system

Abstract

The system according to this embodiment aims to efficiently input household budget data and manage unnecessary expenses. [Solution] The system according to the embodiment comprises an input unit, an analysis unit, a suggestion unit, and a product suggestion unit. The input unit either takes a picture of a receipt with a camera or acquires data from an electronic payment system. The analysis unit analyzes the data acquired by the input unit and automatically inputs it into a household ledger. The suggestion unit suggests unnecessary spending based on the household ledger history analyzed by the analysis unit. The product suggestion unit suggests cheaper or better performing products for the user based on the unnecessary spending suggested by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that inputting a household ledger and managing wasteful expenses were laborious and difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently perform inputting a household ledger and managing wasteful expenses.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an input unit, an analysis unit, a suggestion unit, and a product suggestion unit. The input unit either photographs a receipt with a camera or acquires data from an electronic payment system. The analysis unit analyzes the data acquired by the input unit and automatically inputs it into a household ledger. The suggestion unit suggests unnecessary spending based on the household ledger history analyzed by the analysis unit. The product suggestion unit suggests cheaper or better performing products for the user based on the unnecessary spending suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently input household budgets and manage unnecessary expenses. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 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.

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

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The household budget support system according to an embodiment of the present invention is a system that supports household budget input using AI. This household budget support system allows the user to either photograph receipts with a camera or acquire data from an electronic payment system. Next, the AI ​​analyzes the input information and automatically enters it into the household budget. Furthermore, the AI ​​analyzes the household budget history and suggests unnecessary expenses. It also suggests cheaper or better-performing alternatives to items the user has purchased. This reduces the effort required to keep a household budget and helps reduce unnecessary spending. The user can efficiently manage their household finances with the support of AI. For example, the user photographs receipts with a camera or acquires data from an electronic payment system, and the AI ​​analyzes the information and enters it into the household budget. Next, the AI ​​analyzes the household budget history and suggests unnecessary expenses. For example, if there is a high expenditure in a particular category, it suggests ways to save money in that category. It also suggests cheaper or better-performing alternatives to items the user has purchased. This allows the user to reduce unnecessary spending and manage their household finances more efficiently. Thus, the household budget support system can streamline the user's household budget input and reduce unnecessary spending.

[0029] The household budget support system according to the embodiment comprises an input unit, an analysis unit, a suggestion unit, and a product suggestion unit. The input unit either photographs receipts with a camera or acquires data from an electronic payment system. For example, the input unit photographs receipts with a camera and saves them as image data. The input unit can also acquire data from an electronic payment system. For example, it can acquire data from an electronic payment system using an API. The analysis unit analyzes the data acquired by the input unit and automatically inputs it into the household budget. For example, the analysis unit converts the image data of receipts into text data using OCR technology. The analysis unit can also analyze data acquired from an electronic payment system and input it into the household budget. For example, the analysis unit converts the acquired data into a household budget format and automatically inputs it. The suggestion unit suggests unnecessary spending based on the household budget history analyzed by the analysis unit. For example, if there is a lot of spending in a particular category, the suggestion unit suggests ways to save in that category. The suggestion unit can also analyze the household budget history and make suggestions to reduce unnecessary spending. For example, the proposal department identifies unnecessary spending based on past spending data and proposes ways to reduce it. The product proposal department then suggests cheaper or better performing alternatives to the products the user has purchased, based on the unnecessary spending identified by the proposal department. For example, the product proposal department compares the prices of products the user has purchased and suggests the cheaper option. It can also compare the performance of products the user has purchased and suggest the better performing alternative. For example, the product proposal department suggests the most suitable product to the user based on product reviews and ratings. As a result, the household budget support system according to this embodiment can streamline the user's household budget entry and reduce unnecessary spending.

[0030] The input unit either takes a picture of the receipt with a camera or retrieves data from an electronic payment system. Specifically, when a user takes a picture of a receipt with a camera, the input unit saves the image data and sends it to the subsequent analysis unit. The camera can be a smartphone or a dedicated scanner, and the captured images are saved in high resolution. Furthermore, when retrieving data from an electronic payment system, an API is used to retrieve data securely and efficiently. For example, when a user makes a payment with a credit card or electronic money, the transaction data is automatically sent to the input unit. This significantly reduces manual data entry and improves data accuracy. In addition, the input unit has the function of integrating and centrally managing information from multiple data sources. This allows users to manage purchase history from different payment methods and stores in a single system. For example, it is possible to integrate data from both cash payments and electronic payments to understand overall spending patterns.

[0031] The analysis unit analyzes the data acquired by the input unit and automatically inputs it into the household budget ledger. Specifically, it uses OCR technology to convert image data of receipts into text data. OCR technology uses a character recognition algorithm to detect characters in an image and convert them into text format. In this process, it takes into account differences in receipt layout and font to perform highly accurate character recognition. The analysis unit can also analyze data acquired from electronic payment systems and input it into the household budget ledger. For example, it converts the acquired data into the household budget ledger format and inputs it automatically. This includes categorizing the data and extracting dates and amounts. Furthermore, the analysis unit has a function to check the integrity of the data, detect and correct duplicates and errors. This allows users to maintain an accurate and reliable household budget ledger. The analysis unit also performs trend analysis and generates statistical information based on past data. For example, it displays monthly spending trends and spending percentages in specific categories in graphs and charts, providing users with a visually easy-to-understand format.

