system

Abstract

The system according to the embodiment aims to propose optimal products and services based on the detailed needs of the user. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a feedback unit. The collection unit collects a user's income and expenditure information. The analysis unit analyzes the information collected by the collection unit to identify the user's consumption patterns and savings needs. The proposal unit proposes products and services based on the needs identified by the analysis unit. The feedback unit provides feedback to businesses regarding the products and services proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately proposed optimal products and services based on the detailed needs of users, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal products and services based on the detailed needs of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a feedback unit. The collection unit collects income and expenditure information of a user. The analysis unit analyzes the information collected by the collection unit to identify the user's consumption patterns and savings needs. The proposal unit proposes products and services based on the needs identified by the analysis unit. The feedback unit provides feedback to a company regarding the products and services proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal products and services based on the detailed needs of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI household accounting system according to an embodiment of the present invention collects a user's income and expenditure information, analyzes it using a generation AI, identifies the user's consumption patterns and savings needs, proposes optimal products and services, and provides feedback to businesses. In this AI household accounting system, a user inputs their daily income and expenditure into a household accounting app, and the generation AI analyzes the information to identify the user's consumption patterns and savings needs. For example, a user with high food expenses can receive sale information and coupons from a nearby supermarket. A user with high electricity bills can receive energy-saving advice and information on energy-efficient home appliances. Next, the generation AI proposes optimal products and services based on the user's attribute information (e.g., age, region, hobbies, and preferences). For example, the latest fashion items and trendy products are suggested to younger users, while health foods and nursing care services are suggested to senior users. Furthermore, the generation AI provides sale information and advice on savings opportunities based on the user's savings goals. For example, a user aiming to save 10,000 yen per month can receive specific savings methods and investment advice. This allows users to save money and increase their savings, or invest the money they save, allowing them to live a richer life. The generative AI also provides feedback on users' needs to companies, encouraging them to improve their products and services. This allows companies to provide products and services that meet their customers' needs, generating significant benefits for all stakeholders. In this way, the AI ​​household accounting system is a service that supports users' savings and financial investments, and by achieving optimal matching with companies, provides benefits to all stakeholders. This allows the AI ​​household accounting system to efficiently collect, analyze, suggest, and provide feedback on users' income and expenditure information.

[0029] The AI ​​household accounting system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a feedback unit. The collection unit collects income and expenditure information of a user. The collection unit, for example, collects information entered by a user into a household accounting app. The collection unit can collect income and expenditure information by the user entering their daily income and expenditure. The analysis unit analyzes the information collected by the collection unit and identifies the user's consumption patterns and savings needs. The analysis unit, for example, uses a generation AI to analyze the collected information. The generation AI can identify the user's consumption patterns and savings needs based on the user's income and expenditure information. The proposal unit proposes products and services based on the needs identified by the analysis unit. The proposal unit, for example, uses the generation AI to propose optimal products and services. The generation AI can propose optimal products and services based on the user's attribute information (such as age, region, hobbies, and preferences). The feedback unit provides companies with feedback on the products and services proposed by the proposal unit. The feedback unit, for example, uses the generation AI to provide users' feedback to companies. The generation AI can provide feedback on user needs to companies and encourage them to improve their products and services. This enables the AI ​​household accounting system according to the embodiment to efficiently collect, analyze, propose, and provide feedback on users' income and expenditure information.

[0030] The AI ​​household accounting system includes an attribute acquisition unit that acquires user attribute information. The attribute acquisition unit acquires the user attribute information. The attribute acquisition unit can acquire information such as the user's age, gender, occupation, and income. The attribute acquisition unit can acquire the attribute information based on information the user inputs into the household accounting app. For example, the attribute acquisition unit acquires information such as age and gender entered by the user into the household accounting app. The attribute acquisition unit can also acquire information such as the user's occupation and income. By acquiring the user's attribute information, the attribute acquisition unit can make more accurate suggestions. Some or all of the above-described processing in the attribute acquisition unit may be performed using, or without, a generation AI. For example, the attribute acquisition unit can input the user's attribute information into the generation AI, which can then analyze and acquire the attribute information.

[0031] The AI ​​household accounting system includes an advice unit that provides sale information or savings points. The advice unit provides the sale information or savings points. The advice unit can provide the sale information or savings points to the user using, for example, a generation AI. The generation AI can provide the sale information or savings points based on the user's consumption patterns and savings needs. For example, if the user wants to save on food costs, the advice unit can provide sale information and coupons for nearby supermarkets. Also, if the user wants to save on electricity bills, the advice unit can provide energy-saving advice and information on energy-efficient home appliances. In this way, the advice unit supports the user's savings by providing sale information and savings points. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's consumption patterns and savings needs into the generation AI, which can then provide sale information and savings points.

[0032] The AI ​​household accounting system includes a reporting unit that provides reports to companies. The reporting unit provides reports to companies. The reporting unit can provide reports to companies based on user feedback, for example, using a generation AI. The generation AI can analyze users' consumption patterns and savings needs and provide the results to companies as reports. For example, the reporting unit reports to companies what products and services users are looking for. The reporting unit can also suggest improvements to products and services to companies based on user feedback. In this way, the reporting unit encourages companies to improve their products and services by providing reports to companies. Some or all of the above-mentioned processing in the reporting unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reporting unit can input user feedback into the generation AI, which can then create a report and provide it to companies.

[0033] The collection unit can collect information that the user inputs into the household accounting app. The collection unit collects information that the user inputs into the household accounting app. For example, the collection unit can collect income and expenditure information by having the user input daily income and expenditure. The collection unit can collect income and expenditure information based on the information that the user inputs into the household accounting app. For example, the collection unit collects information on income and expenditure that the user inputs into the household accounting app. The collection unit can also collect savings information that the user inputs into the household accounting app. In this way, the collection unit can obtain accurate income and expenditure information by collecting the information that the user inputs into the household accounting app. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI, for example. For example, the collection unit can input the information that the user inputs into the household accounting app into a generation AI, and the generation AI can collect the income and expenditure information.

[0034] The analysis unit can analyze the collected information to identify the user's consumption patterns and savings needs. The analysis unit can analyze the collected information to identify the user's consumption patterns and savings needs. The analysis unit, for example, uses a generation AI to analyze the collected information. The generation AI can identify the consumption patterns and savings needs based on the user's income and expenditure information. For example, the analysis unit can analyze the user's income and expenditure information and provide sale information and coupons from nearby supermarkets to users with high food expenses. The analysis unit can also provide energy-saving advice and information on energy-efficient home appliances to users with high electricity bills. In this way, the analysis unit can identify the user's consumption patterns and savings needs by analyzing the collected information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected information into the generation AI, which can identify the consumption patterns and savings needs.

[0035] The suggestion unit can suggest products and services based on the analysis results. The suggestion unit suggests products and services based on the analysis results. The suggestion unit can suggest optimal products and services using, for example, a generation AI. The generation AI can suggest optimal products and services based on user attribute information (age, region, hobbies, etc.). For example, the suggestion unit can suggest the latest fashion items and trendy products to younger users. The suggestion unit can also suggest health foods and nursing care services to senior users. This allows the suggestion unit to suggest optimal products and services based on the analysis results, thereby making suggestions that meet the user's needs. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the analysis results into the generation AI, which can suggest optimal products and services.

[0036] The feedback unit can provide user feedback to a company. The feedback unit can provide user feedback to a company. The feedback unit can provide user feedback to a company, for example, using a generation AI. The generation AI can analyze the user's consumption patterns and savings needs and provide the results to the company as feedback. For example, the feedback unit reports to the company what kind of products or services the user is looking for. The feedback unit can also suggest improvements to products or services to the company based on the user feedback. In this way, the feedback unit can provide user feedback to the company, thereby encouraging improvements to the product or service. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input user feedback to the generation AI, which can analyze the feedback and provide it to the company.

