system

Abstract

The system according to the embodiment aims to provide optimal saving advice tailored to individual needs based on the user's usage history. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, and a reception unit. The collection unit collects a user's usage history. The analysis unit analyzes the data collected by the collection unit. The generation unit generates saving advice based on the data analyzed by the analysis unit. The provision unit provides the advice generated by the generation unit. The reception unit interactively receives user requests.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that money-saving advice based on a user's usage history is one-sided, making it difficult to provide optimal suggestions that meet individual needs.

[0005] The system according to the embodiment aims to provide optimal saving advice tailored to individual needs based on the user's usage history. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, and a reception unit. The collection unit collects a user's usage history. The analysis unit analyzes the data collected by the collection unit. The generation unit generates saving advice based on the data analyzed by the analysis unit. The provision unit provides the advice generated by the generation unit. The reception unit interactively receives a user's request. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal saving advice according to individual needs based on the user's usage history. [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 electronic payment system according to an embodiment of the present invention uses AI to provide savings advice based on a user's usage history. This system collects the user's usage history and analyzes it with AI to generate specific savings advice. Furthermore, the system interactively incorporates the user's requests and proposes personalized recommendations. It also analyzes the payment information of the electronic payment card to provide detailed advice. For example, it suggests how much savings you can achieve by switching to a gold card. First, the user's usage history is collected and analyzed by AI. For example, it collects daily shopping and restaurant payment history. Next, the AI ​​generates savings advice based on the collected data. For example, it provides specific advice such as "cut back on eating out once a week." Furthermore, it can interactively incorporate user requests. For example, if a user inputs "I want to eat snacks once every two days," the AI ​​takes that request into account and suggests ways to save money in other areas. This allows the system to provide the optimal savings plan for the user. It also analyzes the payment information of the electronic payment card to provide more detailed advice. For example, it suggests how much savings you can achieve by switching to a gold card based on the user's usage history. This allows the user to optimize their spending and maximize their savings. This system analyzes the user's usage history and interactively incorporates their requests to provide personalized savings advice. It also analyzes payment information from electronic payment cards to provide more detailed advice and optimize the user's spending. This allows the electronic payment system to provide personalized savings advice based on the user's usage history.

[0029] The electronic payment system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, and a reception unit. The collection unit collects a user's usage history. The user's usage history may include, but is not limited to, daily shopping and restaurant payment histories. The collection unit may collect, for example, credit card payment histories and cash payment histories. The collection unit may also collect payment information for electronic payment cards. The analysis unit analyzes the data collected by the collection unit. The analysis may be performed using, for example, data mining, statistical analysis, machine learning algorithms, or the like, but is not limited to these examples. The analysis unit may analyze a user's spending patterns using, for example, data mining techniques. The analysis unit may also analyze a user's spending trends using statistical analysis techniques. The analysis unit may also predict a user's spending using a machine learning algorithm. The generation unit generates saving advice based on the data analyzed by the analysis unit. The saving advice may include, for example, specific action suggestions for saving money and simulations of saving effects, but is not limited to these examples. The generation unit generates specific advice, such as reducing eating out once a week. The generation unit can also simulate the savings effect and present it to the user. The provision unit provides the advice generated by the generation unit. The provision can be performed, for example, by email, app notification, dashboard display, or other methods, but is not limited to these examples. The provision unit, for example, sends the savings advice to the user by email. The provision unit can also provide the advice through app notification. The provision unit can also display the advice on a dashboard. The reception unit interactively accepts the user's request. For example, the reception unit can input a request such as "I want to eat a snack once every two days." After accepting the user's request, the reception unit transmits the request to the generation unit. As a result, the generation unit can propose ways to save money in other areas while taking the user's request into consideration. As a result, the electronic payment system according to the embodiment can provide savings advice that is individually optimized based on the user's usage history.

[0030] The collection unit can collect shopping and restaurant payment histories. The collection unit, for example, collects credit card payment histories. For example, the collection unit acquires credit card usage details and collects payment histories. The collection unit can also collect cash payment histories. For example, the collection unit analyzes receipt images and collects cash payment histories. The collection unit can also collect payment information for electronic payment cards. For example, the collection unit acquires electronic payment card usage histories and collects payment information. By collecting daily payment histories, more detailed money-saving advice can be provided. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input credit card usage details into AI and have the AI ​​collect the payment history.

[0031] The analysis unit can analyze the collected data and generate specific advice for saving money. The analysis unit can analyze the collected data using, for example, data mining technology. For example, the analysis unit can analyze a user's spending patterns and generate specific advice for saving money. The analysis unit can also analyze the collected data using statistical analysis technology. For example, the analysis unit can analyze a user's spending trends and generate specific advice for saving money. The analysis unit can also analyze the collected data using a machine learning algorithm. For example, the analysis unit can predict a user's spending and generate specific advice for saving money. In this way, specific advice for saving money can be provided by analyzing the collected data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0032] The reception unit can receive user requests. For example, the reception unit can input a user request such as "I want to eat a snack once every two days." For example, the reception unit provides an interface through which the user inputs the request through an app. The reception unit can also receive user requests using voice input. For example, the reception unit provides an interface through which the user can input the request by voice. The reception unit can also receive user requests using a chatbot. For example, the reception unit provides an interface through which the user inputs the request through a chatbot. By receiving the user request, individually optimized advice can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user request to AI and have the AI ​​receive the request.

[0033] The providing unit can provide advice according to the user's request. The providing unit, for example, provides specific advice for saving money according to the user's request. For example, the providing unit considers a user's request such as "I want to eat a snack once every two days" and suggests ways to save money in other areas. The providing unit can also simulate savings effects based on the user's request and present the results to the user. For example, the providing unit can provide specific advice such as reducing eating out once a week according to the user's request. The providing unit can also simulate savings effects based on the user's request and present the results to the user. This enables more effective savings by providing advice according to the user's request. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's request into AI and have the AI ​​provide the advice.