[0032] The suggestion department proposes unnecessary spending based on household expense history analyzed by the analysis department. Specifically, if spending is high in a particular category, it suggests ways to save money in that category. For example, if food expenses are high, the suggestion department will suggest inexpensive ingredients and recipes and show ways to reduce eating out. The suggestion department can also analyze household expense history and make suggestions to reduce unnecessary spending. For example, based on past spending data, it can identify unnecessary spending and suggest ways to reduce it. This includes reviewing regular subscription services and canceling unnecessary insurance. Furthermore, the suggestion department uses AI to learn the user's spending patterns and provide individually optimized saving suggestions. For example, it analyzes the products and services the user frequently purchases and provides cheaper alternatives and discount information. In this way, the suggestion department can propose specific and practical saving methods tailored to the user's lifestyle, effectively reducing unnecessary spending.

[0033] The product recommendation department suggests cheaper or better-performing alternatives to products purchased by users based on the wasteful spending identified by the recommendation department. Specifically, it compares the prices of products purchased by users and suggests cheaper options. For example, it lists lower-priced items in the same category and notifies the user. The product recommendation department can also compare the performance of products purchased by users and suggest better-performing alternatives. For example, it evaluates the performance of home appliances and electronic devices and recommends cost-effective products. The product recommendation department suggests the most suitable products to users based on product reviews and ratings. This involves collecting data from online shopping sites and specialized review sites and using AI for evaluation. Furthermore, the product recommendation department learns the user's purchase history and preferences to provide individually customized suggestions. For example, users who prefer a particular brand or feature will be given priority in suggesting products that match their preferences. In this way, the product recommendation department can help users make smarter purchasing choices and reduce wasteful spending.

[0034] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit can prioritize suggesting input methods that the user has frequently used in the past (such as camera capture or electronic payment systems). The input unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the input unit can select the optimal input method based on the type of data the user has previously entered. This allows the system to provide the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0035] The input unit can filter receipts based on their type and content when a receipt is photographed. For example, the input unit can extract only the necessary information based on the type of receipt (e.g., groceries, clothing). It can also prioritize the acquisition of important information based on the content of the receipt (e.g., purchased items, amount). Furthermore, the input unit can perform filtering to minimize reading errors based on the receipt format. This allows for the efficient acquisition of necessary information based on the type and content of the receipt. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the receipt image data into a generating AI and have the generating AI perform the filtering.

[0036] The input unit can prioritize the acquisition of highly relevant data when a receipt is photographed, taking into account the user's geographical location information. For example, if the user made a purchase at a specific store, the input unit will prioritize acquiring receipts from that store. It can also prioritize acquiring receipts from a specific region if the user made a purchase in that region. Furthermore, if the user is traveling, the input unit can prioritize acquiring receipts from their travel destination. This allows for the acquisition of highly relevant data based on the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location data into a generating AI and have the generating AI acquire highly relevant data.

[0037] The input unit can analyze the user's social media activity and acquire relevant data when a receipt is photographed. For example, the input unit can acquire relevant receipts based on purchase information shared by the user on social media. The input unit can also prioritize acquiring receipts for locations checked in by the user on social media. Furthermore, the input unit can acquire receipts for products mentioned by the user on social media. This allows for the acquisition of relevant data based on the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media data into a generating AI and have the generating AI acquire the relevant data.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also adjust the level of detail of the analysis based on the data category (e.g., food expenses, transportation expenses). Furthermore, the analysis unit can adjust the level of detail of the analysis based on the user's level of interest. This allows the level of detail of the analysis to be adjusted based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating data importance into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a food expense-specific analysis algorithm to food expense data. It can also apply a transportation expense-specific analysis algorithm to transportation expense data. Furthermore, it can apply an entertainment expense-specific analysis algorithm to entertainment expense data. This allows the optimal analysis algorithm to be applied according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for identifying data categories into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0040] The analysis unit can determine the priority of analysis based on the data acquisition timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also prioritize the analysis of data from a specific period (e.g., the end of the month). Furthermore, the analysis unit can prioritize the analysis of data from a period specified by the user. This allows the analysis priority to be determined based on the data acquisition timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the data acquisition timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also prioritize the analysis of highly relevant data based on the user's level of interest. Furthermore, it can prioritize the analysis of highly relevant data based on the data category. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating data relevance into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The proposal unit can adjust the level of detail of its proposals based on the importance of the wasteful spending. For example, it can provide detailed proposals for important wasteful spending and simplified proposals for less important wasteful spending. The proposal unit can also adjust the level of detail based on the category of wasteful spending (e.g., food expenses, transportation expenses). Furthermore, it can adjust the level of detail based on the user's level of interest. This allows for adjustment of the level of detail based on the importance of the wasteful spending. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input data for evaluating the importance of wasteful spending into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0043] The proposal unit can apply different proposal algorithms depending on the category of wasteful spending when making a proposal. For example, for wasteful spending on food, the proposal unit can apply a proposal algorithm specifically for food expenses. It can also apply a proposal algorithm specifically for transportation expenses for wasteful spending on transportation expenses. Furthermore, it can apply a proposal algorithm specifically for entertainment expenses for wasteful spending on entertainment expenses. This allows the optimal proposal algorithm to be applied according to the category of wasteful spending. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data for identifying categories of wasteful spending into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0044] The proposal unit can prioritize proposals based on when the wasteful spending occurred. For example, the proposal unit may prioritize the most recent wasteful spending. It can also prioritize wasteful spending for a specific period (e.g., the end of the month). Furthermore, it can prioritize wasteful spending for a period specified by the user. This allows the proposal unit to prioritize proposals based on when the wasteful spending occurred. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input data for evaluating the timing of wasteful spending into a generating AI and have the generating AI determine the priority of proposals.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of the wasteful spending. For example, the suggestion unit can prioritize suggesting highly relevant wasteful spending. It can also prioritize suggesting highly relevant wasteful spending based on the user's level of interest. Furthermore, it can prioritize suggesting highly relevant wasteful spending based on the category of wasteful spending. This allows the order of suggestions to be adjusted based on the relevance of the wasteful spending. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data for evaluating the relevance of wasteful spending into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0046] The product suggestion unit can analyze the user's past purchase history to select the most suitable product when making product suggestions. For example, the product suggestion unit can suggest products similar to those the user has previously purchased. Furthermore, the product suggestion unit can prioritize suggesting products from specific brands or categories based on the user's past purchase history. In addition, the product suggestion unit can analyze the user's past purchase history and suggest products that match the user's preferences. This allows the product suggestion unit to propose the most suitable product based on the user's past purchase history. Some or all of the above processes in the product suggestion unit may be performed using AI, or not. For example, the product suggestion unit can input the user's past purchase history data into a generating AI and have the generating AI select the most suitable product.

