system

Abstract

The system according to the embodiment aims to automatically reflect a user's payment history and purchase history in a household account book and provide evaluations and feedback. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, a reflection unit, and an evaluation unit. The acquisition unit automatically acquires a user's payment history or purchase history. The analysis unit analyzes the history data acquired by the acquisition unit and classifies it by expenditure item. The reflection unit automatically reflects the data analyzed by the analysis unit in a household account book. The evaluation unit evaluates the household account book reflected by the reflection unit and provides feedback to the user.

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 do not adequately automatically reflect a user's payment history and purchase history in a household account book, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically reflect a user's payment history and purchase history in a household account book and provide evaluations and feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a reflection unit, and an evaluation unit. The acquisition unit automatically acquires a user's payment history or purchase history. The analysis unit analyzes the history data acquired by the acquisition unit and classifies it by expenditure item. The reflection unit automatically reflects the data analyzed by the analysis unit in a household account book. The evaluation unit evaluates the household account book reflected by the reflection unit and provides feedback to the user. [Effects of the Invention]

[0007] The system according to the embodiment can automatically reflect the user's payment history and purchase history in the household account book and provide evaluations and feedback. [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 automatically acquires a user's payment history and purchase history, analyzes the history using AI, and automatically reflects the history in a household account book and provides an evaluation. The AI ​​household accounting system automatically acquires a user's payment history and purchase history, analyzes the history, classifies the history by expenditure item, and automatically reflects the history in a household account book. The AI ​​household accounting system also evaluates the household account book and provides feedback to the user. For example, when a user makes a payment, the AI ​​household accounting system automatically reflects the history in an app. Next, the AI ​​analyzes the acquired history data and classifies it by expenditure item. For example, the AI ​​automatically classifies the history into categories such as food expenses, transportation expenses, and entertainment expenses. Next, the AI ​​household accounting system automatically reflects the analyzed data in a household account book. Furthermore, the AI ​​household accounting system evaluates the household account book and provides feedback to the user. For example, if food expenses exceed the budget, the AI ​​household accounting system analyzes the cause and provides advice on saving. This allows the AI ​​household accounting system to centrally manage a user's payment history and purchase history, enabling efficient household management. This allows the AI ​​household accounting system to efficiently acquire, analyze, reflect, and evaluate a user's payment history and purchase history. For example, users can centrally manage their payment and purchase history and efficiently manage their household finances. Furthermore, through point redemption information and household accounting evaluations, users can experience the convenience of the economic sphere and potentially attract users from other QR payment methods.

[0029] The AI ​​household accounting system according to the embodiment includes an acquisition unit, an analysis unit, a reflection unit, and an evaluation unit. The acquisition unit automatically acquires a user's payment history or purchase history. The payment history includes, but is not limited to, credit card payments, bank transfers, and cash payments. The purchase history includes, but is not limited to, online shopping history and in-store purchase history. The acquisition unit can automatically acquire data using, for example, an API. The acquisition unit can also acquire data manually entered by a user. The analysis unit analyzes the history data acquired by the acquisition unit and classifies the data by expenditure item. The analysis can be performed using, for example, but is not limited to, data mining technology or statistical analysis. The classification can be performed based on categories such as, for example, food expenses, transportation expenses, and entertainment expenses, but is not limited to, the analysis unit classifies the data by expenditure item using, for example, data mining technology. The analysis unit can also classify the data by statistical analysis. The analysis unit can also classify the data by AI. The reflecting unit automatically reflects the data analyzed by the analyzing unit in the household account book. The household account book includes, for example, income and expenditure items and monthly totals, but is not limited to these examples. The reflecting unit automatically reflects, for example, the analyzed data in the household account book. The reflecting unit can also graph history and assets. For example, the reflecting unit displays monthly expenditures and incomes in bar graphs or line graphs. The evaluating unit evaluates the household account book reflected by the reflecting unit and provides feedback to the user. The evaluation is performed, for example, based on spending efficiency and budget achievement, but is not limited to these examples. The evaluating unit can evaluate, for example, spending efficiency and provide feedback to the user. The evaluating unit can also evaluate budget achievement and provide feedback to the user. The evaluating unit can also use AI to evaluate the household account book and provide feedback to the user. This enables the AI ​​household account book system according to the embodiment to efficiently acquire, analyze, reflect, and evaluate a user's payment history and purchase history. For example, users can centrally manage their payment history and purchase history, allowing them to efficiently manage their household finances.In addition, through point redemption information and household account book evaluations, users can experience the convenience of the economic zone and attract users from other QR payment methods.

[0030] The evaluation unit can evaluate the user's spending situation or budget achievement level and provide suggestions for improvement or advice. The evaluation unit, for example, evaluates the user's spending situation and provides suggestions for improvement. For example, the evaluation unit evaluates the total monthly spending amount or the spending ratio by category and provides suggestions for improvement. The evaluation unit can also evaluate the user's budget achievement level and provide advice. For example, the evaluation unit evaluates the ratio of actual spending to the budget or the frequency of budget overruns and provides advice. This evaluates the user's spending situation and budget achievement level and provides suggestions for improvement or advice, thereby improving the efficiency of household management. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the user's spending situation and budget achievement level into AI and have the AI ​​provide suggestions for improvement or advice.

[0031] The acquisition unit can provide information regarding point redemption. For example, the acquisition unit provides point redemption information for credit cards. For example, the acquisition unit provides information on points being redeemed in response to credit card usage. The acquisition unit can also provide information regarding store point programs. For example, the acquisition unit provides information on points being redeemed in response to purchases at a specific store. By providing point redemption information, the user's incentive to spend is thereby increased. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input point redemption information into AI and have the AI ​​provide the information.

[0032] The analysis unit can classify each expenditure item. For example, the analysis unit classifies expenditure items into categories such as food, transportation, and entertainment. For example, the analysis unit displays expenditure items classified into the food category. The analysis unit can also display expenditure items classified into the transportation category. The analysis unit can also display expenditure items classified into the entertainment category. In this way, by classifying by expenditure item, the user's expenditure situation can be understood in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on expenditure items into AI and have the AI ​​perform the classification.

[0033] The reflection unit can graph the history or assets. The reflection unit, for example, displays monthly expenditures and incomes in bar graphs or line graphs. For example, the reflection unit displays monthly expenditures in a bar graph. The reflection unit can also display monthly incomes in a line graph. The reflection unit can also graph the transition of assets. For example, the reflection unit displays increases and decreases in assets in a line graph. In this way, by graphing the history and assets, the user can visually understand the household situation. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input history and asset data into AI and have the AI ​​perform the graphing.

[0034] The evaluation unit can evaluate the household account book and provide feedback to the user. The evaluation unit, for example, evaluates the household account book and provides feedback to the user. For example, the evaluation unit evaluates the efficiency of spending and the degree of budget achievement and provides feedback to the user. The evaluation unit can also analyze areas for improvement in the household account book and provide feedback to the user. For example, if food expenses exceed the budget, the evaluation unit analyzes the cause and provides advice on saving. In this way, evaluating the household account book and providing feedback to the user promotes improvement in household management. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input household account book data into AI and have the AI ​​perform the evaluation and provide feedback.

[0035] The acquisition unit can analyze the user's past payment history or purchase history and select an appropriate acquisition method. For example, the acquisition unit prioritizes acquisition of payment methods and purchase sites frequently used by the user. For example, the acquisition unit prioritizes acquisition of payment histories of credit cards frequently used by the user. The acquisition unit can also efficiently collect data from the user's past history by acquiring data during a specific time period. For example, the acquisition unit acquires payment histories during a time period frequently used by the user. The acquisition unit can also analyze the user's past history and prioritize acquisition of payment histories and purchase histories of specific categories. For example, the acquisition unit prioritizes acquisition of histories of categories frequently purchased by the user. This enables efficient data acquisition by analyzing the past history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past history data into AI and have the AI ​​select the optimal acquisition method.