[0034] The analysis unit can analyze payment information of an electronic payment card. The analysis unit, for example, analyzes the usage history of the electronic payment card. For example, the analysis unit obtains details of the electronic payment card and analyzes the payment information. The analysis unit can also analyze a user's spending patterns based on the usage history of the electronic payment card. For example, the analysis unit can analyze a user's spending tendencies based on the usage history of the electronic payment card. The analysis unit can also predict a user's spending based on the usage history of the electronic payment card. This enables more detailed saving advice to be provided by analyzing the payment information of the electronic payment card. Some or all of the above-described 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 the usage history of the electronic payment card into AI and have the AI ​​analyze the payment information.

[0035] The provision unit can suggest the savings effect of switching to a gold card. For example, the provision unit suggests how much savings can be achieved by switching to a gold card. For example, the provision unit considers the annual membership fee of the gold card, the point redemption rate, the benefits, etc., and presents the savings effect for the user. The provision unit can also explain to the user the benefits and advantages that can be obtained by switching to a gold card. For example, the provision unit explains the benefits of the gold card in detail and presents the benefits for the user. This allows the user to maximize the savings effect by switching to a gold card. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the benefits of the gold card into AI and have the AI ​​execute the savings effect suggestion.

[0036] The collection unit can analyze the user's past usage history and select the optimal collection method. For example, the collection unit prioritizes collecting history from stores frequently used by the user. For example, the collection unit analyzes the user's past usage history and prioritizes collecting payment history from stores frequently used. The collection unit can also focus on collecting history from the user's usage during a specific time period. For example, the collection unit analyzes the user's past usage patterns and prioritizes collecting payment history made during a specific time period. The collection unit can also analyze the user's past usage patterns and select the most efficient collection method. For example, the collection unit selects the optimal collection method based on the user's past usage patterns. In this way, the optimal data collection method can be selected by analyzing the past usage history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past usage history into AI and have the AI ​​select the optimal collection method.

[0037] When collecting usage history, the collection unit can filter the usage history based on the user's lifestyle and areas of interest. For example, if the user is health-conscious, the collection unit prioritizes collecting health-related payment history. For example, the collection unit analyzes the user's lifestyle and prioritizes collecting health-related payment history for the health-conscious user. Furthermore, if the user is traveling, the collection unit can also prioritize collecting travel-related payment history. For example, the collection unit analyzes the user's areas of interest and prioritizes collecting travel-related payment history for the traveling user. Furthermore, if the user has a specific hobby, the collection unit can prioritize collecting payment history related to the hobby. For example, the collection unit analyzes the user's hobby and prioritizes collecting payment history related to the hobby. This allows for filtering data based on the user's lifestyle and areas of interest, thereby collecting more relevant data. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's lifestyle and areas of interest into AI and have the AI ​​perform data filtering.

[0038] When collecting usage history, the collection unit can prioritize collecting relevant history based on the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting payment history for that area. For example, the collection unit acquires the user's geographical location information and prioritizes collecting payment history for that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting payment history for the travel destination. For example, the collection unit prioritizes collecting payment history for the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the collection unit can prioritize collecting payment history for nearby stores. For example, the collection unit prioritizes collecting payment history for nearby stores based on the user's geographical location information. In this way, by taking geographical location information into consideration, highly relevant data can be prioritized. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to collect related history.

[0039] When collecting usage history, the collection unit can analyze the user's social media activity and collect related history. For example, the collection unit prioritizes collecting payment history of stores mentioned by the user on social media. For example, the collection unit analyzes the user's social media activity and prioritizes collecting payment history of the mentioned stores. The collection unit can also focus on collecting payment history related to events shared by the user on social media. For example, the collection unit analyzes the user's social media activity and prioritizes collecting payment history related to the shared event. The collection unit can also analyze the content of the user's social media posts and collect related payment history. For example, the collection unit analyzes the content of the user's social media posts and collects related payment history. In this way, highly relevant data can be collected by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI and cause the AI ​​to collect related history.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important payment histories. For example, the analysis unit analyzes the user's payment history and performs a detailed analysis on the important payment histories. The analysis unit can also perform a brief analysis on general payment histories. For example, the analysis unit analyzes the user's payment history and performs a brief analysis on the general payment histories. The analysis unit can also perform a detailed analysis according to the category on payment histories belonging to a specific category. For example, the analysis unit analyzes the user's payment history and performs a detailed analysis according to the category on payment histories belonging to a specific category. This enables more effective analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 payment history into AI and have the AI ​​adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a dining-related analysis algorithm to a payment history at a restaurant. For example, the analysis unit analyzes the user's payment history at a restaurant and applies a dining-related analysis algorithm. The analysis unit can also apply a shopping-related analysis algorithm to everyday shopping. For example, the analysis unit analyzes the user's payment history for everyday shopping and applies a shopping-related analysis algorithm. The analysis unit can also apply a travel-related analysis algorithm to a travel-related payment history. For example, the analysis unit analyzes the user's travel-related payment history and applies a travel-related analysis algorithm. This enables more accurate analysis by applying different analysis algorithms depending on the data category. 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 the user's payment history into AI and have the AI ​​apply the analysis algorithm.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of recent payment histories. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of recent payment histories. The analysis unit can also focus on analyzing payment histories made in a specific period. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of payment histories made in a specific period. The analysis unit can also prioritize analysis of payment histories made in connection with a specific event. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of payment histories made in connection with a specific event. This enables more effective analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-described 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 payment history into AI and have the AI ​​determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of data. For example, the analysis unit prioritizes analysis of highly relevant payment histories. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of highly relevant payment histories. The analysis unit can also analyze payment histories belonging to a specific category collectively. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of payment histories belonging to a specific category collectively. The analysis unit can also prioritize analysis of highly relevant data based on the user's past usage patterns. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of highly relevant data based on the past usage patterns. This enables more effective analysis by adjusting the order of analysis based on the relevance of data. Some or all of the above-described 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 payment history into AI and have the AI ​​adjust the order of analysis.