[0047] The product suggestion unit can customize the types of products it suggests based on the user's current living situation. For example, if a user has moved to a new house, the product suggestion unit can suggest products related to moving. It can also prioritize suggesting baby products if the user has children. Furthermore, if a user has a specific event coming up (e.g., a wedding), the product suggestion unit can suggest products related to that event. This allows the product suggestion unit to recommend the most suitable products based on the user's current living situation. Some or all of the above processing in the product suggestion unit may be performed using AI, for example, or not. For example, the product suggestion unit can input user living situation data into a generating AI and have the generating AI perform the customization of product types.

[0048] The product suggestion unit can suggest the most suitable products by considering the user's geographical location when making product suggestions. For example, if the user is in a specific region, the product suggestion unit can suggest popular products in that region. Furthermore, if the user is traveling, the product suggestion unit can suggest products that will be useful at their travel destination. Additionally, if the user is near a specific store, the product suggestion unit can suggest products available at that store. This allows the product suggestion unit to suggest the most suitable products based on the user's geographical location. Some or all of the above processing in the product suggestion unit may be performed using AI, for example, or without AI. For example, the product suggestion unit can input the user's geographical location data into a generating AI and have the generating AI suggest the most suitable products.

[0049] The product suggestion department can analyze a user's social media activity and suggest relevant products when making product suggestions. For example, the product suggestion department can suggest products related to products the user has shared on social media. It can also suggest products from brands the user has mentioned on social media. Furthermore, the product suggestion department can suggest products recommended by influencers the user follows on social media. This allows the department to suggest relevant products based on the user's social media activity. Some or all of the above processes in the product suggestion department may be performed using AI, for example, or not. For example, the product suggestion department can input the user's social media data into a generating AI and have the generating AI generate suggestions for relevant products.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The household budget support system can also acquire the user's health data and suggest spending based on their health status. For example, by acquiring the user's step count and heart rate data, it can suggest gym membership fees or the purchase of fitness equipment if it detects a lack of exercise. It can also acquire the user's diet data and suggest the purchase of health foods or supplements if their nutritional balance is poor. Furthermore, by acquiring the user's sleep data and suggesting the use of sleep aids or relaxation services if their sleep quality is poor, it can make spending suggestions that support a healthier lifestyle based on the user's health status.

[0052] The household budget support system can also acquire the user's family structure data and make spending suggestions based on the family situation. For example, if the user has children, it can suggest education expenses and the purchase of children's goods. If the user lives with elderly people, it can also suggest the use of care products and health management services. Furthermore, if the user has pets, it can suggest pet supplies and pet insurance. This allows for more appropriate spending suggestions tailored to the user's family structure.

[0053] The household budget support system can further analyze the user's purchase history and make spending suggestions based on their purchasing patterns. For example, if a user frequently purchases products from a particular brand, it can suggest sales information and new products from that brand. It can also suggest subscription services if a user regularly purchases products from a specific category. Furthermore, it can suggest similar products based on reviews of items the user has previously purchased. This allows for more appropriate spending suggestions based on the user's purchasing patterns.

[0054] The household budget support system can also acquire the user's geographical location and provide spending suggestions based on that location. For example, if the user is in a specific area, it can suggest popular products and services in that area. If the user is traveling, it can also suggest products and services that would be useful at their destination. Furthermore, if the user is near a specific store, it can suggest coupons and sales information available at that store. This allows for more appropriate spending suggestions based on the user's geographical location.