[0036] When acquiring payment history or purchase history, the acquisition unit can filter the history based on the user's current lifestyle or areas of interest. For example, if the user is traveling, the acquisition unit prioritizes acquiring travel-related payment history or purchase history. For example, the acquisition unit prioritizes acquiring payment history for hotels and transportation used by the user during the trip. Furthermore, if the user is interested in health, the acquisition unit can prioritize acquiring health-related payment history or purchase history. For example, the acquisition unit prioritizes acquiring payment history for health foods and fitness-related items purchased by the user. Furthermore, if the user has started a new hobby, the acquisition unit can prioritize acquiring payment history or purchase history related to the hobby. For example, the acquisition unit prioritizes acquiring payment history for hobby-related tools and teaching materials purchased by the user. This enables data acquisition according to the user's lifestyle and areas of interest. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input data on the user's lifestyle and areas of interest into AI and have the AI ​​perform filtering.

[0037] When acquiring a payment history or purchase history, the acquisition unit can select an appropriate acquisition means according to the user's input method. For example, if the user uses voice input, the acquisition unit acquires the payment history or purchase history using voice recognition technology. For example, the acquisition unit converts the payment history input by the user's voice into text data using voice recognition technology and acquires it. Furthermore, if the user uses text input, the acquisition unit can also acquire the payment history or purchase history using text analysis technology. For example, the acquisition unit analyzes and acquires the payment history input by the user using text analysis technology. Furthermore, if the user uses image input, the acquisition unit can also acquire the payment history or purchase history using image recognition technology. For example, the acquisition unit analyzes and acquires the payment history input by the user using image recognition technology. This enables optimal data acquisition according to the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit may input the user's input data into AI and have the AI ​​select the optimal acquisition means.

[0038] When acquiring payment history or purchase history, the acquisition unit can prioritize acquiring highly relevant history by taking into account the user's geographical location information. For example, when the user is in a specific region, the acquisition unit prioritizes acquiring payment history or purchase history for that region. For example, when the user is in a specific region, the acquisition unit prioritizes acquiring payment history for that region. Furthermore, when the user is traveling, the acquisition unit can prioritize acquiring payment history or purchase history at the travel destination. For example, the acquisition unit prioritizes acquiring payment history for hotels and transportation used by the user during the trip. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring payment history or purchase history in the vicinity of the user's home. For example, when the user is at home, the acquisition unit prioritizes acquiring purchase history in the vicinity of the user's home. This enables acquisition of highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information into AI and cause the AI ​​to acquire highly relevant history.

[0039] When acquiring a payment history or a purchase history, the acquisition unit can analyze the user's social media activity and acquire related history. For example, the acquisition unit prioritizes acquiring payment history or purchase history for places where the user has checked in on social media. For example, the acquisition unit prioritizes acquiring payment history for places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire related payment history or purchase history. For example, the acquisition unit analyzes the content of the user's social media posts to acquire related purchase history. The acquisition unit can also acquire related payment history or purchase history by referring to the activities of the user's friends on social media. For example, the acquisition unit acquires payment history for places where the user's friends have checked in on social media. This makes it possible to acquire highly relevant data based on the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's social media activity data into AI and cause the AI ​​to acquire related history.

[0040] When acquiring a payment history or a purchase history, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit adjusts the acquisition method based on, for example, feedback provided by the user in the past. For example, the acquisition unit adjusts the acquisition method for the payment history based on feedback provided by the user in the past. Furthermore, if the user provides feedback to prioritize acquisition of history in a specific category, the acquisition unit can prioritize acquisition of history in that category. For example, if the user provides feedback to prioritize acquisition of history in a specific category, the acquisition unit prioritizes acquisition of history in that category. Furthermore, if the user provides feedback to acquire history during a specific time period, the acquisition unit can acquire history during that time period. For example, if the user provides feedback to acquire history during a specific time period, the acquisition unit acquires history during that time period. This enables customized data acquisition based on the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's past feedback data into AI and cause the AI ​​to customize the acquisition method.

[0041] During analysis, the analysis unit can adjust the level of detail of the classification based on the importance of the expenditure item. For example, the analysis unit classifies expenditure items with high importance in detail and expenditure items with low importance in simple classification. For example, the analysis unit classifies expenditure items with high importance into multiple subcategories. The analysis unit can also classify expenditure items with low importance into only major categories. The analysis unit can also analyze expenditure items with high importance in detail and expenditure items with low importance in simple classification. For example, the analysis unit analyzes expenditure items with high importance in detail and expenditure items with low importance in simple classification. This enables efficient classification by adjusting the level of detail of the classification based on the importance of the expenditure item. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input importance data of expenditure items into AI and have the AI ​​adjust the level of detail of the classification.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of expenditure item. For example, the analysis unit applies an analysis algorithm based on the type of ingredients and purchase frequency to the food expense category. For example, the analysis unit applies an analysis algorithm based on the type of ingredients and purchase frequency to the food expense category. The analysis unit can also apply an analysis algorithm based on the means of transportation and usage frequency to the transportation expense category. For example, the analysis unit applies an analysis algorithm based on the means of transportation and usage frequency to the transportation expense category. The analysis unit can also apply an analysis algorithm based on the type of entertainment and usage frequency to the entertainment expense category. For example, the analysis unit applies an analysis algorithm based on the type of entertainment and usage frequency to the entertainment expense category. This enables efficient analysis by applying an analysis algorithm according to the category of expenditure item. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input expenditure item category data into AI and have the AI ​​apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of classification by referring to the user's past analysis results. The analysis unit, for example, improves the classification accuracy of expense items based on the user's past analysis results. For example, the analysis unit improves the classification accuracy of expense items based on the user's past analysis results. The analysis unit can also correct misclassifications based on the user's past analysis results to improve accuracy. For example, the analysis unit corrects misclassifications based on the user's past analysis results to improve accuracy. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results to improve accuracy. For example, the analysis unit adjusts the analysis algorithm by referring to the user's past analysis results to improve accuracy. In this way, the classification accuracy is improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the classification accuracy.

[0044] During analysis, the analysis unit can determine the priority of classification based on the submission time of the expenditure items. For example, the analysis unit prioritizes classification of recently submitted expenditure items. For example, the analysis unit prioritizes classification of recently submitted expenditure items. The analysis unit can also postpone expenditure items that have been submitted recently. For example, the analysis unit postpones expenditure items that have been submitted recently. The analysis unit can also prioritize classification of expenditure items with high importance based on the submission time. For example, the analysis unit prioritizes classification of expenditure items with high importance based on the submission time. This enables efficient classification by determining the priority of classification based on the submission time of the expenditure items. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of expenditure items into AI and have the AI ​​determine the priority of classification.

[0045] During analysis, the analysis unit can adjust the order of classification based on the relevance of the expenditure items. For example, the analysis unit prioritizes classification of highly relevant expenditure items. For example, the analysis unit prioritizes classification of highly relevant expenditure items. The analysis unit can also postpone low-relevance expenditure items. For example, the analysis unit postpones low-relevance expenditure items. The analysis unit can also adjust the order of classification based on the relevance of the expenditure items. For example, the analysis unit adjusts the order of classification based on the relevance of the expenditure items. In this way, efficient classification is possible by adjusting the order of classification based on the relevance of the expenditure items. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of expenditure items into AI and have the AI ​​adjust the order of classification.

[0046] During analysis, the analysis unit can adjust the use of technical terminology for classification according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology for classification. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology for classification. The analysis unit can also use simple terms for classification if the user does not have technical expertise. For example, if the user does not have technical expertise, the analysis unit uses simple terms for classification. The analysis unit can also adjust the use of technical terminology for classification according to the user's level of expertise. This enables efficient classification by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terminology.

[0047] The reflection unit can adjust the level of detail of the reflection based on the importance of the expenditure item when reflecting. For example, the reflection unit reflects expenditure items with high importance in detail and expenditure items with low importance in a simplified manner. For example, the reflection unit reflects expenditure items with high importance in multiple subcategories. The reflection unit can also reflect expenditure items with low importance only in the main category. The reflection unit can also reflect expenditure items with high importance in detail and expenditure items with low importance in a simplified manner. For example, the reflection unit reflects expenditure items with high importance in detail and expenditure items with low importance in a simplified manner. This enables efficient reflection by adjusting the level of detail of the reflection based on the importance of the expenditure item. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input importance data of expenditure items into AI and have the AI ​​adjust the level of detail of the reflection.