[0044] When generating advice, the generation unit can adjust the level of detail of the advice based on the importance of the data. The generation unit, for example, provides detailed advice for important payment history. For example, the generation unit analyzes the user's payment history and provides detailed advice for important payment history. The generation unit can also provide concise advice for general payment history. For example, the generation unit analyzes the user's payment history and provides concise advice for general payment history. The generation unit can also provide detailed advice according to the category for payment history belonging to a specific category. For example, the generation unit analyzes the user's payment history and provides detailed advice according to the category for payment history belonging to a specific category. This enables more effective advice by adjusting the level of detail of the advice based on the importance of the data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's payment history into AI and cause the AI ​​to adjust the level of detail of the advice.

[0045] When generating advice, the generation unit can apply different advice generation algorithms depending on the category of data. For example, the generation unit applies a dining-related advice generation algorithm to a payment history at a restaurant. For example, the generation unit analyzes the user's payment history at restaurants and applies a dining-related advice generation algorithm. The generation unit can also apply a shopping-related advice generation algorithm to everyday shopping. For example, the generation unit analyzes the user's payment history for everyday shopping and applies a shopping-related advice generation algorithm. The generation unit can also apply a travel-related advice generation algorithm to a travel-related payment history. For example, the generation unit analyzes the user's travel-related payment history and applies a travel-related advice generation algorithm. This allows for more accurate advice to be provided by applying different advice generation algorithms depending on the category of data. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's payment history into AI and cause the AI ​​to apply the advice generation algorithm.

[0046] When generating advice, the generation unit can determine the priority of advice based on the time of data submission. The generation unit provides advice based on, for example, recent payment history. For example, the generation unit analyzes the user's payment history and provides advice based on the recent payment history. The generation unit can also provide advice based on payment history concentrated in a specific period. For example, the generation unit analyzes the user's payment history and provides advice based on the payment history concentrated in a specific period. The generation unit can also provide advice based on payment history made by the user in connection with a specific event. For example, the generation unit analyzes the user's payment history and provides advice based on the payment history made in connection with a specific event. This enables more effective advice by determining the priority of advice based on the time of data submission. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's payment history into AI and have the AI ​​determine the priority of advice.

[0047] When generating advice, the generation unit can adjust the order of advice based on the relevance of data. The generation unit provides advice based on, for example, highly relevant payment history. For example, the generation unit analyzes the user's payment history and provides advice based on highly relevant payment history. The generation unit can also provide advice based on payment history belonging to a specific category. For example, the generation unit analyzes the user's payment history and provides advice based on payment history belonging to a specific category. The generation unit can also provide advice based on highly relevant data based on the user's past usage patterns. For example, the generation unit analyzes the user's payment history and provides advice based on highly relevant data based on the past usage patterns. This enables more effective advice by adjusting the order of advice based on the relevance of data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's payment history into AI and cause the AI ​​to adjust the order of advice.

[0048] When providing advice, the providing unit can analyze the user's past responses and select the optimal providing method. The providing unit, for example, prioritizes providing a form of advice that the user has previously preferred. For example, the providing unit analyzes the user's past responses and prioritizes providing a form of advice that the user has previously preferred. The providing unit can also provide advice by referring to the content of advice to which the user has previously responded well. For example, the providing unit analyzes the user's past responses and provides advice by referring to the content of advice to which the user has previously responded well. The providing unit can also analyze the user's past responses and select the most effective providing method. For example, the providing unit analyzes the user's past responses and selects the most effective providing method. In this way, the optimal advice providing method can be selected by analyzing the user's past responses. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past responses to AI and cause the AI ​​to select the optimal providing method.

[0049] The providing unit can customize the providing means based on the user's current living situation when providing advice. For example, if the user is busy, the providing unit provides short, to-the-point advice. For example, the providing unit analyzes the user's living situation and provides short, to-the-point advice to a busy user. The providing unit can also provide detailed advice if the user is relaxed. For example, the providing unit analyzes the user's living situation and provides detailed advice to a relaxed user. The providing unit can also provide money-saving advice at the travel destination if the user is traveling. For example, the providing unit analyzes the user's living situation and provides money-saving advice at the travel destination to a user who is traveling. This allows more appropriate advice to be provided by customizing the providing means based on the user's living situation. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's living situation into AI and have the AI ​​customize the providing means.

[0050] When providing advice, the providing unit can select an appropriate provision method based on the user's geographical location information. For example, when the user is in a specific area, the providing unit provides money-saving advice for that area. For example, the providing unit acquires the user's geographical location information and provides money-saving advice for that area. Furthermore, when the user is traveling, the providing unit can also provide money-saving advice at the travel destination. For example, the providing unit provides money-saving advice at the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the providing unit can also provide money-saving advice for nearby stores. For example, the providing unit provides money-saving advice for nearby stores based on the user's geographical location information. In this way, the optimal advice provision method can be selected by taking the geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI and cause the AI ​​to select the provision method.

[0051] When providing advice, the providing unit can analyze the user's social media activity and suggest a means of providing the advice. The providing unit, for example, provides money-saving advice for stores mentioned by the user on social media. For example, the providing unit analyzes the user's social media activity and provides money-saving advice for the mentioned stores. The providing unit can also provide money-saving advice related to events shared by the user on social media. For example, the providing unit analyzes the user's social media activity and provides money-saving advice related to the shared event. The providing unit can also analyze the content of the user's social media posts and provide related money-saving advice. For example, the providing unit analyzes the content of the user's social media posts and provides related money-saving advice. In this way, highly relevant advice can be provided by analyzing social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI and cause the AI ​​to suggest a means of providing the advice.

[0052] When receiving a request, the reception unit can select the optimal reception method by referring to the user's past request history. For example, the reception unit automatically displays requests that the user frequently input in the past as candidates. For example, the reception unit analyzes the user's past request history and automatically displays frequently input requests as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit analyzes the user's past request history and preferentially suggests the input method that was used. The reception unit can also predict and suggest requests to be used in a specific time period from the user's past request history. For example, the reception unit analyzes the user's past request history and predicts and suggests requests to be used in a specific time period. In this way, the optimal reception method can be selected by referring to the past request history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past request history into AI and have the AI ​​select the optimal reception method.