[0037] The collection unit can analyze the user's past income and expenditure history and select the optimal collection method. The collection unit can analyze the user's past income and expenditure history and select the optimal collection method. The collection unit can analyze the user's past income and expenditure history, for example, using a generation AI. The generation AI can select the optimal collection method based on the user's past income and expenditure data. For example, the collection unit can automatically display income and expenditure items that the user has frequently entered in the past as candidates. The collection unit can also analyze the user's past income and expenditure history to determine whether they tend to enter data during specific time periods and prompt the user to enter data during those time periods. Furthermore, the collection unit can provide an automatic input function to reduce the effort of inputting data based on the user's past income and expenditure history. This allows the collection unit to select the optimal collection method by analyzing the user's past income and expenditure history. Some or all of the above-described processing in the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's past income and expenditure data into the generation AI, which can select the optimal collection method.

[0038] When collecting income and expenditure information, the collection unit can filter the information based on the user's current living situation and areas of interest. When collecting income and expenditure information, the collection unit can filter the information based on the user's current living situation and areas of interest. The collection unit can analyze the user's living situation and areas of interest using, for example, a generation AI. The generation AI can filter the income and expenditure information based on the user's living situation and areas of interest. For example, if the user is traveling, the collection unit can prioritize collecting travel-related income and expenditure information. Also, if the user has started a new hobby, the collection unit can collect income and expenditure information related to that hobby. Furthermore, if the user is participating in a specific event, the collection unit can collect income and expenditure information related to the event. This allows the collection unit to collect more relevant information by filtering the income and expenditure information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI, which can then filter the income and expenditure information.

[0039] When collecting income and expenditure information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. When collecting income and expenditure information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. The collection unit can, for example, use a generation AI to analyze the user's geographical location information. The generation AI can filter the income and expenditure information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting sale information for that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting income and expenditure information for the travel destination. Furthermore, when the user is in a specific store, the collection unit can prioritize collecting coupon information for that store. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI, which can then filter the income and expenditure information.

[0040] The collection unit can analyze the user's social media activities and collect related information when collecting the income and expenditure information. The collection unit can analyze the user's social media activities and collect related information when collecting the income and expenditure information. The collection unit can, for example, use a generation AI to analyze the user's social media activities. The generation AI can filter the income and expenditure information based on the user's social media activities. For example, the collection unit can collect purchase information shared by the user on social media. The collection unit can also collect sale information for brands the user follows on social media. Furthermore, the collection unit can collect income and expenditure information for events the user participates in on social media. In this way, the collection unit can collect related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input data on the user's social media activities into the generation AI, which can then filter the income and expenditure information.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the income and expenditure information during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the income and expenditure information during analysis. The analysis unit can evaluate the importance of the income and expenditure information, for example, using a generation AI. The generation AI can evaluate the importance based on the amount, impact, frequency, etc. of the income and expenditure information. For example, the analysis unit performs a detailed analysis of important income and expenditure information. The analysis unit can also perform a simplified analysis of less important income and expenditure information. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the income and expenditure information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the income and expenditure information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input importance data of the income and expenditure information to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of income and expenditure information during analysis. The analysis unit can apply different analysis algorithms depending on the category of income and expenditure information during analysis. The analysis unit can classify the categories of income and expenditure information using, for example, a generation AI. The generation AI can classify the income and expenditure information into categories such as food expenses, utility expenses, and transportation expenses, and apply an appropriate analysis algorithm to each. For example, the analysis unit can apply an analysis algorithm dedicated to food expenses to income and expenditure information related to food expenses. The analysis unit can also apply an analysis algorithm dedicated to utility expenses to income and expenditure information related to utility expenses. Furthermore, the analysis unit can apply an analysis algorithm dedicated to transportation expenses to income and expenditure information related to transportation expenses. In this way, the analysis unit can apply different analysis algorithms depending on the category of income and expenditure information, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input category data of income and expenditure information to the generation AI, which can then apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the timing of submission of income and expenditure information. During analysis, the analysis unit can determine the priority of analysis based on the timing of submission of income and expenditure information. The analysis unit can, for example, use a generation AI to evaluate the timing of submission of income and expenditure information. The generation AI can determine the priority based on the date and time of submission of the income and expenditure information, the frequency of submission, etc. For example, the analysis unit prioritizes analysis of recently submitted income and expenditure information. The analysis unit can also prioritize analysis of income and expenditure information submitted at important times. Furthermore, the analysis unit can adjust the order of analysis based on the timing of submission. This enables efficient analysis by the analysis unit determining the priority of analysis based on the timing of submission of income and expenditure information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the timing of submission of income and expenditure information into the generation AI, and the generation AI can determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the income and expenditure information during analysis. The analysis unit can adjust the order of analysis based on the relevance of the income and expenditure information during analysis. The analysis unit can evaluate the relevance of the income and expenditure information, for example, using a generation AI. The generation AI can evaluate the relevance based on the relevance, correlation, etc. of the income and expenditure information. For example, the analysis unit prioritizes analysis of highly relevant income and expenditure information. The analysis unit can also postpone analysis of less relevant income and expenditure information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the income and expenditure information. This enables efficient analysis by the analysis unit adjusting the order of analysis based on the relevance of the income and expenditure information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input relevance data of the income and expenditure information to the generation AI, and the generation AI can adjust the order of analysis.

[0045] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. The suggestion unit can evaluate the importance of the product, for example, using a generation AI. The generation AI can evaluate the importance based on the value, impact, demand, etc. of the product. For example, the suggestion unit makes detailed suggestions for important products. The suggestion unit can also make simplified suggestions for products with low importance. Furthermore, the suggestion unit can determine the priority of the suggestion according to the importance of the product. As a result, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the product, thereby enabling efficient suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input product importance data to the generation AI, which can then adjust the level of detail of the suggestion.

[0046] The suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. The suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. The suggestion unit can classify product categories, for example, using a generation AI. The generation AI can classify products into categories such as food, home appliances, and fashion, and apply a suggestion algorithm appropriate for each category. For example, the suggestion unit can apply a suggestion algorithm dedicated to food to suggestions related to food. The suggestion unit can also apply a suggestion algorithm dedicated to home appliances to suggestions related to home appliances. Furthermore, the suggestion unit can apply a suggestion algorithm dedicated to fashion to suggestions related to fashion items. In this way, the suggestion unit can apply different suggestion algorithms depending on the product category, enabling more accurate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input product category data into the generation AI, which can then apply an appropriate suggestion algorithm.

[0047] The suggestion unit can determine the priority of suggestions based on the submission time of the products when making suggestions. The suggestion unit can determine the priority of suggestions based on the submission time of the products when making suggestions. The suggestion unit can evaluate the submission time of the products, for example, using a generation AI. The generation AI can determine the priority based on the product release date, campaign period, etc. For example, the suggestion unit can prioritize recently released products. The suggestion unit can also prioritize products released at important times. Furthermore, the suggestion unit can adjust the order of suggestions based on the submission time. This enables efficient suggestions by the suggestion unit prioritizing suggestions based on the submission time of the products. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input product submission time data into the generation AI, which can then determine the priority of suggestions.

[0048] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. The suggestion unit can evaluate the relevance of products using, for example, a generation AI. The generation AI can evaluate the relevance based on the relevance, correlation, etc. of products. For example, the suggestion unit prioritizes suggesting highly relevant products. The suggestion unit can also postpone suggesting less relevant products. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of products. As a result, the suggestion unit can adjust the order of suggestions based on the relevance of products, thereby enabling efficient suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input product relevance data into the generation AI, and the generation AI can adjust the order of suggestions.

[0049] The feedback unit can select the optimal feedback method by analyzing the user's past feedback history when providing feedback. The feedback unit can select the optimal feedback method by analyzing the user's past feedback history when providing feedback. The feedback unit can analyze the user's past feedback history, for example, using a generation AI. The generation AI can select the optimal feedback method based on the user's past feedback content, evaluation results, etc. For example, the feedback unit can preferentially provide feedback methods that the user has previously preferred. The feedback unit can also select the optimal feedback method from the user's past feedback history. Furthermore, the feedback unit can customize the feedback content based on the user's past feedback history. In this way, the feedback unit can select the optimal feedback method by analyzing the user's past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's past feedback data into the generation AI, which can select the optimal feedback method.