[0048] The reflection unit can apply different reflection algorithms depending on the category of the expenditure item when reflecting. For example, the reflection unit applies a reflection algorithm based on the type of ingredients and purchase frequency to the food expense category. For example, the reflection unit applies a reflection algorithm based on the type of ingredients and purchase frequency to the food expense category. The reflection unit can also apply a reflection algorithm based on the means of transportation and usage frequency to the transportation expense category. For example, the reflection unit applies a reflection algorithm based on the means of transportation and usage frequency to the transportation expense category. The reflection unit can also apply a reflection algorithm based on the type of entertainment and usage frequency to the entertainment expense category. For example, the reflection unit applies a reflection algorithm based on the type of entertainment and usage frequency to the entertainment expense category. This enables efficient reflection by applying a reflection algorithm according to the category of the expenditure item. Some or all of the above-mentioned processing in the reflection unit may be performed using AI, for example, or may be performed without using AI. For example, the reflection unit can input category data of the expenditure item into AI and cause the AI ​​to apply the reflection algorithm.

[0049] The reflection unit can improve the accuracy of the reflection by referring to the user's past reflection results when reflecting data. The reflection unit, for example, improves the reflection accuracy of expenditure items based on the user's past reflection results. For example, the reflection unit improves the reflection accuracy of expenditure items based on the user's past reflection results. The reflection unit can also correct erroneous reflections based on the user's past reflection results to improve accuracy. For example, the reflection unit corrects erroneous reflections based on the user's past reflection results to improve accuracy. The reflection unit can also adjust the reflection algorithm by referring to the user's past reflection results to improve accuracy. For example, the reflection unit adjusts the reflection algorithm by referring to the user's past reflection results to improve accuracy. In this way, the accuracy of the reflection is improved by referring to the user's past reflection results. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input the user's past reflection result data into AI and have the AI ​​improve the accuracy of the reflection.

[0050] At the time of reflection, the reflection unit can determine the priority of reflection based on the submission time of the expenditure item. For example, the reflection unit prioritizes the reflection of recently submitted expenditure items. For example, the reflection unit prioritizes the reflection of recently submitted expenditure items. The reflection unit can also postpone expenditure items that have been submitted recently. For example, the reflection unit postpones expenditure items that have been submitted recently. The reflection unit can also prioritize the reflection of expenditure items with high importance based on the submission time. For example, the reflection unit prioritizes the reflection of expenditure items with high importance based on the submission time. This enables efficient reflection by determining the priority of reflection based on the submission time of the expenditure item. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input data on the submission time of expenditure items into AI and have the AI ​​determine the priority of reflection.

[0051] The reflection unit can adjust the order of reflection based on the relevance of the expenditure items when reflecting. For example, the reflection unit prioritizes reflecting highly relevant expenditure items. For example, the reflection unit prioritizes reflecting highly relevant expenditure items. The reflection unit can also postpone less relevant expenditure items. For example, the reflection unit postpones less relevant expenditure items. The reflection unit can also adjust the order of reflection based on the relevance of the expenditure items. For example, the reflection unit adjusts the order of reflection based on the relevance of the expenditure items. This enables efficient reflection by adjusting the order of reflection based on the relevance of the expenditure items. Some or all of the above-mentioned processing in the reflection unit may be performed using AI, for example, or may be performed without using AI. For example, the reflection unit can input relevance data of expenditure items into AI and have the AI ​​adjust the order of reflection.

[0052] The reflection unit can adjust the use of technical terms in the reflection according to the user's level of expertise during reflection. For example, if the user has technical expertise, the reflection unit uses detailed technical terms for reflection. For example, if the user has technical expertise, the reflection unit uses detailed technical terms for reflection. The reflection unit can also use simple terms for reflection if the user does not have technical expertise. For example, if the user does not have technical expertise, the reflection unit uses simple terms for reflection. The reflection unit can also adjust the use of technical terms in the reflection according to the user's level of expertise. This enables efficient reflection by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the reflection unit can be performed using AI, for example, or without AI. For example, the reflection unit can input the user's level of expertise data into AI and cause the AI ​​to adjust the use of technical terms.

[0053] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the expenditure item during evaluation. For example, the evaluation unit evaluates expenditure items with high importance in detail and evaluates expenditure items with low importance in a simplified manner. For example, the evaluation unit evaluates expenditure items with high importance into multiple subcategories. The evaluation unit can also evaluate expenditure items with low importance only into major categories. The evaluation unit can also evaluate expenditure items with high importance in detail and expenditure items with low importance in a simplified manner. For example, the evaluation unit evaluates expenditure items with high importance in detail and expenditure items with low importance in a simplified manner. This enables efficient evaluation by adjusting the level of detail of the evaluation based on the importance of the expenditure item. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input importance data of expenditure items into AI and have the AI ​​adjust the level of detail of the evaluation.

[0054] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the category of the expenditure item. For example, the evaluation unit applies an evaluation algorithm based on the type of ingredients and purchase frequency to the food expense category. For example, the evaluation unit applies an evaluation algorithm based on the type of ingredients and purchase frequency to the food expense category. The evaluation unit can also apply an evaluation algorithm based on the means of transportation and usage frequency to the transportation expense category. For example, the evaluation unit applies an evaluation algorithm based on the means of transportation and usage frequency to the transportation expense category. The evaluation unit can also apply an evaluation algorithm based on the type of entertainment and usage frequency to the entertainment expense category. For example, the evaluation unit applies an evaluation algorithm based on the type of entertainment and usage frequency to the entertainment expense category. This enables efficient evaluation by applying an evaluation algorithm according to the category of the expenditure item. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input expenditure item category data into AI and have the AI ​​apply the evaluation algorithm.

[0055] The evaluation unit can improve the accuracy of the evaluation by referring to the user's past evaluation results when evaluating. The evaluation unit, for example, improves the evaluation accuracy of expenditure items based on the user's past evaluation results. For example, the evaluation unit improves the evaluation accuracy of expenditure items based on the user's past evaluation results. The evaluation unit can also correct erroneous evaluations based on the user's past evaluation results to improve accuracy. For example, the evaluation unit corrects erroneous evaluations based on the user's past evaluation results to improve accuracy. The evaluation unit can also adjust the evaluation algorithm by referring to the user's past evaluation results to improve accuracy. For example, the evaluation unit adjusts the evaluation algorithm by referring to the user's past evaluation results to improve accuracy. In this way, the accuracy of the evaluation is improved by referring to the user's past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past evaluation result data into AI and have the AI ​​improve the accuracy of the evaluation.

[0056] During evaluation, the evaluation unit can determine the priority of evaluation based on the submission date of the expenditure item. For example, the evaluation unit prioritizes evaluation of recently submitted expenditure items. For example, the evaluation unit prioritizes evaluation of recently submitted expenditure items. The evaluation unit can also postpone evaluation of expenditure items that have been submitted recently. For example, the evaluation unit postpones evaluation of expenditure items that have been submitted recently. The evaluation unit can also prioritize evaluation of expenditure items with high importance based on the submission date. For example, the evaluation unit prioritizes evaluation of expenditure items with high importance based on the submission date. This enables efficient evaluation by determining the priority of evaluation based on the submission date of the expenditure item. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the submission date of the expenditure item into AI and have the AI ​​determine the priority of evaluation.

[0057] During evaluation, the evaluation unit can adjust the order of evaluation based on the relevance of the expenditure items. For example, the evaluation unit prioritizes evaluation of highly relevant expenditure items. For example, the evaluation unit prioritizes evaluation of highly relevant expenditure items. The evaluation unit can also postpone less relevant expenditure items. For example, the evaluation unit postpones less relevant expenditure items. The evaluation unit can also adjust the order of evaluation based on the relevance of the expenditure items. For example, the evaluation unit adjusts the order of evaluation based on the relevance of the expenditure items. This enables efficient evaluation by adjusting the order of evaluation based on the relevance of the expenditure items. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input relevance data of expenditure items into AI and have the AI ​​adjust the order of evaluation.