[0055] The household budget support system can further analyze users' social media activity and provide spending suggestions based on that activity. For example, it can suggest related products and services based on purchase information shared by users on social media. It can also suggest coupons and sales information available near locations where users have checked in on social media. Furthermore, it can suggest similar products based on reviews and ratings of products mentioned by users on social media. This enables more appropriate spending suggestions based on users' social media activity.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The input unit either takes a picture of the receipt with a camera or retrieves data from an electronic payment system. For example, it can take a picture of the receipt with a camera and save it as image data. Alternatively, it can retrieve data from an electronic payment system using an API. Step 2: The analysis unit analyzes the data acquired by the input unit and automatically inputs it into the household ledger. For example, it uses OCR technology to convert image data of receipts into text data. It also converts data acquired from electronic payment systems into the household ledger format and automatically inputs it. Step 3: The proposal unit suggests unnecessary spending based on the household budget history analyzed by the analysis unit. For example, if there is excessive spending in a particular category, it will suggest ways to save money in that category. It will also identify unnecessary spending based on past spending data and suggest ways to reduce it. Step 4: The product suggestion department proposes cheaper or better-performing alternatives to the products the user has purchased, based on the wasteful spending identified by the suggestion department. For example, they compare the prices of products the user has purchased and suggest cheaper alternatives. They also suggest better-performing alternatives based on product reviews and ratings.

[0058] (Example of form 2) The household budget support system according to an embodiment of the present invention is a system that supports household budget input using AI. This household budget support system allows the user to either photograph receipts with a camera or acquire data from an electronic payment system. Next, the AI ​​analyzes the input information and automatically enters it into the household budget. Furthermore, the AI ​​analyzes the household budget history and suggests unnecessary expenses. It also suggests cheaper or better-performing alternatives to items the user has purchased. This reduces the effort required to keep a household budget and helps reduce unnecessary spending. The user can efficiently manage their household finances with the support of AI. For example, the user photographs receipts with a camera or acquires data from an electronic payment system, and the AI ​​analyzes the information and enters it into the household budget. Next, the AI ​​analyzes the household budget history and suggests unnecessary expenses. For example, if there is a high expenditure in a particular category, it suggests ways to save money in that category. It also suggests cheaper or better-performing alternatives to items the user has purchased. This allows the user to reduce unnecessary spending and manage their household finances more efficiently. Thus, the household budget support system can streamline the user's household budget input and reduce unnecessary spending.

[0059] The household budget support system according to the embodiment comprises an input unit, an analysis unit, a suggestion unit, and a product suggestion unit. The input unit either photographs receipts with a camera or acquires data from an electronic payment system. For example, the input unit photographs receipts with a camera and saves them as image data. The input unit can also acquire data from an electronic payment system. For example, it can acquire data from an electronic payment system using an API. The analysis unit analyzes the data acquired by the input unit and automatically inputs it into the household budget. For example, the analysis unit converts the image data of receipts into text data using OCR technology. The analysis unit can also analyze data acquired from an electronic payment system and input it into the household budget. For example, the analysis unit converts the acquired data into a household budget format and automatically inputs it. The suggestion unit suggests unnecessary spending based on the household budget history analyzed by the analysis unit. For example, if there is a lot of spending in a particular category, the suggestion unit suggests ways to save in that category. The suggestion unit can also analyze the household budget history and make suggestions to reduce unnecessary spending. For example, the proposal department identifies unnecessary spending based on past spending data and proposes ways to reduce it. The product proposal department then suggests cheaper or better performing alternatives to the products the user has purchased, based on the unnecessary spending identified by the proposal department. For example, the product proposal department compares the prices of products the user has purchased and suggests the cheaper option. It can also compare the performance of products the user has purchased and suggest the better performing alternative. For example, the product proposal department suggests the most suitable product to the user based on product reviews and ratings. As a result, the household budget support system according to this embodiment can streamline the user's household budget entry and reduce unnecessary spending.

[0060] The input unit either takes a picture of the receipt with a camera or retrieves data from an electronic payment system. Specifically, when a user takes a picture of a receipt with a camera, the input unit saves the image data and sends it to the subsequent analysis unit. The camera can be a smartphone or a dedicated scanner, and the captured images are saved in high resolution. Furthermore, when retrieving data from an electronic payment system, an API is used to retrieve data securely and efficiently. For example, when a user makes a payment with a credit card or electronic money, the transaction data is automatically sent to the input unit. This significantly reduces manual data entry and improves data accuracy. In addition, the input unit has the function of integrating and centrally managing information from multiple data sources. This allows users to manage purchase history from different payment methods and stores in a single system. For example, it is possible to integrate data from both cash payments and electronic payments to understand overall spending patterns.