[0050] The feedback unit can customize the feedback means based on the user's current living situation at the time of feedback. The feedback unit customizes the feedback means based on the user's current living situation at the time of feedback. The feedback unit can analyze the user's living situation, for example, using a generation AI. The generation AI can customize the feedback means based on the user's family composition, living situation, lifestyle, etc. For example, if the user is traveling, the feedback unit can provide feedback related to the travel destination. Also, if the user has started a new hobby, the feedback unit can provide feedback related to the hobby. Furthermore, if the user is participating in a specific event, the feedback unit can provide feedback related to the event. In this way, the feedback unit can customize the feedback means based on the user's current living situation, thereby enabling more relevant feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's living situation data into the generation AI, which can then customize the feedback means.

[0051] The feedback unit can select the optimal feedback method by taking into account the user's geographical location information when providing feedback. The feedback unit can select the optimal feedback method by taking into account the user's geographical location information when providing feedback. The feedback unit can analyze the user's geographical location information, for example, using a generation AI. The generation AI can select a feedback method based on the user's geographical location information. For example, if the user is in a specific area, the feedback unit can provide feedback related to the area. Also, if the user is traveling, the feedback unit can provide feedback related to the travel destination. Furthermore, if the user is in a specific store, the feedback unit can provide feedback related to the store. In this way, the feedback unit can select the optimal feedback method by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's geographical location information data into the generation AI, which can select the optimal feedback method.

[0052] The feedback unit can analyze the user's social media activity and suggest a means of feedback at the time of feedback. The feedback unit can analyze the user's social media activity and suggest a means of feedback at the time of feedback. The feedback unit can analyze the user's social media activity, for example, using a generation AI. The generation AI can suggest a means of feedback based on the user's social media activity. For example, the feedback unit can provide feedback based on purchase information shared by the user on social media. The feedback unit can also provide feedback related to brands the user follows on social media. Furthermore, the feedback unit can provide feedback related to events the user is participating in on social media. In this way, the feedback unit can suggest the optimal means of feedback by analyzing the user's social media activity. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input the user's social media activity data into the generation AI, which can then suggest a means of feedback.

[0053] When acquiring attribute information, the attribute acquisition unit can select the optimal acquisition method by referring to the user's past attribute information. When acquiring attribute information, the attribute acquisition unit can select the optimal acquisition method by referring to the user's past attribute information. The attribute acquisition unit can analyze the user's past attribute information, for example, using a generation AI. The generation AI can select the optimal acquisition method based on the user's past attribute information. For example, the attribute acquisition unit can acquire additional information based on attribute information previously provided by the user. The attribute acquisition unit can also select the optimal question format from the user's past attribute information. Furthermore, the attribute acquisition unit can determine the priority of information to be acquired based on the user's past attribute information. In this way, the attribute acquisition unit can select the optimal acquisition method by referring to the user's past attribute information. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the attribute acquisition unit can input the user's past attribute information data into the generation AI, which can select the optimal acquisition method.

[0054] When acquiring attribute information, the attribute acquisition unit can select the optimal acquisition method by taking into account the user's geographical location information. When acquiring attribute information, the attribute acquisition unit can select the optimal acquisition method by taking into account the user's geographical location information. The attribute acquisition unit can analyze the user's geographical location information, for example, using a generation AI. The generation AI can select the attribute information acquisition method based on the user's geographical location information. For example, when the user is in a specific area, the attribute acquisition unit can prioritize acquiring attribute information related to that area. Also, when the user is traveling, the attribute acquisition unit can prioritize acquiring attribute information related to the travel destination. Furthermore, when the user is in a specific store, the attribute acquisition unit can prioritize acquiring attribute information related to that store. In this way, the attribute acquisition unit can select the optimal acquisition method by taking into account the user's geographical location information. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the attribute acquisition unit can input the user's geographical location information data into the generation AI, which can select the optimal acquisition method.

[0055] The advice unit can adjust the level of detail of the advice based on the importance of saving when providing advice. The advice unit can adjust the level of detail of the advice based on the importance of saving when providing advice. The advice unit can evaluate the importance of saving, for example, using a generation AI. The generation AI can evaluate the importance based on the effect, impact, demand, etc. of saving. For example, the advice unit can provide detailed advice for important saving methods. The advice unit can also provide simplified advice for less important saving methods. Furthermore, the advice unit can determine the priority of advice according to the importance of saving. As a result, the advice unit can adjust the level of detail of the advice based on the importance of saving, thereby enabling efficient advice. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input importance of saving data to the generation AI, and the generation AI can adjust the level of detail of the advice.

[0056] The advice unit, when providing advice, can determine the priority of the advice based on the time of submission of the savings plan. The advice unit, when providing advice, can determine the priority of the advice based on the time of submission of the savings plan. The advice unit can, for example, use a generation AI to evaluate the time of submission of the savings plan. The generation AI can determine the priority based on the start date of the savings plan, the campaign period, etc. For example, the advice unit can prioritize advice on recently submitted savings plans. The advice unit can also prioritize advice on savings plans submitted at an important time. Furthermore, the advice unit can adjust the order of advice based on the time of submission. This enables the advice unit to prioritize advice based on the time of submission, thereby enabling efficient advice. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input data on the time of submission of the savings plan to the generation AI, which can then determine the priority of the advice.

[0057] When creating a report, the reporting unit can select the optimal reporting method by referring to the user's past report history. When creating a report, the reporting unit can select the optimal reporting method by referring to the user's past report history. The reporting unit can analyze the user's past report history, for example, using a generation AI. The generation AI can select the optimal reporting method based on the user's past report content, evaluation results, etc. For example, the reporting unit can prioritize providing report formats that the user has previously preferred. The reporting unit can also select the optimal reporting method from the user's past report history. Furthermore, the reporting unit can customize the report content based on the user's past report history. In this way, the reporting unit can select the optimal reporting method by referring to the user's past report history. Some or all of the above-described processing in the reporting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reporting unit can input the user's past report data into the generation AI, which can select the optimal reporting method.

[0058] When creating a report, the reporting unit can select the optimal reporting method by taking into account the user's geographical location information. When creating a report, the reporting unit can select the optimal reporting method by taking into account the user's geographical location information. The reporting unit can analyze the user's geographical location information, for example, using a generation AI. The generation AI can select a reporting method based on the user's geographical location information. For example, if the user is in a specific area, the reporting unit can provide a report related to that area. Also, if the user is traveling, the reporting unit can provide a report related to the travel destination. Furthermore, if the user is in a specific store, the reporting unit can provide a report related to that store. In this way, the reporting unit can select the optimal reporting method by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reporting unit can input the user's geographical location information data into the generation AI, which can select the optimal reporting method.

[0059] When creating a report, the reporting unit can analyze the user's social media activity and suggest a reporting method. When creating a report, the reporting unit can analyze the user's social media activity and suggest a reporting method. The reporting unit can analyze the user's social media activity, for example, using a generation AI. The generation AI can suggest a reporting method based on the user's social media activity. For example, the reporting unit can provide a report based on purchase information shared by the user on social media. The reporting unit can also provide a report related to brands the user follows on social media. Furthermore, the reporting unit can provide a report related to events the user is participating in on social media. In this way, the reporting unit can suggest the optimal reporting method by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, the generation AI. For example, the reporting unit can input the user's social media activity data into the generation AI, which can then suggest a reporting method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The suggestion unit can also analyze the user's past purchase history and select the optimal suggestion method. For example, the suggestion unit can suggest related products and services based on products and services the user has purchased in the past. The suggestion unit can also select the optimal suggestion timing based on the user's past purchase history. Furthermore, the suggestion unit can customize the suggestion content based on the user's past purchase history. This allows the suggestion unit to make more appropriate suggestions by analyzing the user's past purchase history.