[0058] During evaluation, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's level of expertise. For example, if the user has technical expertise, the evaluation unit uses detailed technical terminology in the evaluation. For example, if the user has technical expertise, the evaluation unit uses detailed technical terminology in the evaluation. The evaluation unit can also use simple terminology in the evaluation if the user does not have technical expertise. For example, if the user does not have technical expertise, the evaluation unit uses simple terminology in the evaluation. The evaluation unit can also adjust the use of technical terminology in the evaluation according to the user's level of expertise. This enables efficient evaluation by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without AI. For example, the evaluation unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terminology.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When analyzing a user's spending history, the analysis unit can adjust the analysis algorithm taking into account the user's past spending patterns. For example, if the user has made large expenditures in a particular month in the past, the analysis unit can analyze the expenditures for that month in detail. Also, if the user has made frequent expenditures in a particular category in the past, the analysis unit can prioritize the analysis of expenditures in that category. This enables efficient analysis based on the user's past spending patterns.

[0061] When analyzing a user's spending history, the analysis unit can adjust the analysis algorithm taking into account the user's hobbies and preferences. For example, if a user spends a lot on a particular hobby, the analysis unit can analyze spending related to that hobby in detail. Also, if a user prefers a particular brand or store, the analysis unit can prioritize spending on that brand or store. This enables efficient analysis based on the user's hobbies and preferences.

[0062] When acquiring a user's payment history or purchase history, the acquisition unit can adjust the acquisition timing taking into account the user's current geographical location information. For example, if the user is traveling, the acquisition unit can prioritize acquiring payment history or purchase history from the travel destination. Also, if the user is at home, the acquisition unit can prioritize acquiring payment history or purchase history from around the home. This enables efficient data acquisition based on the user's geographical location information.

[0063] When analyzing a user's spending history, the analysis unit can adjust the analysis algorithm taking into account the user's past feedback. For example, the analysis unit can adjust the classification method for spending items based on the user's past feedback. Also, if the user provides feedback to analyze spending in a specific category in more detail, the analysis unit can prioritize the analysis of spending in that category. This enables efficient analysis based on the user's past feedback.

[0064] When acquiring a user's payment history or purchase history, the acquisition unit can adjust the acquisition timing taking into account the user's social media activity. For example, the acquisition unit can prioritize acquisition of payment history or purchase history for locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media to acquire related payment history or purchase history. This enables efficient data acquisition based on the user's social media activity.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The acquisition unit automatically acquires the user's payment history or purchase history. Payment history includes credit card payments, bank transfers, cash payments, etc., while purchase history includes online shopping history, in-store purchase history, etc. The acquisition unit can acquire data automatically using an API, or it can acquire data manually entered by the user. Step 2: The analysis unit analyzes the historical data acquired by the acquisition unit and classifies it by expenditure item. The analysis is performed using data mining technology, statistical analysis methods, and AI, and classification is based on categories such as food expenses, transportation expenses, and entertainment expenses. Step 3: The reflection unit automatically reflects the data analyzed by the analysis unit in the household ledger. The household ledger includes income and expenditure items, monthly totals, etc., and the reflection unit automatically reflects the analyzed data and also creates graphs of history and assets. For example, monthly expenditure and income amounts can be displayed in bar graphs or line graphs. Step 4: The evaluation unit evaluates the household account book reflected by the reflection unit and provides feedback to the user. The evaluation is based on the efficiency of spending and the degree of budget achievement, and AI can also be used to evaluate the household account book and provide feedback to the user.

[0067] (Example 2) An AI household accounting system according to an embodiment of the present invention automatically acquires a user's payment history and purchase history, analyzes the history using AI, and automatically reflects the history in a household account book and provides an evaluation. The AI ​​household accounting system automatically acquires a user's payment history and purchase history, analyzes the history, classifies the history by expenditure item, and automatically reflects the history in a household account book. The AI ​​household accounting system also evaluates the household account book and provides feedback to the user. For example, when a user makes a payment, the AI ​​household accounting system automatically reflects the history in an app. Next, the AI ​​analyzes the acquired history data and classifies it by expenditure item. For example, the AI ​​automatically classifies the history into categories such as food expenses, transportation expenses, and entertainment expenses. Next, the AI ​​household accounting system automatically reflects the analyzed data in a household account book. Furthermore, the AI ​​household accounting system evaluates the household account book and provides feedback to the user. For example, if food expenses exceed the budget, the AI ​​household accounting system analyzes the cause and provides advice on saving. This allows the AI ​​household accounting system to centrally manage a user's payment history and purchase history, enabling efficient household management. This allows the AI ​​household accounting system to efficiently acquire, analyze, reflect, and evaluate a user's payment history and purchase history. For example, users can centrally manage their payment and purchase history and efficiently manage their household finances. Furthermore, through point redemption information and household accounting evaluations, users can experience the convenience of the economic sphere and potentially attract users from other QR payment methods.

[0068] The AI ​​household accounting system according to the embodiment includes an acquisition unit, an analysis unit, a reflection unit, and an evaluation unit. The acquisition unit automatically acquires a user's payment history or purchase history. The payment history includes, but is not limited to, credit card payments, bank transfers, and cash payments. The purchase history includes, but is not limited to, online shopping history and in-store purchase history. The acquisition unit can automatically acquire data using, for example, an API. The acquisition unit can also acquire data manually entered by a user. The analysis unit analyzes the history data acquired by the acquisition unit and classifies the data by expenditure item. The analysis can be performed using, for example, but is not limited to, data mining technology or statistical analysis. The classification can be performed based on categories such as, for example, food expenses, transportation expenses, and entertainment expenses, but is not limited to, the analysis unit classifies the data by expenditure item using, for example, data mining technology. The analysis unit can also classify the data by statistical analysis. The analysis unit can also classify the data by AI. The reflecting unit automatically reflects the data analyzed by the analyzing unit in the household account book. The household account book includes, for example, income and expenditure items and monthly totals, but is not limited to these examples. The reflecting unit automatically reflects, for example, the analyzed data in the household account book. The reflecting unit can also graph history and assets. For example, the reflecting unit displays monthly expenditures and incomes in bar graphs or line graphs. The evaluating unit evaluates the household account book reflected by the reflecting unit and provides feedback to the user. The evaluation is performed, for example, based on spending efficiency and budget achievement, but is not limited to these examples. The evaluating unit can evaluate, for example, spending efficiency and provide feedback to the user. The evaluating unit can also evaluate budget achievement and provide feedback to the user. The evaluating unit can also use AI to evaluate the household account book and provide feedback to the user. This enables the AI ​​household account book system according to the embodiment to efficiently acquire, analyze, reflect, and evaluate a user's payment history and purchase history. For example, users can centrally manage their payment history and purchase history, allowing them to efficiently manage their household finances.In addition, through point redemption information and household account book evaluations, users can experience the convenience of the economic zone and attract users from other QR payment methods.

[0069] The evaluation unit can evaluate the user's spending situation or budget achievement level and provide suggestions for improvement or advice. The evaluation unit, for example, evaluates the user's spending situation and provides suggestions for improvement. For example, the evaluation unit evaluates the total monthly spending amount or the spending ratio by category and provides suggestions for improvement. The evaluation unit can also evaluate the user's budget achievement level and provide advice. For example, the evaluation unit evaluates the ratio of actual spending to the budget or the frequency of budget overruns and provides advice. This evaluates the user's spending situation and budget achievement level and provides suggestions for improvement or advice, thereby improving the efficiency of household management. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the user's spending situation and budget achievement level into AI and have the AI ​​provide suggestions for improvement or advice.

[0070] The acquisition unit can provide information regarding point redemption. For example, the acquisition unit provides point redemption information for credit cards. For example, the acquisition unit provides information on points being redeemed in response to credit card usage. The acquisition unit can also provide information regarding store point programs. For example, the acquisition unit provides information on points being redeemed in response to purchases at a specific store. By providing point redemption information, the user's incentive to spend is thereby increased. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input point redemption information into AI and have the AI ​​provide the information.

[0071] The analysis unit can classify each expenditure item. For example, the analysis unit classifies expenditure items into categories such as food, transportation, and entertainment. For example, the analysis unit displays expenditure items classified into the food category. The analysis unit can also display expenditure items classified into the transportation category. The analysis unit can also display expenditure items classified into the entertainment category. In this way, by classifying by expenditure item, the user's expenditure situation can be understood in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on expenditure items into AI and have the AI ​​perform the classification.