[0053] The reception unit can customize the reception means based on the user's current living situation when receiving a request. For example, if the user is busy, the reception unit provides a short and to-the-point request reception method. For example, the reception unit analyzes the user's living situation and provides a short and to-the-point request reception method for the busy user. The reception unit can also provide a detailed request reception method for the relaxed user when the user is relaxed. For example, the reception unit analyzes the user's living situation and provides a detailed request reception method for the relaxed user when the user is traveling. For example, the reception unit analyzes the user's living situation and provides a request reception method for the travel destination for the traveling user. This allows for more appropriate request reception by customizing the reception means based on the user's living situation. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's living situation into AI and have the AI ​​customize the reception means.

[0054] When receiving a request, the reception unit can select the optimal reception method by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving requests from that area. For example, the reception unit acquires the user's geographical location information and prioritizes receiving requests from that area. Furthermore, if the user is traveling, the reception unit can also prioritize receiving requests from the travel destination. For example, the reception unit prioritizes receiving requests from the travel destination based on the user's geographical location information. Furthermore, if the user is at home, the reception unit can also prioritize receiving requests from nearby stores. For example, the reception unit prioritizes receiving requests from nearby stores based on the user's geographical location information. In this way, the optimal reception method can be selected by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​select the reception method.

[0055] When receiving a request, the reception unit can analyze the user's social media activity and suggest a reception means. For example, the reception unit prioritizes receiving requests mentioned by the user on social media. For example, the reception unit analyzes the user's social media activity and prioritizes receiving requests mentioned by the user. The reception unit can also prioritize receiving requests related to events shared by the user on social media. For example, the reception unit analyzes the user's social media activity and prioritizes receiving requests related to the shared event. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related requests. For example, the reception unit analyzes the content of the user's social media posts and prioritizes receiving related requests. In this way, by analyzing the social media activity, it is possible to receive highly relevant requests. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI and have the AI ​​execute the suggestion of a reception means.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can analyze the user's past usage history and select the optimal collection method. For example, the collection unit prioritizes collection of history from stores that the user frequently visits. The collection unit analyzes the user's past usage history and prioritizes collection of payment history from stores that the user frequently visits. The collection unit can also focus on collecting history from the user's usage during a specific time period. The collection unit analyzes the user's past usage patterns and prioritizes collection of payment history made during a specific time period. The collection unit can also analyze the user's past usage patterns and select the most efficient collection method. The collection unit selects the optimal collection method based on the user's past usage patterns. In this way, the optimal data collection method can be selected by analyzing the past usage history.

[0058] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis is performed on important payment histories. The analysis unit analyzes the user's payment history and performs a detailed analysis on important payment histories. A brief analysis can also be performed on general payment histories. The analysis unit analyzes the user's payment history and performs a brief analysis on general payment histories. Furthermore, a detailed analysis according to the category can be performed on payment histories belonging to a specific category. The analysis unit analyzes the user's payment history and performs a detailed analysis according to the category on payment histories belonging to a specific category. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0059] When providing advice, the providing unit can analyze the user's past reactions and select the optimal method of providing advice. For example, the providing unit can prioritize providing the form of advice that the user has preferred in the past. The providing unit can also provide advice by referring to the content of advice that the user has responded well to in the past. The providing unit can analyze the user's past reactions and provide advice by referring to the content of advice that has responded well to in the past. Furthermore, the providing unit can analyze the user's past reactions and select the most effective method of providing advice. The providing unit analyzes the user's past reactions and selects the most effective method of providing advice. In this way, the optimal method of providing advice can be selected by analyzing the user's past reactions.

[0060] When receiving a request, the reception unit can select the optimal reception method by referring to the user's past request history. For example, requests that the user has frequently input in the past are automatically displayed as candidates. The reception unit analyzes the user's past request history and automatically displays frequently input requests as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit analyzes the user's past request history and preferentially suggests input methods that have been used. Furthermore, the reception unit can predict and suggest requests that will be used in a specific time period from the user's past request history. The reception unit analyzes the user's past request history and predicts and suggests requests that will be used in a specific time period. In this way, the optimal reception method can be selected by referring to the past request history.

[0061] When providing advice, the providing unit can select an appropriate method of providing advice based on the user's geographical location information. For example, if the user is in a specific area, the providing unit provides money-saving advice for that area. The providing unit acquires the user's geographical location information and provides money-saving advice for that area. Furthermore, if the user is traveling, the providing unit can also provide money-saving advice for the travel destination based on the user's geographical location information. Furthermore, if the user is at home, the providing unit can also provide money-saving advice for nearby stores. The providing unit provides money-saving advice for nearby stores based on the user's geographical location information. In this way, the optimal method of providing advice can be selected by taking geographical location information into consideration.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit collects the user's usage history, which may include, for example, daily shopping and restaurant payment history, credit card payment history, cash payment history, and electronic payment card payment information. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms to identify user spending patterns, spending trends, and spending predictions. Step 3: The generator generates energy saving advice based on the data analyzed by the analyzer. The energy saving advice includes specific action suggestions and a simulation of the energy saving effect. Step 4: The providing unit provides the advice generated by the generating unit. The providing method may include email, app notification, dashboard display, etc. Step 5: The reception unit interactively receives a user request. For example, the user inputs a request such as "I want to eat a snack once every two days," and the reception unit transmits the request to the generation unit.