[0062] The feedback unit can also customize the feedback means based on the user's current life situation. For example, if the user is traveling, the feedback unit can provide feedback related to the travel destination. If the user has started a new hobby, the feedback unit can provide feedback related to the hobby. Furthermore, if the user is participating in a specific event, the feedback unit can provide feedback related to the event. This allows the feedback unit to customize the feedback means based on the user's current life situation, thereby enabling more relevant feedback.

[0063] The collection unit can also collect income and expenditure information taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting sale information for that area. Also, if the user is traveling, the collection unit can prioritize collecting income and expenditure information for the travel destination. Furthermore, if the user is in a specific store, the collection unit can prioritize collecting coupon information for that store. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0064] The analysis unit can also apply different analysis algorithms depending on the category of income and expenditure information. For example, the analysis unit can apply an analysis algorithm dedicated to food expenses to income and expenditure information related to food expenses. The analysis unit can also apply an analysis algorithm dedicated to utility expenses to income and expenditure information related to utility expenses. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to transportation expenses to income and expenditure information related to transportation expenses. In this way, the analysis unit can apply different analysis algorithms depending on the category of income and expenditure information, enabling more accurate analysis.

[0065] The suggestion unit can also determine the priority of suggestions based on the time of submission of the product. For example, the suggestion unit can give priority to suggesting recently released products. The suggestion unit can also give priority to suggesting products released at an important time. Furthermore, the suggestion unit can adjust the order of suggestions based on the time of submission. This allows the suggestion unit to determine the priority of suggestions based on the time of submission of the product, thereby enabling efficient suggestions.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects income and expenditure information of a user. For example, the collection unit collects information that a user inputs into a household accounting app, and can collect income and expenditure information by inputting daily income and expenditure. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies the user's consumption patterns and savings needs. For example, the information collected using a generation AI can be analyzed to identify consumption patterns and savings needs. Step 3: The proposal unit proposes products and services based on the needs identified by the analysis unit. For example, using generation AI, it can propose optimal products and services based on the user's attribute information (age, region, hobbies, preferences, etc.). Step 4: The feedback unit provides the company with feedback on the products and services proposed by the proposal unit. For example, the generation AI can be used to provide user feedback to the company to encourage improvements to the products and services.

[0068] (Example 2) An AI household accounting system according to an embodiment of the present invention collects a user's income and expenditure information, analyzes it using a generation AI, identifies the user's consumption patterns and savings needs, proposes optimal products and services, and provides feedback to businesses. In this AI household accounting system, a user inputs their daily income and expenditure into a household accounting app, and the generation AI analyzes the information to identify the user's consumption patterns and savings needs. For example, a user with high food expenses can receive sale information and coupons from a nearby supermarket. A user with high electricity bills can receive energy-saving advice and information on energy-efficient home appliances. Next, the generation AI proposes optimal products and services based on the user's attribute information (e.g., age, region, hobbies, and preferences). For example, the latest fashion items and trendy products are suggested to younger users, while health foods and nursing care services are suggested to senior users. Furthermore, the generation AI provides sale information and advice on savings opportunities based on the user's savings goals. For example, a user aiming to save 10,000 yen per month can receive specific savings methods and investment advice. This allows users to save money and increase their savings, or invest the money they save, allowing them to live a richer life. The generative AI also provides feedback on users' needs to companies, encouraging them to improve their products and services. This allows companies to provide products and services that meet their customers' needs, generating significant benefits for all stakeholders. In this way, the AI ​​household accounting system is a service that supports users' savings and financial investments, and by achieving optimal matching with companies, provides benefits to all stakeholders. This allows the AI ​​household accounting system to efficiently collect, analyze, suggest, and provide feedback on users' income and expenditure information.

[0069] The AI ​​household accounting system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a feedback unit. The collection unit collects income and expenditure information of a user. The collection unit, for example, collects information entered by a user into a household accounting app. The collection unit can collect income and expenditure information by the user entering their daily income and expenditure. The analysis unit analyzes the information collected by the collection unit and identifies the user's consumption patterns and savings needs. The analysis unit, for example, uses a generation AI to analyze the collected information. The generation AI can identify the user's consumption patterns and savings needs based on the user's income and expenditure information. The proposal unit proposes products and services based on the needs identified by the analysis unit. The proposal unit, for example, uses the generation AI to propose optimal products and services. The generation AI can propose optimal products and services based on the user's attribute information (such as age, region, hobbies, and preferences). The feedback unit provides companies with feedback on the products and services proposed by the proposal unit. The feedback unit, for example, uses the generation AI to provide users' feedback to companies. The generation AI can provide feedback on user needs to companies and encourage them to improve their products and services. This enables the AI ​​household accounting system according to the embodiment to efficiently collect, analyze, propose, and provide feedback on users' income and expenditure information.

[0070] The AI ​​household accounting system includes an attribute acquisition unit that acquires user attribute information. The attribute acquisition unit acquires the user attribute information. The attribute acquisition unit can acquire information such as the user's age, gender, occupation, and income. The attribute acquisition unit can acquire the attribute information based on information the user inputs into the household accounting app. For example, the attribute acquisition unit acquires information such as age and gender entered by the user into the household accounting app. The attribute acquisition unit can also acquire information such as the user's occupation and income. By acquiring the user's attribute information, the attribute acquisition unit can make more accurate suggestions. Some or all of the above-described processing in the attribute acquisition unit may be performed using, or without, a generation AI. For example, the attribute acquisition unit can input the user's attribute information into the generation AI, which can then analyze and acquire the attribute information.

[0071] The AI ​​household accounting system includes an advice unit that provides sale information or savings points. The advice unit provides the sale information or savings points. The advice unit can provide the sale information or savings points to the user using, for example, a generation AI. The generation AI can provide the sale information or savings points based on the user's consumption patterns and savings needs. For example, if the user wants to save on food costs, the advice unit can provide sale information and coupons for nearby supermarkets. Also, if the user wants to save on electricity bills, the advice unit can provide energy-saving advice and information on energy-efficient home appliances. In this way, the advice unit supports the user's savings by providing sale information and savings points. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's consumption patterns and savings needs into the generation AI, which can then provide sale information and savings points.

[0072] The AI ​​household accounting system includes a reporting unit that provides reports to companies. The reporting unit provides reports to companies. The reporting unit can provide reports to companies based on user feedback, for example, using a generation AI. The generation AI can analyze users' consumption patterns and savings needs and provide the results to companies as reports. For example, the reporting unit reports to companies what products and services users are looking for. The reporting unit can also suggest improvements to products and services to companies based on user feedback. In this way, the reporting unit encourages companies to improve their products and services by providing reports to companies. Some or all of the above-mentioned processing in the reporting unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reporting unit can input user feedback into the generation AI, which can then create a report and provide it to companies.

[0073] The collection unit can collect information that the user inputs into the household accounting app. The collection unit collects information that the user inputs into the household accounting app. For example, the collection unit can collect income and expenditure information by having the user input daily income and expenditure. The collection unit can collect income and expenditure information based on the information that the user inputs into the household accounting app. For example, the collection unit collects information on income and expenditure that the user inputs into the household accounting app. The collection unit can also collect savings information that the user inputs into the household accounting app. In this way, the collection unit can obtain accurate income and expenditure information by collecting the information that the user inputs into the household accounting app. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI, for example. For example, the collection unit can input the information that the user inputs into the household accounting app into a generation AI, and the generation AI can collect the income and expenditure information.

[0074] The analysis unit can analyze the collected information to identify the user's consumption patterns and savings needs. The analysis unit can analyze the collected information to identify the user's consumption patterns and savings needs. The analysis unit, for example, uses a generation AI to analyze the collected information. The generation AI can identify the consumption patterns and savings needs based on the user's income and expenditure information. For example, the analysis unit can analyze the user's income and expenditure information and provide sale information and coupons from nearby supermarkets to users with high food expenses. The analysis unit can also provide energy-saving advice and information on energy-efficient home appliances to users with high electricity bills. In this way, the analysis unit can identify the user's consumption patterns and savings needs by analyzing the collected information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected information into the generation AI, which can identify the consumption patterns and savings needs.