[0072] The reflection unit can graph the history or assets. The reflection unit, for example, displays monthly expenditures and incomes in bar graphs or line graphs. For example, the reflection unit displays monthly expenditures in a bar graph. The reflection unit can also display monthly incomes in a line graph. The reflection unit can also graph the transition of assets. For example, the reflection unit displays increases and decreases in assets in a line graph. In this way, by graphing the history and assets, the user can visually understand the household situation. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input history and asset data into AI and have the AI ​​perform the graphing.

[0073] The evaluation unit can evaluate the household account book and provide feedback to the user. The evaluation unit, for example, evaluates the household account book and provides feedback to the user. For example, the evaluation unit evaluates the efficiency of spending and the degree of budget achievement and provides feedback to the user. The evaluation unit can also analyze areas for improvement in the household account book and provide feedback to the user. For example, if food expenses exceed the budget, the evaluation unit analyzes the cause and provides advice on saving. In this way, evaluating the household account book and providing feedback to the user promotes improvement in household management. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input household account book data into AI and have the AI ​​perform the evaluation and provide feedback.

[0074] The AI ​​household accounting system further includes an acquisition unit that estimates a user's emotions and adjusts the timing of acquiring payment histories or purchase histories based on the estimated user emotions. For example, when the user is stressed, the acquisition unit delays the acquisition of payment histories or purchase histories and acquires them when the user is relaxed. Furthermore, when the user is in a hurry, the acquisition unit can immediately acquire payment histories or purchase histories and quickly reflect them in the household accounting system. Furthermore, when the user is relaxed, the acquisition unit can periodically acquire payment histories or purchase histories to avoid burdening the user. This reduces the burden on the user by adjusting the acquisition timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI. For example, the acquisition unit may input the user's emotion data into an AI and have the AI ​​adjust the acquisition timing.

[0075] The acquisition unit can analyze the user's past payment history or purchase history and select an appropriate acquisition method. For example, the acquisition unit prioritizes acquisition of payment methods and purchase sites frequently used by the user. For example, the acquisition unit prioritizes acquisition of payment histories of credit cards frequently used by the user. The acquisition unit can also efficiently collect data from the user's past history by acquiring data during a specific time period. For example, the acquisition unit acquires payment histories during a time period frequently used by the user. The acquisition unit can also analyze the user's past history and prioritize acquisition of payment histories and purchase histories of specific categories. For example, the acquisition unit prioritizes acquisition of histories of categories frequently purchased by the user. This enables efficient data acquisition by analyzing the past history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past history data into AI and have the AI ​​select the optimal acquisition method.

[0076] When acquiring payment history or purchase history, the acquisition unit can filter the history based on the user's current lifestyle or areas of interest. For example, if the user is traveling, the acquisition unit prioritizes acquiring travel-related payment history or purchase history. For example, the acquisition unit prioritizes acquiring payment history for hotels and transportation used by the user during the trip. Furthermore, if the user is interested in health, the acquisition unit can prioritize acquiring health-related payment history or purchase history. For example, the acquisition unit prioritizes acquiring payment history for health foods and fitness-related items purchased by the user. Furthermore, if the user has started a new hobby, the acquisition unit can prioritize acquiring payment history or purchase history related to the hobby. For example, the acquisition unit prioritizes acquiring payment history for hobby-related tools and teaching materials purchased by the user. This enables data acquisition according to the user's lifestyle and areas of interest. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input data on the user's lifestyle and areas of interest into AI and have the AI ​​perform filtering.

[0077] When acquiring a payment history or purchase history, the acquisition unit can select an appropriate acquisition means according to the user's input method. For example, if the user uses voice input, the acquisition unit acquires the payment history or purchase history using voice recognition technology. For example, the acquisition unit converts the payment history input by the user's voice into text data using voice recognition technology and acquires it. Furthermore, if the user uses text input, the acquisition unit can also acquire the payment history or purchase history using text analysis technology. For example, the acquisition unit analyzes and acquires the payment history input by the user using text analysis technology. Furthermore, if the user uses image input, the acquisition unit can also acquire the payment history or purchase history using image recognition technology. For example, the acquisition unit analyzes and acquires the payment history input by the user using image recognition technology. This enables optimal data acquisition according to the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit may input the user's input data into AI and have the AI ​​select the optimal acquisition means.

[0078] The acquisition unit can estimate the user's emotions and determine the priority of history to be acquired based on the estimated user emotions. For example, when the user is stressed, the acquisition unit postpones acquiring less important history and prioritizes acquiring more important history. For example, when the user is stressed, the acquisition unit prioritizes acquiring more important payment history. Furthermore, when the user is relaxed, the acquisition unit can acquire all history evenly. For example, when the user is relaxed, the acquisition unit acquires all payment history evenly. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring the most recent history. For example, when the user is in a hurry, the acquisition unit prioritizes acquiring the most recent purchase history. This enables efficient data acquisition by determining the priority of history according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input user emotion data into AI and have the AI ​​determine the priority of the history.

[0079] When acquiring payment history or purchase history, the acquisition unit can prioritize acquiring highly relevant history by taking into account the user's geographical location information. For example, when the user is in a specific region, the acquisition unit prioritizes acquiring payment history or purchase history for that region. For example, when the user is in a specific region, the acquisition unit prioritizes acquiring payment history for that region. Furthermore, when the user is traveling, the acquisition unit can prioritize acquiring payment history or purchase history at the travel destination. For example, the acquisition unit prioritizes acquiring payment history for hotels and transportation used by the user during the trip. Furthermore, when the user is at home, the acquisition unit can prioritize acquiring payment history or purchase history in the vicinity of the user's home. For example, when the user is at home, the acquisition unit prioritizes acquiring purchase history in the vicinity of the user's home. This enables acquisition of highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information into AI and cause the AI ​​to acquire highly relevant history.

[0080] When acquiring a payment history or a purchase history, the acquisition unit can analyze the user's social media activity and acquire related history. For example, the acquisition unit prioritizes acquiring payment history or purchase history for places where the user has checked in on social media. For example, the acquisition unit prioritizes acquiring payment history for places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire related payment history or purchase history. For example, the acquisition unit analyzes the content of the user's social media posts to acquire related purchase history. The acquisition unit can also acquire related payment history or purchase history by referring to the activities of the user's friends on social media. For example, the acquisition unit acquires payment history for places where the user's friends have checked in on social media. This makes it possible to acquire highly relevant data based on the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's social media activity data into AI and cause the AI ​​to acquire related history.

[0081] When acquiring a payment history or a purchase history, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit adjusts the acquisition method based on, for example, feedback provided by the user in the past. For example, the acquisition unit adjusts the acquisition method for the payment history based on feedback provided by the user in the past. Furthermore, if the user provides feedback to prioritize acquisition of history in a specific category, the acquisition unit can prioritize acquisition of history in that category. For example, if the user provides feedback to prioritize acquisition of history in a specific category, the acquisition unit prioritizes acquisition of history in that category. Furthermore, if the user provides feedback to acquire history during a specific time period, the acquisition unit can acquire history during that time period. For example, if the user provides feedback to acquire history during a specific time period, the acquisition unit acquires history during that time period. This enables customized data acquisition based on the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's past feedback data into AI and cause the AI ​​to customize the acquisition method.

[0082] The analysis unit can estimate the user's emotions and adjust the classification method of expense items based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit uses a simple classification method and avoids detailed classification. For example, if the user is feeling stressed, the analysis unit uses the simple classification method. Furthermore, if the user is relaxed, the analysis unit can use a detailed classification method and classify items into detailed categories. For example, if the user is relaxed, the analysis unit uses the detailed classification method. Furthermore, if the user is in a hurry, the analysis unit can classify items into only major categories and perform analysis quickly. For example, if the user is in a hurry, the analysis unit classifies items into only major categories. This enables efficient classification by adjusting the classification method of expense items according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the classification method.