[0061] The analysis unit analyzes the data acquired by the input unit and automatically inputs it into the household budget ledger. Specifically, it uses OCR technology to convert image data of receipts into text data. OCR technology uses a character recognition algorithm to detect characters in an image and convert them into text format. In this process, it takes into account differences in receipt layout and font to perform highly accurate character recognition. The analysis unit can also analyze data acquired from electronic payment systems and input it into the household budget ledger. For example, it converts the acquired data into the household budget ledger format and inputs it automatically. This includes categorizing the data and extracting dates and amounts. Furthermore, the analysis unit has a function to check the integrity of the data, detect and correct duplicates and errors. This allows users to maintain an accurate and reliable household budget ledger. The analysis unit also performs trend analysis and generates statistical information based on past data. For example, it displays monthly spending trends and spending percentages in specific categories in graphs and charts, providing users with a visually easy-to-understand format.

[0062] The suggestion department proposes unnecessary spending based on household expense history analyzed by the analysis department. Specifically, if spending is high in a particular category, it suggests ways to save money in that category. For example, if food expenses are high, the suggestion department will suggest inexpensive ingredients and recipes and show ways to reduce eating out. The suggestion department can also analyze household expense history and make suggestions to reduce unnecessary spending. For example, based on past spending data, it can identify unnecessary spending and suggest ways to reduce it. This includes reviewing regular subscription services and canceling unnecessary insurance. Furthermore, the suggestion department uses AI to learn the user's spending patterns and provide individually optimized saving suggestions. For example, it analyzes the products and services the user frequently purchases and provides cheaper alternatives and discount information. In this way, the suggestion department can propose specific and practical saving methods tailored to the user's lifestyle, effectively reducing unnecessary spending.

[0063] The product recommendation department suggests cheaper or better-performing alternatives to products purchased by users based on the wasteful spending identified by the recommendation department. Specifically, it compares the prices of products purchased by users and suggests cheaper options. For example, it lists lower-priced items in the same category and notifies the user. The product recommendation department can also compare the performance of products purchased by users and suggest better-performing alternatives. For example, it evaluates the performance of home appliances and electronic devices and recommends cost-effective products. The product recommendation department suggests the most suitable products to users based on product reviews and ratings. This involves collecting data from online shopping sites and specialized review sites and using AI for evaluation. Furthermore, the product recommendation department learns the user's purchase history and preferences to provide individually customized suggestions. For example, users who prefer a particular brand or feature will be given priority in suggesting products that match their preferences. In this way, the product recommendation department can help users make smarter purchasing choices and reduce wasteful spending.

[0064] The input unit can estimate the user's emotions and adjust the timing of receipt photography and data acquisition based on the estimated emotions. For example, if the user is stressed, the AI ​​in the input unit can delay the timing of receipt photography and prompt the user to take the photo when they are relaxed. If the user is in a hurry, the AI ​​in the input unit can immediately prompt the user to take the receipt photo and acquire the data quickly. Furthermore, if the user is tired, the AI ​​in the input unit can automatically acquire data from the electronic payment system, saving the user time. This allows for data acquisition at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0065] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit can prioritize suggesting input methods that the user has frequently used in the past (such as camera capture or electronic payment systems). The input unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the input unit can select the optimal input method based on the type of data the user has previously entered. This allows the system to provide the optimal input method based on the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0066] The input unit can filter receipts based on their type and content when a receipt is photographed. For example, the input unit can extract only the necessary information based on the type of receipt (e.g., groceries, clothing). It can also prioritize the acquisition of important information based on the content of the receipt (e.g., purchased items, amount). Furthermore, the input unit can perform filtering to minimize reading errors based on the receipt format. This allows for the efficient acquisition of necessary information based on the type and content of the receipt. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the receipt image data into a generating AI and have the generating AI perform the filtering.

[0067] The input unit can estimate the user's emotions and determine the priority of the data to be input based on the estimated emotions. For example, if the user is stressed, the input unit will prioritize inputting only important data. If the user is relaxed, the input unit can also input detailed data. Furthermore, if the user is in a hurry, the input unit can prioritize data that can be entered quickly. This allows the data priority to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, or not using AI. For example, the input unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] The input unit can prioritize the acquisition of highly relevant data when a receipt is photographed, taking into account the user's geographical location information. For example, if the user made a purchase at a specific store, the input unit will prioritize acquiring receipts from that store. It can also prioritize acquiring receipts from a specific region if the user made a purchase in that region. Furthermore, if the user is traveling, the input unit can prioritize acquiring receipts from their travel destination. This allows for the acquisition of highly relevant data based on the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location data into a generating AI and have the generating AI acquire highly relevant data.