[0075] The suggestion unit can suggest products and services based on the analysis results. The suggestion unit suggests products and services based on the analysis results. The suggestion unit can suggest optimal products and services using, for example, a generation AI. The generation AI can suggest optimal products and services based on user attribute information (age, region, hobbies, etc.). For example, the suggestion unit can suggest the latest fashion items and trendy products to younger users. The suggestion unit can also suggest health foods and nursing care services to senior users. This allows the suggestion unit to suggest optimal products and services based on the analysis results, thereby making suggestions that meet the user's needs. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the analysis results into the generation AI, which can suggest optimal products and services.

[0076] The feedback unit can provide user feedback to a company. The feedback unit can provide user feedback to a company. The feedback unit can provide user feedback to a company, for example, using a generation AI. The generation AI can analyze the user's consumption patterns and savings needs and provide the results to the company as feedback. For example, the feedback unit reports to the company what kind of products or services the user is looking for. The feedback unit can also suggest improvements to products or services to the company based on the user feedback. In this way, the feedback unit can provide user feedback to the company, thereby encouraging improvements to the product or service. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input user feedback to the generation AI, which can analyze the feedback and provide it to the company.

[0077] The collection unit can estimate the user's emotions and adjust the timing of collecting income and expenditure information based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the timing of collecting income and expenditure information based on the estimated user emotions. The collection unit can estimate the user's emotions using, for example, a generation AI. The generation AI can estimate emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, if the user is feeling stressed, the collection unit can simplify the input of income and expenditure information so that it can be completed in a short time. Furthermore, if the user is relaxed, the collection unit can prompt the user to input detailed income and expenditure information to collect more accurate data. Furthermore, if the user is busy, the collection unit can set a reminder to postpone input of income and expenditure information so that it can be input all at once later. This allows the collection unit to adjust the timing of collecting income and expenditure information according to the user's emotions, thereby collecting more appropriate income and expenditure information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotional data into the generation AI, and the generation AI can adjust the timing of collecting income and expenditure information.

[0078] The collection unit can analyze the user's past income and expenditure history and select the optimal collection method. The collection unit can analyze the user's past income and expenditure history and select the optimal collection method. The collection unit can analyze the user's past income and expenditure history, for example, using a generation AI. The generation AI can select the optimal collection method based on the user's past income and expenditure data. For example, the collection unit can automatically display income and expenditure items that the user has frequently entered in the past as candidates. The collection unit can also analyze the user's past income and expenditure history to determine whether they tend to enter data during specific time periods and prompt the user to enter data during those time periods. Furthermore, the collection unit can provide an automatic input function to reduce the effort of inputting data based on the user's past income and expenditure history. This allows the collection unit to select the optimal collection method by analyzing the user's past income and expenditure history. Some or all of the above-described processing in the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's past income and expenditure data into the generation AI, which can select the optimal collection method.

[0079] When collecting income and expenditure information, the collection unit can filter the information based on the user's current living situation and areas of interest. When collecting income and expenditure information, the collection unit can filter the information based on the user's current living situation and areas of interest. The collection unit can analyze the user's living situation and areas of interest using, for example, a generation AI. The generation AI can filter the income and expenditure information based on the user's living situation and areas of interest. For example, if the user is traveling, the collection unit can prioritize collecting travel-related income and expenditure information. Also, if the user has started a new hobby, the collection unit can collect income and expenditure information related to that hobby. Furthermore, if the user is participating in a specific event, the collection unit can collect income and expenditure information related to the event. This allows the collection unit to collect more relevant information by filtering the income and expenditure information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI, which can then filter the income and expenditure information.

[0080] The collection unit can estimate the user's emotions and determine the priority of the income and expenditure information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of the income and expenditure information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions, for example, using a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, if the user is feeling stressed, the collection unit can prioritize collecting only important income and expenditure information. Also, if the user is relaxed, the collection unit can prioritize collecting detailed income and expenditure information. Furthermore, if the user is busy, the collection unit can temporarily store the income and expenditure information for later review. In this way, the collection unit can prioritize collecting important information by prioritizing the income and expenditure information according to the user's emotions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which can then prioritize the income and expenditure information.

[0081] When collecting income and expenditure information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. When collecting income and expenditure information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. The collection unit can, for example, use a generation AI to analyze the user's geographical location information. The generation AI can filter the income and expenditure information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting sale information for that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting income and expenditure information for the travel destination. Furthermore, when the user is in a specific store, the collection unit can prioritize collecting coupon information for that store. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI, which can then filter the income and expenditure information.

[0082] The collection unit can analyze the user's social media activities and collect related information when collecting the income and expenditure information. The collection unit can analyze the user's social media activities and collect related information when collecting the income and expenditure information. The collection unit can, for example, use a generation AI to analyze the user's social media activities. The generation AI can filter the income and expenditure information based on the user's social media activities. For example, the collection unit can collect purchase information shared by the user on social media. The collection unit can also collect sale information for brands the user follows on social media. Furthermore, the collection unit can collect income and expenditure information for events the user participates in on social media. In this way, the collection unit can collect related information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input data on the user's social media activities into the generation AI, which can then filter the income and expenditure information.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions, for example, using a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, if the user is stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. In this way, the analysis unit can adjust the presentation method of the analysis according to the user's emotions, thereby providing a more understandable analysis result. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's emotion data into a generation AI, which can adjust the presentation method of the analysis.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the income and expenditure information during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the income and expenditure information during analysis. The analysis unit can evaluate the importance of the income and expenditure information, for example, using a generation AI. The generation AI can evaluate the importance based on the amount, impact, frequency, etc. of the income and expenditure information. For example, the analysis unit performs a detailed analysis of important income and expenditure information. The analysis unit can also perform a simplified analysis of less important income and expenditure information. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the income and expenditure information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the income and expenditure information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input importance data of the income and expenditure information to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of income and expenditure information during analysis. The analysis unit can apply different analysis algorithms depending on the category of income and expenditure information during analysis. The analysis unit can classify the categories of income and expenditure information using, for example, a generation AI. The generation AI can classify the income and expenditure information into categories such as food expenses, utility expenses, and transportation expenses, and apply an appropriate analysis algorithm to each. For example, the analysis unit can apply an analysis algorithm dedicated to food expenses to income and expenditure information related to food expenses. The analysis unit can also apply an analysis algorithm dedicated to utility expenses to income and expenditure information related to utility expenses. Furthermore, the analysis unit can apply an analysis algorithm dedicated to transportation expenses to income and expenditure information related to transportation expenses. In this way, the analysis unit can apply different analysis algorithms depending on the category of income and expenditure information, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input category data of income and expenditure information to the generation AI, which can then apply an appropriate analysis algorithm.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions, for example, using a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. For example, if the user is in a hurry, the analysis unit can provide a short and 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 an analysis result with visually stimulating effects. This allows the analysis unit to adjust the length of the analysis according to the user's emotions and provide a more appropriate analysis result. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the timing of submission of income and expenditure information. During analysis, the analysis unit can determine the priority of analysis based on the timing of submission of income and expenditure information. The analysis unit can, for example, use a generation AI to evaluate the timing of submission of income and expenditure information. The generation AI can determine the priority based on the date and time of submission of the income and expenditure information, the frequency of submission, etc. For example, the analysis unit prioritizes analysis of recently submitted income and expenditure information. The analysis unit can also prioritize analysis of income and expenditure information submitted at important times. Furthermore, the analysis unit can adjust the order of analysis based on the timing of submission. This enables efficient analysis by the analysis unit determining the priority of analysis based on the timing of submission of income and expenditure information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the timing of submission of income and expenditure information into the generation AI, and the generation AI can determine the priority of analysis.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the income and expenditure information during analysis. The analysis unit can adjust the order of analysis based on the relevance of the income and expenditure information during analysis. The analysis unit can evaluate the relevance of the income and expenditure information, for example, using a generation AI. The generation AI can evaluate the relevance based on the relevance, correlation, etc. of the income and expenditure information. For example, the analysis unit prioritizes analysis of highly relevant income and expenditure information. The analysis unit can also postpone analysis of less relevant income and expenditure information. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the income and expenditure information. This enables efficient analysis by the analysis unit adjusting the order of analysis based on the relevance of the income and expenditure information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input relevance data of the income and expenditure information to the generation AI, and the generation AI can adjust the order of analysis.