[0064] (Example 2) An electronic payment system according to an embodiment of the present invention uses AI to provide savings advice based on a user's usage history. This system collects the user's usage history and analyzes it with AI to generate specific savings advice. Furthermore, the system interactively incorporates the user's requests and proposes personalized recommendations. It also analyzes the payment information of the electronic payment card to provide detailed advice. For example, it suggests how much savings you can achieve by switching to a gold card. First, the user's usage history is collected and analyzed by AI. For example, it collects daily shopping and restaurant payment history. Next, the AI ​​generates savings advice based on the collected data. For example, it provides specific advice such as "cut back on eating out once a week." Furthermore, it can interactively incorporate user requests. For example, if a user inputs "I want to eat snacks once every two days," the AI ​​takes that request into account and suggests ways to save money in other areas. This allows the system to provide the optimal savings plan for the user. It also analyzes the payment information of the electronic payment card to provide more detailed advice. For example, it suggests how much savings you can achieve by switching to a gold card based on the user's usage history. This allows the user to optimize their spending and maximize their savings. This system analyzes the user's usage history and interactively incorporates their requests to provide personalized savings advice. It also analyzes payment information from electronic payment cards to provide more detailed advice and optimize the user's spending. This allows the electronic payment system to provide personalized savings advice based on the user's usage history.

[0065] The electronic payment system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a provision unit, and a reception unit. The collection unit collects a user's usage history. The user's usage history may include, but is not limited to, daily shopping and restaurant payment histories. The collection unit may collect, for example, credit card payment histories and cash payment histories. The collection unit may also collect payment information for electronic payment cards. The analysis unit analyzes the data collected by the collection unit. The analysis may be performed using, for example, data mining, statistical analysis, machine learning algorithms, or the like, but is not limited to these examples. The analysis unit may analyze a user's spending patterns using, for example, data mining techniques. The analysis unit may also analyze a user's spending trends using statistical analysis techniques. The analysis unit may also predict a user's spending using a machine learning algorithm. The generation unit generates saving advice based on the data analyzed by the analysis unit. The saving advice may include, for example, specific action suggestions for saving money and simulations of saving effects, but is not limited to these examples. The generation unit generates specific advice, such as reducing eating out once a week. The generation unit can also simulate the savings effect and present it to the user. The provision unit provides the advice generated by the generation unit. The provision can be performed, for example, by email, app notification, dashboard display, or other methods, but is not limited to these examples. The provision unit, for example, sends the savings advice to the user by email. The provision unit can also provide the advice through app notification. The provision unit can also display the advice on a dashboard. The reception unit interactively accepts the user's request. For example, the reception unit can input a request such as "I want to eat a snack once every two days." After accepting the user's request, the reception unit transmits the request to the generation unit. As a result, the generation unit can propose ways to save money in other areas while taking the user's request into consideration. As a result, the electronic payment system according to the embodiment can provide savings advice that is individually optimized based on the user's usage history.

[0066] The collection unit can collect shopping and restaurant payment histories. The collection unit, for example, collects credit card payment histories. For example, the collection unit acquires credit card usage details and collects payment histories. The collection unit can also collect cash payment histories. For example, the collection unit analyzes receipt images and collects cash payment histories. The collection unit can also collect payment information for electronic payment cards. For example, the collection unit acquires electronic payment card usage histories and collects payment information. By collecting daily payment histories, more detailed money-saving advice can be provided. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input credit card usage details into AI and have the AI ​​collect the payment history.

[0067] The analysis unit can analyze the collected data and generate specific advice for saving money. The analysis unit can analyze the collected data using, for example, data mining technology. For example, the analysis unit can analyze a user's spending patterns and generate specific advice for saving money. The analysis unit can also analyze the collected data using statistical analysis technology. For example, the analysis unit can analyze a user's spending trends and generate specific advice for saving money. The analysis unit can also analyze the collected data using a machine learning algorithm. For example, the analysis unit can predict a user's spending and generate specific advice for saving money. In this way, specific advice for saving money can be provided by analyzing the collected data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0068] The reception unit can receive user requests. For example, the reception unit can input a user request such as "I want to eat a snack once every two days." For example, the reception unit provides an interface through which the user inputs the request through an app. The reception unit can also receive user requests using voice input. For example, the reception unit provides an interface through which the user can input the request by voice. The reception unit can also receive user requests using a chatbot. For example, the reception unit provides an interface through which the user inputs the request through a chatbot. By receiving the user request, individually optimized advice can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user request to AI and have the AI ​​receive the request.

[0069] The providing unit can provide advice according to the user's request. The providing unit, for example, provides specific advice for saving money according to the user's request. For example, the providing unit considers a user's request such as "I want to eat a snack once every two days" and suggests ways to save money in other areas. The providing unit can also simulate savings effects based on the user's request and present the results to the user. For example, the providing unit can provide specific advice such as reducing eating out once a week according to the user's request. The providing unit can also simulate savings effects based on the user's request and present the results to the user. This enables more effective savings by providing advice according to the user's request. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's request into AI and have the AI ​​provide the advice.

[0070] The analysis unit can analyze payment information of an electronic payment card. The analysis unit, for example, analyzes the usage history of the electronic payment card. For example, the analysis unit obtains details of the electronic payment card and analyzes the payment information. The analysis unit can also analyze a user's spending patterns based on the usage history of the electronic payment card. For example, the analysis unit can analyze a user's spending tendencies based on the usage history of the electronic payment card. The analysis unit can also predict a user's spending based on the usage history of the electronic payment card. This enables more detailed saving advice to be provided by analyzing the payment information of the electronic payment card. Some or all of the above-described 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 the usage history of the electronic payment card into AI and have the AI ​​analyze the payment information.

[0071] The provision unit can suggest the savings effect of switching to a gold card. For example, the provision unit suggests how much savings can be achieved by switching to a gold card. For example, the provision unit considers the annual membership fee of the gold card, the point redemption rate, the benefits, etc., and presents the savings effect for the user. The provision unit can also explain to the user the benefits and advantages that can be obtained by switching to a gold card. For example, the provision unit explains the benefits of the gold card in detail and presents the benefits for the user. This allows the user to maximize the savings effect by switching to a gold card. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the benefits of the gold card into AI and have the AI ​​execute the savings effect suggestion.