[0069] The input unit can analyze the user's social media activity and acquire relevant data when a receipt is photographed. For example, the input unit can acquire relevant receipts based on purchase information shared by the user on social media. The input unit can also prioritize acquiring receipts for locations checked in by the user on social media. Furthermore, the input unit can acquire receipts for products mentioned by the user on social media. This allows for the acquisition of relevant data based on the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media data into a generating AI and have the generating AI acquire the relevant data.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. This allows the presentation of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also adjust the level of detail of the analysis based on the data category (e.g., food expenses, transportation expenses). Furthermore, the analysis unit can adjust the level of detail of the analysis based on the user's level of interest. This allows the level of detail of the analysis to be adjusted based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating data importance into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a food expense-specific analysis algorithm to food expense data. It can also apply a transportation expense-specific analysis algorithm to transportation expense data. Furthermore, it can apply an entertainment expense-specific analysis algorithm to entertainment expense data. This allows the optimal analysis algorithm to be applied according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for identifying data categories into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the length of the analysis result to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can determine the priority of analysis based on the data acquisition timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also prioritize the analysis of data from a specific period (e.g., the end of the month). Furthermore, the analysis unit can prioritize the analysis of data from a period specified by the user. This allows the analysis priority to be determined based on the data acquisition timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating the data acquisition timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also prioritize the analysis of highly relevant data based on the user's level of interest. Furthermore, it can prioritize the analysis of highly relevant data based on the data category. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data for evaluating data relevance into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0076] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions. This allows the suggestion unit to adjust its presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The proposal unit can adjust the level of detail of its proposals based on the importance of the wasteful spending. For example, it can provide detailed proposals for important wasteful spending and simplified proposals for less important wasteful spending. The proposal unit can also adjust the level of detail based on the category of wasteful spending (e.g., food expenses, transportation expenses). Furthermore, it can adjust the level of detail based on the user's level of interest. This allows for adjustment of the level of detail based on the importance of the wasteful spending. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input data for evaluating the importance of wasteful spending into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0078] The proposal unit can apply different proposal algorithms depending on the category of wasteful spending when making a proposal. For example, for wasteful spending on food, the proposal unit can apply a proposal algorithm specifically for food expenses. It can also apply a proposal algorithm specifically for transportation expenses for wasteful spending on transportation expenses. Furthermore, it can apply a proposal algorithm specifically for entertainment expenses for wasteful spending on entertainment expenses. This allows the optimal proposal algorithm to be applied according to the category of wasteful spending. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data for identifying categories of wasteful spending into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0079] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, it can also provide detailed suggestions. Furthermore, if the user is excited, it can provide visually stimulating suggestions. This allows the length of suggestions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The proposal unit can prioritize proposals based on when the wasteful spending occurred. For example, the proposal unit may prioritize the most recent wasteful spending. It can also prioritize wasteful spending for a specific period (e.g., the end of the month). Furthermore, it can prioritize wasteful spending for a period specified by the user. This allows the proposal unit to prioritize proposals based on when the wasteful spending occurred. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input data for evaluating the timing of wasteful spending into a generating AI and have the generating AI determine the priority of proposals.

[0081] The suggestion unit can adjust the order of suggestions based on the relevance of the wasteful spending. For example, the suggestion unit can prioritize suggesting highly relevant wasteful spending. It can also prioritize suggesting highly relevant wasteful spending based on the user's level of interest. Furthermore, it can prioritize suggesting highly relevant wasteful spending based on the category of wasteful spending. This allows the order of suggestions to be adjusted based on the relevance of the wasteful spending. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data for evaluating the relevance of wasteful spending into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0082] The product suggestion unit can estimate the user's emotions and adjust its product suggestion method based on those emotions. For example, if the user is stressed, the product suggestion unit can provide simple and highly visible product suggestions. If the user is relaxed, it can also provide detailed product suggestions. Furthermore, if the user is in a hurry, it can provide concise product suggestions. This allows the product suggestion method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the product suggestion unit may be performed using AI or not. For example, the product suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The product suggestion unit can analyze the user's past purchase history to select the most suitable product when making product suggestions. For example, the product suggestion unit can suggest products similar to those the user has previously purchased. Furthermore, the product suggestion unit can prioritize suggesting products from specific brands or categories based on the user's past purchase history. In addition, the product suggestion unit can analyze the user's past purchase history and suggest products that match the user's preferences. This allows the product suggestion unit to propose the most suitable product based on the user's past purchase history. Some or all of the above processes in the product suggestion unit may be performed using AI, or not. For example, the product suggestion unit can input the user's past purchase history data into a generating AI and have the generating AI select the most suitable product.

[0084] The product suggestion unit can customize the types of products it suggests based on the user's current living situation. For example, if a user has moved to a new house, the product suggestion unit can suggest products related to moving. It can also prioritize suggesting baby products if the user has children. Furthermore, if a user has a specific event coming up (e.g., a wedding), the product suggestion unit can suggest products related to that event. This allows the product suggestion unit to recommend the most suitable products based on the user's current living situation. Some or all of the above processing in the product suggestion unit may be performed using AI, for example, or not. For example, the product suggestion unit can input user living situation data into a generating AI and have the generating AI perform the customization of product types.

[0085] The product recommendation unit can estimate the user's emotions and determine the priority of products to recommend based on the estimated emotions. For example, if the user is stressed, the product recommendation unit will prioritize recommending products with a relaxing effect. If the user is relaxed, the product recommendation unit can also prioritize recommending products that pique their interest. Furthermore, if the user is in a hurry, the product recommendation unit can prioritize recommending products that can be purchased quickly. This allows for adjusting product priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the product recommendation unit may be performed using AI, or not. For example, the product recommendation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The product suggestion unit can suggest the most suitable products by considering the user's geographical location when making product suggestions. For example, if the user is in a specific region, the product suggestion unit can suggest popular products in that region. Furthermore, if the user is traveling, the product suggestion unit can suggest products that will be useful at their travel destination. Additionally, if the user is near a specific store, the product suggestion unit can suggest products available at that store. This allows the product suggestion unit to suggest the most suitable products based on the user's geographical location. Some or all of the above processing in the product suggestion unit may be performed using AI, for example, or without AI. For example, the product suggestion unit can input the user's geographical location data into a generating AI and have the generating AI suggest the most suitable products.