[0089] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. The suggestion unit can estimate the user's emotions, for example, using a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, if the user is stressed, the suggestion unit can make simple and visually easy-to-understand suggestions. Also, if the user is relaxed, the suggestion unit can make detailed suggestions. Furthermore, if the user is excited, the suggestion unit can make suggestions with visually stimulating effects. In this way, the suggestion unit can adjust the way the suggestions are expressed based on the user's emotions, thereby enabling more understandable suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input user emotion data into the generation AI, which can then adjust the way the suggestions are expressed.

[0090] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. The suggestion unit can evaluate the importance of the product, for example, using a generation AI. The generation AI can evaluate the importance based on the value, impact, demand, etc. of the product. For example, the suggestion unit makes detailed suggestions for important products. The suggestion unit can also make simplified suggestions for products with low importance. Furthermore, the suggestion unit can determine the priority of the suggestion according to the importance of the product. As a result, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the product, thereby enabling efficient suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input product importance data to the generation AI, which can then adjust the level of detail of the suggestion.

[0091] The suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. The suggestion unit can apply different suggestion algorithms depending on the product category when making a suggestion. The suggestion unit can classify product categories, for example, using a generation AI. The generation AI can classify products into categories such as food, home appliances, and fashion, and apply a suggestion algorithm appropriate for each category. For example, the suggestion unit can apply a suggestion algorithm dedicated to food to suggestions related to food. The suggestion unit can also apply a suggestion algorithm dedicated to home appliances to suggestions related to home appliances. Furthermore, the suggestion unit can apply a suggestion algorithm dedicated to fashion to suggestions related to fashion items. In this way, the suggestion unit can apply different suggestion algorithms depending on the product category, enabling more accurate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input product category data into the generation AI, which can then apply an appropriate suggestion algorithm.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. The suggestion unit can estimate the user's emotions using, for example, a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, if the user is in a hurry, the suggestion unit can make short and to-the-point suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. Furthermore, if the user is excited, the suggestion unit can make suggestions with visually stimulating effects. This allows the suggestion unit to adjust the length of the suggestions according to the user's emotions, thereby enabling more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input user emotion data into the generation AI, which can then adjust the length of the suggestions.

[0093] The suggestion unit can determine the priority of suggestions based on the submission time of the products when making suggestions. The suggestion unit can determine the priority of suggestions based on the submission time of the products when making suggestions. The suggestion unit can evaluate the submission time of the products, for example, using a generation AI. The generation AI can determine the priority based on the product release date, campaign period, etc. For example, the suggestion unit can prioritize recently released products. The suggestion unit can also prioritize products released at important times. Furthermore, the suggestion unit can adjust the order of suggestions based on the submission time. This enables efficient suggestions by the suggestion unit prioritizing suggestions based on the submission time of the products. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input product submission time data into the generation AI, which can then determine the priority of suggestions.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of products when making suggestions. The suggestion unit can evaluate the relevance of products using, for example, a generation AI. The generation AI can evaluate the relevance based on the relevance, correlation, etc. of products. For example, the suggestion unit prioritizes suggesting highly relevant products. The suggestion unit can also postpone suggesting less relevant products. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of products. As a result, the suggestion unit can adjust the order of suggestions based on the relevance of products, thereby enabling efficient suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input product relevance data into the generation AI, and the generation AI can adjust the order of suggestions.

[0095] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. The feedback unit can estimate the user's emotions using, for example, a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, if the user is feeling stressed, the feedback unit can provide simple, visually easy-to-understand feedback. Also, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is excited, the feedback unit can provide feedback with visually stimulating effects. In this way, the feedback unit can adjust the feedback method according to the user's emotions, thereby enabling more understandable feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can input the user's emotion data into the generation AI, which can adjust the feedback method.

[0096] The feedback unit can select the optimal feedback method by analyzing the user's past feedback history when providing feedback. The feedback unit can select the optimal feedback method by analyzing the user's past feedback history when providing feedback. The feedback unit can analyze the user's past feedback history, for example, using a generation AI. The generation AI can select the optimal feedback method based on the user's past feedback content, evaluation results, etc. For example, the feedback unit can preferentially provide feedback methods that the user has previously preferred. The feedback unit can also select the optimal feedback method from the user's past feedback history. Furthermore, the feedback unit can customize the feedback content based on the user's past feedback history. In this way, the feedback unit can select the optimal feedback method by analyzing the user's past feedback history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's past feedback data into the generation AI, which can select the optimal feedback method.

[0097] The feedback unit can customize the feedback means based on the user's current living situation at the time of feedback. The feedback unit customizes the feedback means based on the user's current living situation at the time of feedback. The feedback unit can analyze the user's living situation, for example, using a generation AI. The generation AI can customize the feedback means based on the user's family composition, living situation, lifestyle, etc. For example, if the user is traveling, the feedback unit can provide feedback related to the travel destination. Also, if the user has started a new hobby, the feedback unit can provide feedback related to the hobby. Furthermore, if the user is participating in a specific event, the feedback unit can provide feedback related to the event. In this way, the feedback unit can customize the feedback means based on the user's current living situation, thereby enabling more relevant feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's living situation data into the generation AI, which can then customize the feedback means.

[0098] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The feedback unit can estimate the user's emotions using, for example, a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, the feedback unit can prioritize providing important feedback when the user is stressed. The feedback unit can also prioritize providing detailed feedback when the user is relaxed. Furthermore, the feedback unit can temporarily store feedback so that it can be checked later when the user is busy. In this way, the feedback unit can prioritize feedback based on the user's emotions and provide important feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input user's emotion data into the generation AI, which can then determine the priority of feedback.

[0099] The feedback unit can select the optimal feedback method by taking into account the user's geographical location information when providing feedback. The feedback unit can select the optimal feedback method by taking into account the user's geographical location information when providing feedback. The feedback unit can analyze the user's geographical location information, for example, using a generation AI. The generation AI can select a feedback method based on the user's geographical location information. For example, if the user is in a specific area, the feedback unit can provide feedback related to the area. Also, if the user is traveling, the feedback unit can provide feedback related to the travel destination. Furthermore, if the user is in a specific store, the feedback unit can provide feedback related to the store. In this way, the feedback unit can select the optimal feedback method by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's geographical location information data into the generation AI, which can select the optimal feedback method.

[0100] The feedback unit can analyze the user's social media activity and suggest a means of feedback at the time of feedback. The feedback unit can analyze the user's social media activity and suggest a means of feedback at the time of feedback. The feedback unit can analyze the user's social media activity, for example, using a generation AI. The generation AI can suggest a means of feedback based on the user's social media activity. For example, the feedback unit can provide feedback based on purchase information shared by the user on social media. The feedback unit can also provide feedback related to brands the user follows on social media. Furthermore, the feedback unit can provide feedback related to events the user is participating in on social media. In this way, the feedback unit can suggest the optimal means of feedback by analyzing the user's social media activity. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input the user's social media activity data into the generation AI, which can then suggest a means of feedback.

[0101] The attribute acquisition unit can estimate the user's emotions and adjust the attribute information acquisition method based on the estimated user's emotions. The attribute acquisition unit can estimate the user's emotions using, for example, a generation AI. The generation AI can estimate the user's emotions using facial expression analysis, voice analysis, text analysis, etc. For example, if the user is feeling stressed, the attribute acquisition unit can acquire attribute information in the form of a simple question. Also, if the user is relaxed, the attribute acquisition unit can acquire attribute information in the form of a detailed question. Furthermore, if the user is busy, the attribute acquisition unit can temporarily save the question so that it can be answered later. This allows the attribute acquisition unit to acquire more appropriate attribute information by adjusting the attribute information acquisition method according to the user's emotions. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the attribute acquisition unit can input the user's emotion data into the generation AI, which can then adjust the attribute information acquisition method.