[0072] The collection unit can estimate the user's emotions and adjust the timing of collecting usage history based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects the usage history when the user is relaxed. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can immediately collect the usage history and perform real-time analysis. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the collection unit can shorten the collection timing and collect data quickly. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's biometric data into the generation AI and have the generation AI estimate the user's emotions.

[0073] The collection unit can analyze the user's past usage history and select the optimal collection method. For example, the collection unit prioritizes collecting history from stores frequently used by the user. For example, the collection unit analyzes the user's past usage history and prioritizes collecting payment history from stores frequently used. The collection unit can also focus on collecting history from the user's usage during a specific time period. For example, the collection unit analyzes the user's past usage patterns and prioritizes collecting payment history made during a specific time period. The collection unit can also analyze the user's past usage patterns and select the most efficient collection method. For example, the collection unit selects the optimal collection method based on the user's past usage patterns. In this way, the optimal data collection method can be selected by analyzing the past usage history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past usage history into AI and have the AI ​​select the optimal collection method.

[0074] When collecting usage history, the collection unit can filter the usage history based on the user's lifestyle and areas of interest. For example, if the user is health-conscious, the collection unit prioritizes collecting health-related payment history. For example, the collection unit analyzes the user's lifestyle and prioritizes collecting health-related payment history for the health-conscious user. Furthermore, if the user is traveling, the collection unit can also prioritize collecting travel-related payment history. For example, the collection unit analyzes the user's areas of interest and prioritizes collecting travel-related payment history for the traveling user. Furthermore, if the user has a specific hobby, the collection unit can prioritize collecting payment history related to the hobby. For example, the collection unit analyzes the user's hobby and prioritizes collecting payment history related to the hobby. This allows for filtering data based on the user's lifestyle and areas of interest, thereby collecting more relevant data. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's lifestyle and areas of interest into AI and have the AI ​​perform data filtering.

[0075] The collection unit can estimate the user's emotions and determine the priority of the usage history to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting payment history related to stress reduction. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can collect the overall payment history in a balanced manner. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting important payment history. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Thus, by determining the priority based on the user's emotions, more important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's biometric data into the generation AI and have the generation AI estimate the user's emotions.

[0076] When collecting usage history, the collection unit can prioritize collecting relevant history based on the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting payment history for that area. For example, the collection unit acquires the user's geographical location information and prioritizes collecting payment history for that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting payment history for the travel destination. For example, the collection unit prioritizes collecting payment history for the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the collection unit can prioritize collecting payment history for nearby stores. For example, the collection unit prioritizes collecting payment history for nearby stores based on the user's geographical location information. In this way, by taking geographical location information into consideration, highly relevant data can be prioritized. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to collect related history.

[0077] When collecting usage history, the collection unit can analyze the user's social media activity and collect related history. For example, the collection unit prioritizes collecting payment history of stores mentioned by the user on social media. For example, the collection unit analyzes the user's social media activity and prioritizes collecting payment history of the mentioned stores. The collection unit can also focus on collecting payment history related to events shared by the user on social media. For example, the collection unit analyzes the user's social media activity and prioritizes collecting payment history related to the shared event. The collection unit can also analyze the content of the user's social media posts and collect related payment history. For example, the collection unit analyzes the content of the user's social media posts and collects related payment history. In this way, highly relevant data can be collected by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI and cause the AI ​​to collect related history.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. The analysis unit can also provide concise analysis results that focus on the main points if the user is in a hurry. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. This allows the analysis unit to adjust the presentation method of the analysis based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 may input the user's biometric data into the generation AI and have the generation AI estimate the user's emotions.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important payment histories. For example, the analysis unit analyzes the user's payment history and performs a detailed analysis on the important payment histories. The analysis unit can also perform a brief analysis on general payment histories. For example, the analysis unit analyzes the user's payment history and performs a brief analysis on the general payment histories. The analysis unit can also perform a detailed analysis according to the category on payment histories belonging to a specific category. For example, the analysis unit analyzes the user's payment history and performs a detailed analysis according to the category on payment histories belonging to a specific category. This enables more effective analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 payment history into AI and have the AI ​​adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a dining-related analysis algorithm to a payment history at a restaurant. For example, the analysis unit analyzes the user's payment history at a restaurant and applies a dining-related analysis algorithm. The analysis unit can also apply a shopping-related analysis algorithm to everyday shopping. For example, the analysis unit analyzes the user's payment history for everyday shopping and applies a shopping-related analysis algorithm. The analysis unit can also apply a travel-related analysis algorithm to a travel-related payment history. For example, the analysis unit analyzes the user's travel-related payment history and applies a travel-related analysis algorithm. This enables more accurate analysis by applying different analysis algorithms depending on the data category. 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 the user's payment history into AI and have the AI ​​apply the analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. The analysis unit can also provide visually stimulating analysis results if the user is excited. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. This allows the analysis unit to adjust the length of the analysis based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 may input the user's biometric data into the generation AI and have the generation AI estimate the user's emotions.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of recent payment histories. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of recent payment histories. The analysis unit can also focus on analyzing payment histories made in a specific period. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of payment histories made in a specific period. The analysis unit can also prioritize analysis of payment histories made in connection with a specific event. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of payment histories made in connection with a specific event. This enables more effective analysis by determining the priority of analysis based on the time of data submission. Some or all of the above-described 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 payment history into AI and have the AI ​​determine the analysis priority.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of data. For example, the analysis unit prioritizes analysis of highly relevant payment histories. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of highly relevant payment histories. The analysis unit can also analyze payment histories belonging to a specific category collectively. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of payment histories belonging to a specific category collectively. The analysis unit can also prioritize analysis of highly relevant data based on the user's past usage patterns. For example, the analysis unit analyzes the user's payment history and prioritizes analysis of highly relevant data based on the past usage patterns. This enables more effective analysis by adjusting the order of analysis based on the relevance of data. Some or all of the above-described 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 payment history into AI and have the AI ​​adjust the order of analysis.