[0087] The product suggestion department can analyze a user's social media activity and suggest relevant products when making product suggestions. For example, the product suggestion department can suggest products related to products the user has shared on social media. It can also suggest products from brands the user has mentioned on social media. Furthermore, the product suggestion department can suggest products recommended by influencers the user follows on social media. This allows the department to suggest relevant products based on the user's social media activity. Some or all of the above processes in the product suggestion department may be performed using AI, for example, or not. For example, the product suggestion department can input the user's social media data into a generating AI and have the generating AI generate suggestions for relevant products.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The household budget support system can also acquire the user's health data and suggest spending based on their health status. For example, by acquiring the user's step count and heart rate data, it can suggest gym membership fees or the purchase of fitness equipment if it detects a lack of exercise. It can also acquire the user's diet data and suggest the purchase of health foods or supplements if their nutritional balance is poor. Furthermore, by acquiring the user's sleep data and suggesting the use of sleep aids or relaxation services if their sleep quality is poor, it can make spending suggestions that support a healthier lifestyle based on the user's health status.

[0090] The household budget support system can further estimate the user's emotions and make spending suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest purchasing relaxation services or stress-relieving goods. If the user is relaxed, it can also suggest purchasing items related to hobbies and entertainment. Furthermore, if the user is excited, it can suggest purchasing tickets for activities or events. This allows for more appropriate spending suggestions based on the user's emotions.

[0091] The household budget support system can also acquire the user's family structure data and make spending suggestions based on the family situation. For example, if the user has children, it can suggest education expenses and the purchase of children's goods. If the user lives with elderly people, it can also suggest the use of care products and health management services. Furthermore, if the user has pets, it can suggest pet supplies and pet insurance. This allows for more appropriate spending suggestions tailored to the user's family structure.

[0092] The household budget support system can further estimate the user's emotions and adjust the display method of the household budget based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible household budget display. If the user is relaxed, it can provide a detailed household budget display. Furthermore, if the user is in a hurry, it can provide a concise household budget display. In this way, the display method of the household budget can be adjusted according to the user's emotions.

[0093] The household budget support system can further analyze the user's purchase history and make spending suggestions based on their purchasing patterns. For example, if a user frequently purchases products from a particular brand, it can suggest sales information and new products from that brand. It can also suggest subscription services if a user regularly purchases products from a specific category. Furthermore, it can suggest similar products based on reviews of items the user has previously purchased. This allows for more appropriate spending suggestions based on the user's purchasing patterns.

[0094] The household budget support system can further estimate the user's emotions and prioritize spending based on those emotions. For example, if the user is stressed, it can prioritize spending related to relaxation and stress relief. If the user is relaxed, it can prioritize spending related to hobbies and entertainment. Furthermore, if the user is in a hurry, it can prioritize spending that can be handled quickly. This allows the system to adjust spending priorities according to the user's emotions.

[0095] The household budget support system can also acquire the user's geographical location and provide spending suggestions based on that location. For example, if the user is in a specific area, it can suggest popular products and services in that area. If the user is traveling, it can also suggest products and services that would be useful at their destination. Furthermore, if the user is near a specific store, it can suggest coupons and sales information available at that store. This allows for more appropriate spending suggestions based on the user's geographical location.

[0096] The household budget support system can also estimate the user's emotions and provide spending feedback based on those emotions. For example, if the user is stressed, it can provide spending feedback in simple, positive terms. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is in a hurry, it can provide concise feedback. This allows the system to adjust spending feedback according to the user's emotions.

[0097] The household budget support system can further analyze users' social media activity and provide spending suggestions based on that activity. For example, it can suggest related products and services based on purchase information shared by users on social media. It can also suggest coupons and sales information available near locations where users have checked in on social media. Furthermore, it can suggest similar products based on reviews and ratings of products mentioned by users on social media. This enables more appropriate spending suggestions based on users' social media activity.

[0098] The household budget support system can further estimate the user's emotions and provide spending alerts based on those emotions. For example, if the user is stressed, it can provide spending alerts in a simple and highly visible format. If the user is relaxed, it can provide more detailed alerts. Furthermore, if the user is in a hurry, it can provide concise alerts. This allows the system to adjust spending alerts according to the user's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The input unit either takes a picture of the receipt with a camera or retrieves data from an electronic payment system. For example, it can take a picture of the receipt with a camera and save it as image data. Alternatively, it can retrieve data from an electronic payment system using an API. Step 2: The analysis unit analyzes the data acquired by the input unit and automatically inputs it into the household ledger. For example, it uses OCR technology to convert image data of receipts into text data. It also converts data acquired from electronic payment systems into the household ledger format and automatically inputs it. Step 3: The proposal unit suggests unnecessary spending based on the household budget history analyzed by the analysis unit. For example, if there is excessive spending in a particular category, it will suggest ways to save money in that category. It will also identify unnecessary spending based on past spending data and suggest ways to reduce it. Step 4: The product suggestion department proposes cheaper or better-performing alternatives to the products the user has purchased, based on the wasteful spending identified by the suggestion department. For example, they compare the prices of products the user has purchased and suggest cheaper alternatives. They also suggest better-performing alternatives based on product reviews and ratings.