[0102] When acquiring attribute information, the attribute acquisition unit can select the optimal acquisition method by referring to the user's past attribute information. When acquiring attribute information, the attribute acquisition unit can select the optimal acquisition method by referring to the user's past attribute information. The attribute acquisition unit can analyze the user's past attribute information, for example, using a generation AI. The generation AI can select the optimal acquisition method based on the user's past attribute information. For example, the attribute acquisition unit can acquire additional information based on attribute information previously provided by the user. The attribute acquisition unit can also select the optimal question format from the user's past attribute information. Furthermore, the attribute acquisition unit can determine the priority of information to be acquired based on the user's past attribute information. In this way, the attribute acquisition unit can select the optimal acquisition method by referring to the user's past attribute information. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the attribute acquisition unit can input the user's past attribute information data into the generation AI, which can select the optimal acquisition method.

[0103] The attribute acquisition unit can estimate the user's emotions and determine the priority of attribute information based on the estimated user's emotions. The attribute acquisition unit can estimate the user's emotions and determine the priority of attribute information based on the estimated user's emotions. The attribute acquisition unit can estimate the user's emotions using, for example, a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, when the user is stressed, the attribute acquisition unit prioritizes acquiring only important attribute information. The attribute acquisition unit can also prioritize acquiring detailed attribute information when the user is relaxed. Furthermore, when the user is busy, the attribute acquisition unit can temporarily store attribute information for later review. In this way, the attribute acquisition unit can prioritize acquiring important attribute information by determining the priority of attribute information according to the user's emotions. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the attribute acquisition unit can input the user's emotion data into the generation AI, which can then determine the priority of the attribute information.

[0104] When acquiring attribute information, the attribute acquisition unit can select the optimal acquisition method by taking into account the user's geographical location information. When acquiring attribute information, the attribute acquisition unit can select the optimal acquisition method by taking into account the user's geographical location information. The attribute acquisition unit can analyze the user's geographical location information, for example, using a generation AI. The generation AI can select the attribute information acquisition method based on the user's geographical location information. For example, when the user is in a specific area, the attribute acquisition unit can prioritize acquiring attribute information related to that area. Also, when the user is traveling, the attribute acquisition unit can prioritize acquiring attribute information related to the travel destination. Furthermore, when the user is in a specific store, the attribute acquisition unit can prioritize acquiring attribute information related to that store. In this way, the attribute acquisition unit can select the optimal acquisition method by taking into account the user's geographical location information. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the attribute acquisition unit can input the user's geographical location information data into the generation AI, which can select the optimal acquisition method.

[0105] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. The advice unit can estimate the user's emotions, for example, using a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, if the user is feeling stressed, the advice unit can provide simple and visually easy-to-understand advice. Furthermore, if the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is excited, the advice unit can provide advice with visually stimulating effects. This allows the advice unit to adjust the way the advice is expressed based on the user's emotions, thereby making the advice easier to understand. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's emotion data into the generation AI, which can adjust the way the advice is expressed.

[0106] The advice unit can adjust the level of detail of the advice based on the importance of saving when providing advice. The advice unit can adjust the level of detail of the advice based on the importance of saving when providing advice. The advice unit can evaluate the importance of saving, for example, using a generation AI. The generation AI can evaluate the importance based on the effect, impact, demand, etc. of saving. For example, the advice unit can provide detailed advice for important saving methods. The advice unit can also provide simplified advice for less important saving methods. Furthermore, the advice unit can determine the priority of advice according to the importance of saving. As a result, the advice unit can adjust the level of detail of the advice based on the importance of saving, thereby enabling efficient advice. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input importance of saving data to the generation AI, and the generation AI can adjust the level of detail of the advice.

[0107] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. The advice unit can estimate the user's emotions, for example, using a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, the advice unit can provide short and to-the-point advice when the user is in a hurry. The advice unit can also provide detailed advice when the user is relaxed. Furthermore, the advice unit can provide advice with visually stimulating effects when the user is excited. This allows the advice unit to adjust the length of the advice according to the user's emotions, thereby enabling more appropriate advice. Some or all of the above-described processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's emotion data into the generation AI, which can then adjust the length of the advice.

[0108] The advice unit, when providing advice, can determine the priority of the advice based on the time of submission of the savings plan. The advice unit, when providing advice, can determine the priority of the advice based on the time of submission of the savings plan. The advice unit can, for example, use a generation AI to evaluate the time of submission of the savings plan. The generation AI can determine the priority based on the start date of the savings plan, the campaign period, etc. For example, the advice unit can prioritize advice on recently submitted savings plans. The advice unit can also prioritize advice on savings plans submitted at an important time. Furthermore, the advice unit can adjust the order of advice based on the time of submission. This enables the advice unit to prioritize advice based on the time of submission, thereby enabling efficient advice. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input data on the time of submission of the savings plan to the generation AI, which can then determine the priority of the advice.

[0109] The report unit can estimate the user's emotions and adjust the presentation method of the report based on the estimated user's emotions. The report unit can estimate the user's emotions and adjust the presentation method of the report based on the estimated user's emotions. The report unit can estimate the user's emotions, for example, using a generation AI. The generation AI can estimate the emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, if the user is feeling stressed, the report unit can provide a simple, visually easy-to-understand report. Also, if the user is relaxed, the report unit can provide a detailed report. Furthermore, if the user is excited, the report unit can provide a report with visually stimulating effects. In this way, the report unit can adjust the presentation method of the report according to the user's emotions, making the report easier to understand. Some or all of the above-mentioned processing in the report unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the report unit can input the user's emotion data into the generation AI, which can adjust the presentation method of the report.

[0110] When creating a report, the reporting unit can select the optimal reporting method by referring to the user's past report history. When creating a report, the reporting unit can select the optimal reporting method by referring to the user's past report history. The reporting unit can analyze the user's past report history, for example, using a generation AI. The generation AI can select the optimal reporting method based on the user's past report content, evaluation results, etc. For example, the reporting unit can prioritize providing report formats that the user has previously preferred. The reporting unit can also select the optimal reporting method from the user's past report history. Furthermore, the reporting unit can customize the report content based on the user's past report history. In this way, the reporting unit can select the optimal reporting method by referring to the user's past report history. Some or all of the above-described processing in the reporting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reporting unit can input the user's past report data into the generation AI, which can select the optimal reporting method.

[0111] The reporting unit can estimate the user's emotions and prioritize reports based on the estimated user emotions. The reporting unit can estimate the user's emotions and prioritize reports based on the estimated user emotions. The reporting unit can estimate the user's emotions using, for example, a generation AI. The generation AI can estimate emotions using facial expression analysis, voice analysis, text analysis, etc. of the user. For example, the reporting unit can prioritize providing important reports when the user is stressed. The reporting unit can also prioritize providing detailed reports when the user is relaxed. Furthermore, if the user is busy, the reporting unit can temporarily save reports for later review. In this way, the reporting unit can prioritize important reports by prioritizing reports according to the user's emotions. Some or all of the above-described processing in the reporting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reporting unit can input user emotion data into the generation AI, which can then prioritize reports.

[0112] When creating a report, the reporting unit can select the optimal reporting method by taking into account the user's geographical location information. When creating a report, the reporting unit can select the optimal reporting method by taking into account the user's geographical location information. The reporting unit can analyze the user's geographical location information, for example, using a generation AI. The generation AI can select a reporting method based on the user's geographical location information. For example, if the user is in a specific area, the reporting unit can provide a report related to that area. Also, if the user is traveling, the reporting unit can provide a report related to the travel destination. Furthermore, if the user is in a specific store, the reporting unit can provide a report related to that store. In this way, the reporting unit can select the optimal reporting method by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reporting unit can input the user's geographical location information data into the generation AI, which can select the optimal reporting method.