[0084] The generation unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit provides advice that progresses at a leisurely pace. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. If the user is in a hurry, the generation unit can also provide advice that emphasizes the shortest route. For example, the generation unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If the user is excited, the generation unit can also provide advice that adds visually stimulating effects. For example, the generation unit records the user's voice and estimates the user's emotions using voice analysis technology. This allows the system to adjust the way the advice is presented based on the user's emotions, thereby providing more appropriate advice. The emotion estimation is achieved 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 generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's biometric data into the generation AI and cause the generation AI to estimate emotions.

[0085] When generating advice, the generation unit can adjust the level of detail of the advice based on the importance of the data. The generation unit, for example, provides detailed advice for important payment history. For example, the generation unit analyzes the user's payment history and provides detailed advice for important payment history. The generation unit can also provide concise advice for general payment history. For example, the generation unit analyzes the user's payment history and provides concise advice for general payment history. The generation unit can also provide detailed advice according to the category for payment history belonging to a specific category. For example, the generation unit analyzes the user's payment history and provides detailed advice according to the category for payment history belonging to a specific category. This enables more effective advice by adjusting the level of detail of the advice based on the importance of the data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's payment history into AI and cause the AI ​​to adjust the level of detail of the advice.

[0086] When generating advice, the generation unit can apply different advice generation algorithms depending on the category of data. For example, the generation unit applies a dining-related advice generation algorithm to a payment history at a restaurant. For example, the generation unit analyzes the user's payment history at restaurants and applies a dining-related advice generation algorithm. The generation unit can also apply a shopping-related advice generation algorithm to everyday shopping. For example, the generation unit analyzes the user's payment history for everyday shopping and applies a shopping-related advice generation algorithm. The generation unit can also apply a travel-related advice generation algorithm to a travel-related payment history. For example, the generation unit analyzes the user's travel-related payment history and applies a travel-related advice generation algorithm. This allows for more accurate advice to be provided by applying different advice generation algorithms depending on the category of data. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's payment history into AI and cause the AI ​​to apply the advice generation algorithm.

[0087] The generation unit can estimate the user's emotions and adjust the length of advice based on the estimated user emotions. For example, if the user is in a hurry, the generation unit provides short, to-the-point advice. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the generation unit can provide longer advice with detailed explanations. For example, the generation unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is excited, the generation unit can provide advice with visually stimulating effects. For example, the generation unit records the user's voice and estimates the user's emotions using voice analysis technology. This allows the length of advice to be adjusted based on the user's emotions, thereby providing more appropriate advice. Emotion estimation is achieved 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 generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's biometric data into the generation AI and cause the generation AI to estimate emotions.

[0088] When generating advice, the generation unit can determine the priority of advice based on the time of data submission. The generation unit provides advice based on, for example, recent payment history. For example, the generation unit analyzes the user's payment history and provides advice based on the recent payment history. The generation unit can also provide advice based on payment history concentrated in a specific period. For example, the generation unit analyzes the user's payment history and provides advice based on payment history concentrated in a specific period. The generation unit can also provide advice based on payment history made by the user in connection with a specific event. For example, the generation unit analyzes the user's payment history and provides advice based on payment history made in connection with a specific event. This enables more effective advice by determining the priority of advice based on the time of data submission. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's payment history into AI and have the AI ​​determine the priority of advice.

[0089] When generating advice, the generation unit can adjust the order of advice based on the relevance of data. The generation unit provides advice based on, for example, highly relevant payment history. For example, the generation unit analyzes the user's payment history and provides advice based on highly relevant payment history. The generation unit can also provide advice based on payment history belonging to a specific category. For example, the generation unit analyzes the user's payment history and provides advice based on payment history belonging to a specific category. The generation unit can also provide advice based on highly relevant data based on the user's past usage patterns. For example, the generation unit analyzes the user's payment history and provides advice based on highly relevant data based on the past usage patterns. This enables more effective advice by adjusting the order of advice based on the relevance of data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's payment history into AI and cause the AI ​​to adjust the order of advice.

[0090] The providing unit can estimate the user's emotions and adjust the way in which advice is provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible advice. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can provide detailed advice. For example, the providing unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the providing unit can provide concise advice that focuses on the main points. For example, the providing unit can record the user's voice and estimate the user's emotions using voice analysis technology. This allows the system to adjust the way in which advice is provided based on the user's emotions, thereby providing more appropriate advice. Emotion estimation is achieved 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's biometric data into the generating AI and cause the generating AI to estimate emotions.

[0091] When providing advice, the providing unit can analyze the user's past responses and select the optimal providing method. The providing unit, for example, prioritizes providing a form of advice that the user has previously preferred. For example, the providing unit analyzes the user's past responses and prioritizes providing a form of advice that the user has previously preferred. The providing unit can also provide advice by referring to the content of advice to which the user has previously responded well. For example, the providing unit analyzes the user's past responses and provides advice by referring to the content of advice to which the user has previously responded well. The providing unit can also analyze the user's past responses and select the most effective providing method. For example, the providing unit analyzes the user's past responses and selects the most effective providing method. In this way, the optimal advice providing method can be selected by analyzing the user's past responses. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past responses to AI and cause the AI ​​to select the optimal providing method.

[0092] The providing unit can customize the providing means based on the user's current living situation when providing advice. For example, if the user is busy, the providing unit provides short, to-the-point advice. For example, the providing unit analyzes the user's living situation and provides short, to-the-point advice to a busy user. The providing unit can also provide detailed advice if the user is relaxed. For example, the providing unit analyzes the user's living situation and provides detailed advice to a relaxed user. The providing unit can also provide money-saving advice at the travel destination if the user is traveling. For example, the providing unit analyzes the user's living situation and provides money-saving advice at the travel destination to a user who is traveling. This allows more appropriate advice to be provided by customizing the providing means based on the user's living situation. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's living situation into AI and have the AI ​​customize the providing means.