[0083] During analysis, the analysis unit can adjust the level of detail of the classification based on the importance of the expenditure item. For example, the analysis unit classifies expenditure items with high importance in detail and expenditure items with low importance in simple classification. For example, the analysis unit classifies expenditure items with high importance into multiple subcategories. The analysis unit can also classify expenditure items with low importance into only major categories. The analysis unit can also analyze expenditure items with high importance in detail and expenditure items with low importance in simple classification. For example, the analysis unit analyzes expenditure items with high importance in detail and expenditure items with low importance in simple classification. This enables efficient classification by adjusting the level of detail of the classification based on the importance of the expenditure item. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input importance data of expenditure items into AI and have the AI ​​adjust the level of detail of the classification.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of expenditure item. For example, the analysis unit applies an analysis algorithm based on the type of ingredients and purchase frequency to the food expense category. For example, the analysis unit applies an analysis algorithm based on the type of ingredients and purchase frequency to the food expense category. The analysis unit can also apply an analysis algorithm based on the means of transportation and usage frequency to the transportation expense category. For example, the analysis unit applies an analysis algorithm based on the means of transportation and usage frequency to the transportation expense category. The analysis unit can also apply an analysis algorithm based on the type of entertainment and usage frequency to the entertainment expense category. For example, the analysis unit applies an analysis algorithm based on the type of entertainment and usage frequency to the entertainment expense category. This enables efficient analysis by applying an analysis algorithm according to the category of expenditure item. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input expenditure item category data into AI and have the AI ​​apply the analysis algorithm.

[0085] During analysis, the analysis unit can improve the accuracy of classification by referring to the user's past analysis results. The analysis unit, for example, improves the classification accuracy of expense items based on the user's past analysis results. For example, the analysis unit improves the classification accuracy of expense items based on the user's past analysis results. The analysis unit can also correct misclassifications based on the user's past analysis results to improve accuracy. For example, the analysis unit corrects misclassifications based on the user's past analysis results to improve accuracy. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results to improve accuracy. For example, the analysis unit adjusts the analysis algorithm by referring to the user's past analysis results to improve accuracy. In this way, the classification accuracy is improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the classification accuracy.

[0086] The analysis unit can estimate the user's emotions and determine the priority of expense item classification based on the estimated user emotions. For example, when the user is stressed, the analysis unit prioritizes classification of expense items with high importance. For example, when the user is stressed, the analysis unit prioritizes classification of expense items with high importance. The analysis unit can also classify all expense items equally when the user is relaxed. For example, when the user is relaxed, the analysis unit classifies all expense items equally. The analysis unit can also prioritize classification of major expense items when the user is in a hurry. For example, when the user is in a hurry, the analysis unit prioritizes classification of major expense items. This enables efficient classification by determining the priority of expense item classification according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user emotional data into the AI ​​and have the AI ​​determine the classification priorities.

[0087] During analysis, the analysis unit can determine the priority of classification based on the submission time of the expenditure items. For example, the analysis unit prioritizes classification of recently submitted expenditure items. For example, the analysis unit prioritizes classification of recently submitted expenditure items. The analysis unit can also postpone expenditure items that have been submitted recently. For example, the analysis unit postpones expenditure items that have been submitted recently. The analysis unit can also prioritize classification of expenditure items with high importance based on the submission time. For example, the analysis unit prioritizes classification of expenditure items with high importance based on the submission time. This enables efficient classification by determining the priority of classification based on the submission time of the expenditure items. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of expenditure items into AI and have the AI ​​determine the priority of classification.

[0088] During analysis, the analysis unit can adjust the order of classification based on the relevance of the expenditure items. For example, the analysis unit prioritizes classification of highly relevant expenditure items. For example, the analysis unit prioritizes classification of highly relevant expenditure items. The analysis unit can also postpone low-relevance expenditure items. For example, the analysis unit postpones low-relevance expenditure items. The analysis unit can also adjust the order of classification based on the relevance of the expenditure items. For example, the analysis unit adjusts the order of classification based on the relevance of the expenditure items. In this way, efficient classification is possible by adjusting the order of classification based on the relevance of the expenditure items. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of expenditure items into AI and have the AI ​​adjust the order of classification.

[0089] During analysis, the analysis unit can adjust the use of technical terminology for classification according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology for classification. For example, if the user has technical expertise, the analysis unit uses detailed technical terminology for classification. The analysis unit can also use simple terms for classification if the user does not have technical expertise. For example, if the user does not have technical expertise, the analysis unit uses simple terms for classification. The analysis unit can also adjust the use of technical terminology for classification according to the user's level of expertise. This enables efficient classification by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terminology.

[0090] The reflection unit can estimate the user's emotions and adjust the reflection method in the household ledger based on the estimated user's emotions. For example, when the user is stressed, the reflection unit uses a simple reflection method and avoids detailed reflection. For example, when the user is stressed, the reflection unit uses the simple reflection method. Furthermore, when the user is relaxed, the reflection unit can also use a detailed reflection method and reflect on detailed items. For example, when the user is relaxed, the reflection unit uses the detailed reflection method. Furthermore, when the user is in a hurry, the reflection unit can also reflect only major items and perform quick reflection. For example, when the user is in a hurry, the reflection unit reflects only major items. This enables efficient reflection by adjusting the reflection method in the household ledger according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reflection unit can be performed using, for example, an AI, or without an AI. For example, the reflection unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the reflection method.

[0091] The reflection unit can adjust the level of detail of the reflection based on the importance of the expenditure item when reflecting. For example, the reflection unit reflects expenditure items with high importance in detail and expenditure items with low importance in a simplified manner. For example, the reflection unit reflects expenditure items with high importance in multiple subcategories. The reflection unit can also reflect expenditure items with low importance only in the main category. The reflection unit can also reflect expenditure items with high importance in detail and expenditure items with low importance in a simplified manner. For example, the reflection unit reflects expenditure items with high importance in detail and expenditure items with low importance in a simplified manner. This enables efficient reflection by adjusting the level of detail of the reflection based on the importance of the expenditure item. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input importance data of expenditure items into AI and have the AI ​​adjust the level of detail of the reflection.

[0092] The reflection unit can apply different reflection algorithms depending on the category of the expenditure item when reflecting. For example, the reflection unit applies a reflection algorithm based on the type of ingredients and purchase frequency to the food expense category. For example, the reflection unit applies a reflection algorithm based on the type of ingredients and purchase frequency to the food expense category. The reflection unit can also apply a reflection algorithm based on the means of transportation and usage frequency to the transportation expense category. For example, the reflection unit applies a reflection algorithm based on the means of transportation and usage frequency to the transportation expense category. The reflection unit can also apply a reflection algorithm based on the type of entertainment and usage frequency to the entertainment expense category. For example, the reflection unit applies a reflection algorithm based on the type of entertainment and usage frequency to the entertainment expense category. This enables efficient reflection by applying a reflection algorithm according to the category of the expenditure item. Some or all of the above-mentioned processing in the reflection unit may be performed using AI, for example, or may be performed without using AI. For example, the reflection unit can input category data of the expenditure item into AI and cause the AI ​​to apply the reflection algorithm.

[0093] The reflection unit can improve the accuracy of the reflection by referring to the user's past reflection results when reflecting data. The reflection unit, for example, improves the reflection accuracy of expenditure items based on the user's past reflection results. For example, the reflection unit improves the reflection accuracy of expenditure items based on the user's past reflection results. The reflection unit can also correct erroneous reflections based on the user's past reflection results to improve accuracy. For example, the reflection unit corrects erroneous reflections based on the user's past reflection results to improve accuracy. The reflection unit can also adjust the reflection algorithm by referring to the user's past reflection results to improve accuracy. For example, the reflection unit adjusts the reflection algorithm by referring to the user's past reflection results to improve accuracy. In this way, the accuracy of the reflection is improved by referring to the user's past reflection results. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input the user's past reflection result data into AI and have the AI ​​improve the accuracy of the reflection.

[0094] The reflection unit can estimate the user's emotions and determine the priority of reflecting the data in the household ledger based on the estimated user's emotions. For example, when the user is feeling stressed, the reflection unit prioritizes reflecting expense items with high importance. For example, when the user is feeling stressed, the reflection unit prioritizes reflecting expense items with high importance. The reflection unit can also reflect all expense items evenly when the user is relaxed. For example, when the user is relaxed, the reflection unit can reflect all expense items evenly. The reflection unit can also prioritize reflecting major expense items when the user is in a hurry. For example, when the user is in a hurry, the reflection unit prioritizes reflecting major expense items. This enables efficient reflection by determining the priority of reflecting the data in the household ledger according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit may input user emotion data into AI and have the AI ​​determine the priority of reflection.