[0113] When creating a report, the reporting unit can analyze the user's social media activity and suggest a reporting method. When creating a report, the reporting unit can analyze the user's social media activity and suggest a reporting method. The reporting unit can analyze the user's social media activity, for example, using a generation AI. The generation AI can suggest a reporting method based on the user's social media activity. For example, the reporting unit can provide a report based on purchase information shared by the user on social media. The reporting unit can also provide a report related to brands the user follows on social media. Furthermore, the reporting unit can provide a report related to events the user is participating in on social media. In this way, the reporting unit can suggest the optimal reporting method by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, the generation AI. For example, the reporting unit can input the user's social media activity data into the generation AI, which can then suggest a reporting method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, feedback unit, attribute acquisition unit, advice unit, report unit, and emotion estimation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects information entered by a user into a household accounting app. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to identify the user's consumption patterns and savings needs. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal products and services based on the user's attribute information. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides user feedback to companies. The attribute acquisition unit is realized by the control unit 46A of the smart device 14 and acquires the user's attribute information. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides sale information and savings points. The report unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a report to the company. The emotion estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of collecting income and expenditure information. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, feedback unit, attribute acquisition unit, advice unit, report unit, and emotion estimation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects information entered by a user into a household accounting app. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to identify the user's consumption patterns and savings needs. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal products and services based on the user's attribute information. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides user feedback to companies. The attribute acquisition unit is realized by the control unit 46A of the smart glasses 214 and acquires the user's attribute information. The advice unit is realized by the specific processing unit 290 of the data processing device 12 and provides sale information and savings points. The report unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a report to the company. The emotion estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of collecting income and expenditure information. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, feedback unit, attribute acquisition unit, advice unit, report unit, and emotion estimation unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset-type terminal 314 and collects information entered by a user into the household accounting app. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to identify the user's consumption patterns and savings needs. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal products and services based on the user's attribute information. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides user feedback to companies. The attribute acquisition unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and acquires the user's attribute information. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides sale information and savings points. The report unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a report to the company. The emotion estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of collecting income and expenditure information. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, feedback unit, attribute acquisition unit, advice unit, report unit, and emotion estimation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects information entered by a user into the household accounting app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to identify the user's consumption patterns and savings needs. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal products and services based on the user's attribute information. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides user feedback to companies. The attribute acquisition unit is realized, for example, by the control unit 46A of the robot 414 and acquires the user's attribute information. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides sale information and savings points. The report unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a report to the company. The emotion estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of collecting income and expenditure information.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The analysis unit can also estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, visually easy-to-understand results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. In this way, the analysis unit can provide information that is easier to understand by adjusting the way in which the analysis results are presented according to the user's emotions.

[0116] The suggestion unit can also estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can suggest products and services that will help them relax. If the user is relaxed, the suggestion unit can also suggest products and services related to active activities. Furthermore, if the user is excited, the suggestion unit can also suggest products and services that are highly entertaining. This allows the suggestion unit to make more appropriate suggestions by adjusting the content of the suggestions according to the user's emotions.

[0117] The feedback unit can also estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can provide simple, visually easy-to-understand feedback. If the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is excited, the feedback unit can provide feedback with visually stimulating effects. In this way, the feedback unit can adjust the feedback method according to the user's emotions, thereby enabling more easily understandable feedback.

[0118] The collection unit can also estimate the user's emotions and adjust the method for collecting income and expenditure information based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can simplify the input of income and expenditure information so that it can be completed in a short time. Also, if the user is relaxed, the collection unit can prompt the user to input detailed income and expenditure information so that more accurate data can be collected. Furthermore, if the user is busy, the collection unit can set a reminder to postpone input of income and expenditure information so that it can be input all at once later. In this way, the collection unit can collect more appropriate income and expenditure information by adjusting the method for collecting income and expenditure information according to the user's emotions.

[0119] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling stressed, only important income and expenditure information can be analyzed with priority. Also, if the user is relaxed, detailed income and expenditure information can be analyzed with priority. Furthermore, if the user is busy, the income and expenditure information can be temporarily saved so that it can be checked later. In this way, the analysis unit can prioritize analysis of important information by determining the priority of analysis according to the user's emotions.

[0120] The suggestion unit can also analyze the user's past purchase history and select the optimal suggestion method. For example, the suggestion unit can suggest related products and services based on products and services the user has purchased in the past. The suggestion unit can also select the optimal suggestion timing based on the user's past purchase history. Furthermore, the suggestion unit can customize the suggestion content based on the user's past purchase history. This allows the suggestion unit to make more appropriate suggestions by analyzing the user's past purchase history.

[0121] The feedback unit can also customize the feedback means based on the user's current life situation. For example, if the user is traveling, the feedback unit can provide feedback related to the travel destination. If the user has started a new hobby, the feedback unit can provide feedback related to the hobby. Furthermore, if the user is participating in a specific event, the feedback unit can provide feedback related to the event. This allows the feedback unit to customize the feedback means based on the user's current life situation, thereby enabling more relevant feedback.

[0122] The collection unit can also collect income and expenditure information taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting sale information for that area. Also, if the user is traveling, the collection unit can prioritize collecting income and expenditure information for the travel destination. Furthermore, if the user is in a specific store, the collection unit can prioritize collecting coupon information for that store. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information.

[0123] The analysis unit can also apply different analysis algorithms depending on the category of income and expenditure information. For example, the analysis unit can apply an analysis algorithm dedicated to food expenses to income and expenditure information related to food expenses. The analysis unit can also apply an analysis algorithm dedicated to utility expenses to income and expenditure information related to utility expenses. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to transportation expenses to income and expenditure information related to transportation expenses. In this way, the analysis unit can apply different analysis algorithms depending on the category of income and expenditure information, enabling more accurate analysis.

[0124] The suggestion unit can also determine the priority of suggestions based on the time of submission of the product. For example, the suggestion unit can give priority to suggesting recently released products. The suggestion unit can also give priority to suggesting products released at an important time. Furthermore, the suggestion unit can adjust the order of suggestions based on the time of submission. This allows the suggestion unit to determine the priority of suggestions based on the time of submission of the product, thereby enabling efficient suggestions.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The collection unit collects income and expenditure information of a user. For example, the collection unit collects information that a user inputs into a household accounting app, and can collect income and expenditure information by inputting daily income and expenditure. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies the user's consumption patterns and savings needs. For example, the information collected using a generation AI can be analyzed to identify consumption patterns and savings needs. Step 3: The proposal unit proposes products and services based on the needs identified by the analysis unit. For example, using generation AI, it can propose optimal products and services based on the user's attribute information (age, region, hobbies, preferences, etc.). Step 4: The feedback unit provides the company with feedback on the products and services proposed by the proposal unit. For example, the generation AI can be used to provide user feedback to the company to encourage improvements to the products and services.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0129] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0171] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0172] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0175] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0182] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0183] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0184] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0189] 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.

[0190] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0191] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0192] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0195] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0196] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0197] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0198] [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects income and expenditure information of users; an analysis unit that analyzes the information collected by the collection unit and identifies a user's consumption pattern and saving needs; a proposal unit that proposes products and services based on the needs identified by the analysis unit; a feedback unit that provides feedback to companies regarding the products and services proposed by the proposal unit; Equipped with A system characterized by:

2. Equipped with an attribute acquisition unit that acquires user attribute information 2. The system of claim 1.

3. Equipped with an advice section that provides special sale information or points to save money 2. The system of claim 1.

4. Establish a reporting department that provides reports to companies 2. The system of claim 1.

5. The collecting unit Collect information that users enter into a household accounting app 2. The system of claim 1.

6. The analysis unit Analyzing collected information to identify users' spending patterns and savings needs 2. The system of claim 1.

7. The proposal unit Propose products and services based on the analysis results 2. The system of claim 1.

8. The feedback unit Providing user feedback to companies 2. The system of claim 1.

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