processing device to a user via a communication device and obtaining the user's approval of the proposal, 【0913】 A means for acquiring user voice and images using an information processing device and analyzing the user's emotional state using an emotion analysis engine, 【0914】 A means for generating personalized economic activity proposals by integrating spending trend analysis with a user's emotional state using an information processing device, 【0915】 A means of automating payments based on user-approved proposals using an information processing device, 【0916】 A system that includes this. 【0917】 (Claim 2) 【0918】 The system according to claim 1, comprising an information processing device that provides means for determining discount promotions and appropriate payment schedules from the user's living expense data. 【0919】 (Claim 3) 【0920】 The system according to claim 1, comprising means for promoting point acquisition based on the user's payment history using an information processing device. 【0921】 "Application example 2 when combining with an emotional engine" 【0922】 (Claim 1) 【0923】 A means for inputting user living expense data using an information processing device and storing said data in a database, 【0924】 A means for classifying living expense data stored by an information processing device into categories and analyzing spending trends, 【0925】 A means of analyzing a user's emotional state from voice and images using an emotion analysis engine and utilizing that data, 【0926】 A means of generating proposals for optimal economic activities by utilizing external information based on analysis results and sentiment data using an information processing device, 【0927】 A means for communicating a proposal generated by an information processing device to the user and obtaining the user's approval of the proposal, 【0928】 A means of automating payments based on user-approved proposals using an information processing device, 【0929】 A system that includes this. 【0930】 (Claim 2) 【0931】 The system according to claim 1, comprising an information processing device that determines discount campaigns and optimal payment plans from the user's living expenses data, and means for suggesting activities according to the user's emotional state. 【0932】 (Claim 3) 【0933】 The system according to claim 1, comprising an information processing device that facilitates the acquisition of supplementary information linked to the user's payment history and emotional state. [Explanation of Symbols] 【0934】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means for inputting user living expense data using an information processing device and storing said data in a database, A means for classifying living expense data stored by an information processing device into categories and analyzing spending trends, A means of generating proposals for optimal economic activities by utilizing external information based on analysis results using an information processing device, A means for communicating a proposal generated by an information processing device to the user and obtaining the user's approval of the proposal, A means of automating payments based on user-approved proposals using an information processing device, A system that includes this. [Claim 2] The system according to claim 1, comprising a means for determining discount campaigns and an optimal payment schedule from the user's living expense data using an information processing device. [Claim 3] The system according to claim 1, comprising an information processing device that provides means for promoting point acquisition in conjunction with the user's payment history.

Images