[0095] At the time of reflection, the reflection unit can determine the priority of reflection based on the submission time of the expenditure item. For example, the reflection unit prioritizes the reflection of recently submitted expenditure items. For example, the reflection unit prioritizes the reflection of recently submitted expenditure items. The reflection unit can also postpone expenditure items that have been submitted recently. For example, the reflection unit postpones expenditure items that have been submitted recently. The reflection unit can also prioritize the reflection of expenditure items with high importance based on the submission time. For example, the reflection unit prioritizes the reflection of expenditure items with high importance based on the submission time. This enables efficient reflection by determining the priority of reflection based on the submission time of the expenditure item. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input data on the submission time of expenditure items into AI and have the AI ​​determine the priority of reflection.

[0096] The reflection unit can adjust the order of reflection based on the relevance of the expenditure items when reflecting. For example, the reflection unit prioritizes reflecting highly relevant expenditure items. For example, the reflection unit prioritizes reflecting highly relevant expenditure items. The reflection unit can also postpone less relevant expenditure items. For example, the reflection unit postpones less relevant expenditure items. The reflection unit can also adjust the order of reflection based on the relevance of the expenditure items. For example, the reflection unit adjusts the order of reflection based on the relevance of the expenditure items. This enables efficient reflection by adjusting the order of reflection based on the relevance of the expenditure items. Some or all of the above-mentioned processing in the reflection unit may be performed using AI, for example, or may be performed without using AI. For example, the reflection unit can input relevance data of expenditure items into AI and have the AI ​​adjust the order of reflection.

[0097] The reflection unit can adjust the use of technical terms in the reflection according to the user's level of expertise during reflection. For example, if the user has technical expertise, the reflection unit uses detailed technical terms for reflection. For example, if the user has technical expertise, the reflection unit uses detailed technical terms for reflection. The reflection unit can also use simple terms for reflection if the user does not have technical expertise. For example, if the user does not have technical expertise, the reflection unit uses simple terms for reflection. The reflection unit can also adjust the use of technical terms in the reflection according to the user's level of expertise. This enables efficient reflection by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the reflection unit can be performed using AI, for example, or without AI. For example, the reflection unit can input the user's level of expertise data into AI and cause the AI ​​to adjust the use of technical terms.

[0098] The evaluation unit can estimate the user's emotions and adjust the household account book evaluation method based on the estimated user's emotions. For example, when the user is stressed, the evaluation unit uses a simple evaluation method and avoids detailed evaluation. For example, when the user is stressed, the evaluation unit uses the simple evaluation method. Also, when the user is relaxed, the evaluation unit can use a detailed evaluation method and evaluate detailed items. For example, when the user is relaxed, the evaluation unit uses the detailed evaluation method. Also, when the user is in a hurry, the evaluation unit can evaluate only major items and perform a quick evaluation. For example, when the user is in a hurry, the evaluation unit evaluates only major items. This enables efficient evaluation by adjusting the household account book evaluation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without AI. For example, the evaluation unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the evaluation method.

[0099] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the expenditure item during evaluation. For example, the evaluation unit evaluates expenditure items with high importance in detail and evaluates expenditure items with low importance in a simplified manner. For example, the evaluation unit evaluates expenditure items with high importance into multiple subcategories. The evaluation unit can also evaluate expenditure items with low importance only into major categories. The evaluation unit can also evaluate expenditure items with high importance in detail and expenditure items with low importance in a simplified manner. For example, the evaluation unit evaluates expenditure items with high importance in detail and expenditure items with low importance in a simplified manner. This enables efficient evaluation by adjusting the level of detail of the evaluation based on the importance of the expenditure item. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input importance data of expenditure items into AI and have the AI ​​adjust the level of detail of the evaluation.

[0100] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the category of the expenditure item. For example, the evaluation unit applies an evaluation algorithm based on the type of ingredients and purchase frequency to the food expense category. For example, the evaluation unit applies an evaluation algorithm based on the type of ingredients and purchase frequency to the food expense category. The evaluation unit can also apply an evaluation algorithm based on the means of transportation and usage frequency to the transportation expense category. For example, the evaluation unit applies an evaluation algorithm based on the means of transportation and usage frequency to the transportation expense category. The evaluation unit can also apply an evaluation algorithm based on the type of entertainment and usage frequency to the entertainment expense category. For example, the evaluation unit applies an evaluation algorithm based on the type of entertainment and usage frequency to the entertainment expense category. This enables efficient evaluation by applying an evaluation algorithm according to the category of the expenditure item. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input expenditure item category data into AI and have the AI ​​apply the evaluation algorithm.

[0101] The evaluation unit can improve the accuracy of the evaluation by referring to the user's past evaluation results when evaluating. The evaluation unit, for example, improves the evaluation accuracy of expenditure items based on the user's past evaluation results. For example, the evaluation unit improves the evaluation accuracy of expenditure items based on the user's past evaluation results. The evaluation unit can also correct erroneous evaluations based on the user's past evaluation results to improve accuracy. For example, the evaluation unit corrects erroneous evaluations based on the user's past evaluation results to improve accuracy. The evaluation unit can also adjust the evaluation algorithm by referring to the user's past evaluation results to improve accuracy. For example, the evaluation unit adjusts the evaluation algorithm by referring to the user's past evaluation results to improve accuracy. In this way, the accuracy of the evaluation is improved by referring to the user's past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the user's past evaluation result data into AI and have the AI ​​improve the accuracy of the evaluation.

[0102] The evaluation unit can estimate the user's emotions and determine the priority of the household account book evaluation based on the estimated user's emotions. For example, when the user is feeling stressed, the evaluation unit prioritizes evaluation of expense items with high importance. For example, when the user is feeling stressed, the evaluation unit prioritizes evaluation of expense items with high importance. The evaluation unit can also evaluate all expense items equally when the user is relaxed. For example, when the user is relaxed, the evaluation unit evaluates all expense items equally. The evaluation unit can also prioritize evaluation of major expense items when the user is in a hurry. For example, when the user is in a hurry, the evaluation unit prioritizes evaluation of major expense items. This enables efficient evaluation by determining the priority of the household account book evaluation according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotional data into the AI ​​and have the AI ​​determine the priority of the evaluation.

[0103] During evaluation, the evaluation unit can determine the priority of evaluation based on the submission date of the expenditure item. For example, the evaluation unit prioritizes evaluation of recently submitted expenditure items. For example, the evaluation unit prioritizes evaluation of recently submitted expenditure items. The evaluation unit can also postpone evaluation of expenditure items that have been submitted recently. For example, the evaluation unit postpones evaluation of expenditure items that have been submitted recently. The evaluation unit can also prioritize evaluation of expenditure items with high importance based on the submission date. For example, the evaluation unit prioritizes evaluation of expenditure items with high importance based on the submission date. This enables efficient evaluation by determining the priority of evaluation based on the submission date of the expenditure item. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the submission date of the expenditure item into AI and have the AI ​​determine the priority of evaluation.

[0104] During evaluation, the evaluation unit can adjust the order of evaluation based on the relevance of the expenditure items. For example, the evaluation unit prioritizes evaluation of highly relevant expenditure items. For example, the evaluation unit prioritizes evaluation of highly relevant expenditure items. The evaluation unit can also postpone less relevant expenditure items. For example, the evaluation unit postpones less relevant expenditure items. The evaluation unit can also adjust the order of evaluation based on the relevance of the expenditure items. For example, the evaluation unit adjusts the order of evaluation based on the relevance of the expenditure items. This enables efficient evaluation by adjusting the order of evaluation based on the relevance of the expenditure items. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input relevance data of expenditure items into AI and have the AI ​​adjust the order of evaluation.