[0101] 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.

[0102] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the input unit, analysis unit, proposal unit, and product proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit takes a picture of the receipt using the camera 42 of the smart device 14 and acquires data from the electronic payment system using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and converts the image data of the receipt into text data using OCR technology and inputs it into the household account book. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the household account book history to suggest unnecessary spending. The product proposal unit is implemented in the control unit 46A of the smart device 14, for example, and suggests cheaper or better performing products for the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0108] 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.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0110] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] 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.

[0112] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the input unit, analysis unit, suggestion unit, and product suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit uses the camera 42 of the smart glasses 214 to photograph a receipt and the specific processing unit 290 of the data processing unit 12 acquires data from an electronic payment system. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and uses OCR technology to convert the image data of the receipt into text data and input it into the household account book. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the household account book history to suggest unnecessary spending. The product suggestion unit is implemented in the control unit 46A of the smart glasses 214, for example, and suggests cheaper or better performing versions of the products the user has purchased. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0124] 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.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0126] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] 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.

[0128] 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.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the input unit, analysis unit, suggestion unit, and product suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit uses the camera 42 of the headset terminal 314 to photograph the receipt and the specific processing unit 290 of the data processing unit 12 acquires data from the electronic payment system. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and uses OCR technology to convert the image data of the receipt into text data and input it into the household account book. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the household account book history to suggest unnecessary spending. The product suggestion unit is implemented in the control unit 46A of the headset terminal 314, for example, and suggests cheaper or better performing products for the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0140] 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0142] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] 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.

[0144] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] 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.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the input unit, analysis unit, proposal unit, and product proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the input unit uses the camera 42 of the robot 414 to photograph a receipt and the specific processing unit 290 of the data processing unit 12 acquires data from an electronic payment system. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses OCR technology to convert the image data of the receipt into text data and inputs it into a household ledger. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the household ledger history and proposes unnecessary spending. The product proposal unit is implemented by, for example, the control unit 46A of the robot 414, which proposes cheaper or better performing products for the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] 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.

[0155] Figure 9 shows the 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.

[0156] 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.

[0157] 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.

[0158] 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, and motorcycles, 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 based, for example, 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.

[0159] 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."

[0160] 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.

[0161] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] 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 other things 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.

[0171] 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 to be incorporated by reference.

[0172] (Note 1) An input unit that either takes a picture of the receipt with a camera or retrieves data from an electronic payment system, The analysis unit analyzes the data acquired by the input unit and automatically inputs it into the household ledger, Based on the household expense history analyzed by the aforementioned analysis unit, a proposal unit proposes unnecessary spending, The system includes a product suggestion unit that suggests cheaper or better performing products to the user based on the wasteful spending suggested by the suggestion unit. A system characterized by the following features. (Note 2) The aforementioned input unit is The system estimates the user's emotions and adjusts the timing of receipt scanning and data acquisition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned input unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned input unit is When taking a picture of a receipt, the system filters the receipt based on its type and content. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned input unit is It estimates the user's emotions and determines the priority of input data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned input unit is When scanning receipts, the system prioritizes acquiring highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is When a receipt is photographed, the system analyzes the user's social media activity and obtains relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the wasteful spending. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of wasteful spending. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, prioritize them based on when the wasteful spending occurs. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the wasteful spending. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned product proposal department, It estimates the user's emotions and adjusts the product suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned product proposal department, When proposing products, we analyze the user's past purchase history to select the most suitable product. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned product proposal department, When suggesting products, customize the types of products suggested based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned product proposal department, It estimates the user's emotions and determines the priority of suggested products based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned product proposal department, When proposing products, we take the user's geographical location into consideration to suggest the most suitable products. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned product proposal department, When proposing products, we analyze the user's social media activity and suggest relevant products. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input unit that either takes a picture of the receipt with a camera or retrieves data from an electronic payment system, The analysis unit analyzes the data acquired by the input unit and automatically inputs it into the household ledger, Based on the household expense history analyzed by the aforementioned analysis unit, a proposal unit proposes unnecessary spending, The system includes a product suggestion unit that suggests cheaper or better performing products to the user based on the wasteful spending suggested by the suggestion unit. A system characterized by the following features.

2. The aforementioned input unit is The system estimates the user's emotions and adjusts the timing of receipt scanning and data acquisition based on those estimated emotions. The system according to feature 1.

3. The aforementioned input unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

4. The aforementioned input unit is When taking a picture of a receipt, the system filters the receipt based on its type and content. The system according to feature 1.

5. The aforementioned input unit is It estimates the user's emotions and determines the priority of input data based on the estimated user emotions. The system according to feature 1.

6. The aforementioned input unit is When scanning receipts, the system prioritizes acquiring highly relevant data by considering the user's geographical location. The system according to feature 1.

7. The aforementioned input unit is When a receipt is photographed, the system analyzes the user's social media activity and obtains relevant data. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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