[0105] During evaluation, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's level of expertise. For example, if the user has technical expertise, the evaluation unit uses detailed technical terminology in the evaluation. For example, if the user has technical expertise, the evaluation unit uses detailed technical terminology in the evaluation. The evaluation unit can also use simple terminology in the evaluation if the user does not have technical expertise. For example, if the user does not have technical expertise, the evaluation unit uses simple terminology in the evaluation. The evaluation unit can also adjust the use of technical terminology in the evaluation according to the user's level of expertise. This enables efficient evaluation by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without AI. For example, the evaluation unit can input the user's level of expertise data into AI and have the AI ​​adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, reflection unit, and evaluation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can automatically acquire payment history and purchase history using an API of the smart device 14. The analysis unit analyzes the history data acquired by the specific processing unit 290 of the data processing device 12 and classifies the history data by expenditure item. The reflection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that automatically reflects the analyzed data in the household account book. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that evaluates the household account book and provides feedback to the user. Furthermore, the acquisition unit is realized by the control unit 46A of the smart device 14 as a processing unit that estimates the user's emotions and adjusts the acquisition timing based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements including the above-described acquisition unit, analysis unit, reflection unit, and evaluation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can automatically acquire payment history and purchase history using an API of the smart glasses 214. The analysis unit analyzes the history data acquired by the specific processing unit 290 of the data processing device 12 and classifies the history data by expenditure item. The reflection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that automatically reflects the analyzed data in the household account book. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that evaluates the household account book and provides feedback to the user. Furthermore, the acquisition unit is realized by the control unit 46A of the smart glasses 214 as a processing unit that estimates the user's emotions and adjusts the acquisition timing based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, analysis unit, reflection unit, and evaluation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit can automatically acquire payment history and purchase history using an API of the headset type terminal 314. The analysis unit analyzes the history data acquired by the specific processing unit 290 of the data processing device 12 and classifies the history data by expenditure item. The reflection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that automatically reflects the analyzed data in the household account book. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that evaluates the household account book and provides feedback to the user. Furthermore, the acquisition unit is realized by the control unit 46A of the headset type terminal 314 as a processing unit that estimates the user's emotions and adjusts the acquisition timing based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, reflection unit, and evaluation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can automatically acquire payment history and purchase history using the API of the robot 414. The analysis unit analyzes the history data acquired by the specific processing unit 290 of the data processing device 12 and classifies the history data by expenditure item. The reflection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that automatically reflects the analyzed data in the household account book. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that evaluates the household account book and provides feedback to the user. Furthermore, the acquisition unit is realized by the control unit 46A of the robot 414 as a processing unit that estimates the user's emotions and adjusts the acquisition timing based on the estimated emotions.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When acquiring a user's payment history or purchase history, the acquisition unit can adjust the acquisition timing taking into account the user's current health condition. For example, if the user is in poor health, the acquisition unit can delay acquisition of the payment history or purchase history and acquire it when the user returns to a healthy state. The acquisition unit can also prioritize acquisition of health-related expenditure history immediately after the user undergoes a health checkup. This makes it possible to acquire data according to the user's health condition, thereby reducing the burden on the user.

[0108] When analyzing a user's spending history, the analysis unit can adjust the analysis algorithm taking into account the user's past spending patterns. For example, if the user has made large expenditures in a particular month in the past, the analysis unit can analyze the expenditures for that month in detail. Also, if the user has made frequent expenditures in a particular category in the past, the analysis unit can prioritize the analysis of expenditures in that category. This enables efficient analysis based on the user's past spending patterns.

[0109] When evaluating the user's spending situation, the evaluation unit can adjust the evaluation criteria taking into account the user's life events. For example, if the user experiences a life event such as marriage or childbirth, the expenses related to that event can be evaluated specially. Also, if the user changes jobs or moves, the expenses during that period can be evaluated specially. This allows for flexible evaluation according to the user's life events.

[0110] When acquiring a user's payment history or purchase history, the acquisition unit can adjust the acquisition timing taking into account the user's current activity level. For example, if the user is exercising, the acquisition unit can delay acquisition of the payment history or purchase history and acquire it while the user is resting. Also, if the user is at work, the acquisition unit can postpone acquisition of the payment history or purchase history and acquire it after the user finishes work. This makes it possible to acquire data according to the user's activity level, reducing the burden on the user.

[0111] When analyzing a user's spending history, the analysis unit can adjust the analysis algorithm taking into account the user's hobbies and preferences. For example, if a user spends a lot on a particular hobby, the analysis unit can analyze spending related to that hobby in detail. Also, if a user prefers a particular brand or store, the analysis unit can prioritize spending on that brand or store. This enables efficient analysis based on the user's hobbies and preferences.

[0112] When evaluating the user's spending situation, the evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit can use simple evaluation criteria and avoid detailed evaluation. On the other hand, if the user is relaxed, the evaluation unit can use detailed evaluation criteria and evaluate detailed items. This allows for flexible evaluation according to the user's emotions.

[0113] When acquiring a user's payment history or purchase history, the acquisition unit can adjust the acquisition timing taking into account the user's current geographical location information. For example, if the user is traveling, the acquisition unit can prioritize acquiring payment history or purchase history from the travel destination. Also, if the user is at home, the acquisition unit can prioritize acquiring payment history or purchase history from around the home. This enables efficient data acquisition based on the user's geographical location information.

[0114] When analyzing a user's spending history, the analysis unit can adjust the analysis algorithm taking into account the user's past feedback. For example, the analysis unit can adjust the classification method for spending items based on the user's past feedback. Also, if the user provides feedback to analyze spending in a specific category in more detail, the analysis unit can prioritize the analysis of spending in that category. This enables efficient analysis based on the user's past feedback.

[0115] When evaluating the user's spending situation, the evaluation unit can estimate the user's emotions and determine the priority of the evaluation based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit can prioritize the evaluation of expenditure items with high importance. Also, if the user is relaxed, the evaluation unit can evaluate all expenditure items equally. This enables efficient evaluation according to the user's emotions.

[0116] When acquiring a user's payment history or purchase history, the acquisition unit can adjust the acquisition timing taking into account the user's social media activity. For example, the acquisition unit can prioritize acquisition of payment history or purchase history for locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's posts on social media to acquire related payment history or purchase history. This enables efficient data acquisition based on the user's social media activity.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The acquisition unit automatically acquires the user's payment history or purchase history. Payment history includes credit card payments, bank transfers, cash payments, etc., while purchase history includes online shopping history, in-store purchase history, etc. The acquisition unit can acquire data automatically using an API, or it can acquire data manually entered by the user. Step 2: The analysis unit analyzes the historical data acquired by the acquisition unit and classifies it by expenditure item. The analysis is performed using data mining technology, statistical analysis methods, and AI, and classification is based on categories such as food expenses, transportation expenses, and entertainment expenses. Step 3: The reflection unit automatically reflects the data analyzed by the analysis unit in the household ledger. The household ledger includes income and expenditure items, monthly totals, etc., and the reflection unit automatically reflects the analyzed data and also creates graphs of history and assets. For example, monthly expenditure and income amounts can be displayed in bar graphs or line graphs. Step 4: The evaluation unit evaluates the household account book reflected by the reflection unit and provides feedback to the user. The evaluation is based on the efficiency of spending and the degree of budget achievement, and AI can also be used to evaluate the household account book and provide feedback to the user.

[0119] 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.

[0120] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. an acquisition unit that automatically acquires a user's payment history or purchase history; an analysis unit that analyzes the history data acquired by the acquisition unit and classifies the data by expenditure item; a reflection unit that automatically reflects the data analyzed by the analysis unit in a household account book; An evaluation unit evaluates the household account book reflected by the reflection unit and provides feedback to the user. A system characterized by:

2. The evaluation unit Evaluate your spending or budget achievements and provide suggestions for improvement or recommendations 2. The system of claim 1.

3. The acquisition unit Providing information about point redemption 2. The system of claim 1.

4. The analysis unit Categorize each expense item 2. The system of claim 1.

5. The reflection unit Graph history or assets 2. The system of claim 1.

6. The evaluation unit Rate the household budget and provide feedback to users 2. The system of claim 1.

7. The acquisition unit Inferring user sentiment and adjusting the timing of retrieving payment or purchase history based on the estimated user sentiment 2. The system of claim 1.

8. The acquisition unit Analyze the user's past payment or purchase history and select the appropriate acquisition method 2. The system of claim 1.

Images