[0093] The providing unit can estimate the user's emotions and prioritize advice based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize advice related to stress reduction. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can provide balanced overall advice. For example, the providing unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the providing unit can prioritize providing important advice. For example, the providing unit can record the user's voice and estimate the user's emotions using voice analysis technology. Thus, by prioritizing advice based on the user's emotions, more important advice can be prioritized. The 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's biometric data into the generating AI and cause the generating AI to estimate emotions.

[0094] When providing advice, the providing unit can select an appropriate provision method based on the user's geographical location information. For example, when the user is in a specific area, the providing unit provides money-saving advice for that area. For example, the providing unit acquires the user's geographical location information and provides money-saving advice for that area. Furthermore, when the user is traveling, the providing unit can also provide money-saving advice at the travel destination. For example, the providing unit provides money-saving advice at the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the providing unit can also provide money-saving advice for nearby stores. For example, the providing unit provides money-saving advice for nearby stores based on the user's geographical location information. In this way, the optimal advice provision method can be selected by taking the geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI and cause the AI ​​to select the provision method.

[0095] When providing advice, the providing unit can analyze the user's social media activity and suggest a means of providing the advice. The providing unit, for example, provides money-saving advice for stores mentioned by the user on social media. For example, the providing unit analyzes the user's social media activity and provides money-saving advice for the mentioned stores. The providing unit can also provide money-saving advice related to events shared by the user on social media. For example, the providing unit analyzes the user's social media activity and provides money-saving advice related to the shared event. The providing unit can also analyze the content of the user's social media posts and provide related money-saving advice. For example, the providing unit analyzes the content of the user's social media posts and provides related money-saving advice. In this way, highly relevant advice can be provided by analyzing social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI and cause the AI ​​to suggest a means of providing the advice.

[0096] The reception unit can estimate the user's emotions and adjust the request reception method based on the estimated user emotions. For example, if the user is nervous, the reception unit provides a simple interface and minimizes input steps. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, the reception unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept requests. For example, the reception unit records the user's voice and estimates the user's emotions using voice analysis technology. This allows the system to adjust the request reception method based on the user's emotions, thereby enabling more appropriate request reception. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's biometric data into the generation AI and cause the generation AI to estimate emotions.

[0097] When receiving a request, the reception unit can select the optimal reception method by referring to the user's past request history. For example, the reception unit automatically displays requests that the user frequently input in the past as candidates. For example, the reception unit analyzes the user's past request history and automatically displays frequently input requests as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit analyzes the user's past request history and preferentially suggests the input method that was used. The reception unit can also predict and suggest requests to be used in a specific time period from the user's past request history. For example, the reception unit analyzes the user's past request history and predicts and suggests requests to be used in a specific time period. In this way, the optimal reception method can be selected by referring to the past request history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past request history into AI and have the AI ​​select the optimal reception method.

[0098] The reception unit can customize the reception means based on the user's current living situation when receiving a request. For example, if the user is busy, the reception unit provides a short and to-the-point request reception method. For example, the reception unit analyzes the user's living situation and provides a short and to-the-point request reception method for the busy user. The reception unit can also provide a detailed request reception method for the relaxed user when the user is relaxed. For example, the reception unit analyzes the user's living situation and provides a detailed request reception method for the relaxed user when the user is traveling. For example, the reception unit analyzes the user's living situation and provides a request reception method for the travel destination for the traveling user. This allows for more appropriate request reception by customizing the reception means based on the user's living situation. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's living situation into AI and have the AI ​​customize the reception means.

[0099] The reception unit can estimate the user's emotions and prioritize requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes requests related to stress reduction. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit can also accept requests in a balanced manner. For example, the reception unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the reception unit can prioritize important requests. For example, the reception unit records the user's voice and estimates the user's emotions using voice analysis technology. Thus, by prioritizing requests based on the user's emotions, more important requests can be prioritized. Emotion estimation is realized using an emotion estimation function, such as 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 reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's biometric data into the generation AI and cause the generation AI to estimate emotions.

[0100] When receiving a request, the reception unit can select the optimal reception method by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving requests from that area. For example, the reception unit acquires the user's geographical location information and prioritizes receiving requests from that area. Furthermore, if the user is traveling, the reception unit can also prioritize receiving requests from the travel destination. For example, the reception unit prioritizes receiving requests from the travel destination based on the user's geographical location information. Furthermore, if the user is at home, the reception unit can also prioritize receiving requests from nearby stores. For example, the reception unit prioritizes receiving requests from nearby stores based on the user's geographical location information. In this way, the optimal reception method can be selected by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​select the reception method.

[0101] When receiving a request, the reception unit can analyze the user's social media activity and suggest a reception means. For example, the reception unit prioritizes receiving requests mentioned by the user on social media. For example, the reception unit analyzes the user's social media activity and prioritizes receiving requests mentioned by the user. The reception unit can also prioritize receiving requests related to events shared by the user on social media. For example, the reception unit analyzes the user's social media activity and prioritizes receiving requests related to the shared event. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related requests. For example, the reception unit analyzes the content of the user's social media posts and prioritizes receiving related requests. In this way, by analyzing the social media activity, it is possible to receive highly relevant requests. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI and have the AI ​​execute the suggestion of a reception means. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, and reception 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 collection unit collects the user's usage history and biometric data using the camera 42 and microphone 38B of the smart device 14, and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates saving advice based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the generated advice to the user. The reception unit, realized, for example, by the control unit 46A of the smart device 14, receives user requests. The collection unit, for example, can estimate the user's emotions and adjust the timing of collecting the usage history based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, and reception unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's usage history and biometric data using the camera 42 and microphone 238 of the smart glasses 214, and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates saving advice based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated advice to the user. The reception unit is realized, for example, by the control unit 46A of the smart glasses 214 and receives user requests. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting the usage history based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, provision unit, and reception 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 collection unit collects the user's usage history and biometric data using the camera 42 and microphone 238 of the headset-type terminal 314, and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates saving advice based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated advice to the user. The reception unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and receives user requests. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting the usage history based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, provision unit, and reception unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's usage history and biometric data using the camera 42 and microphone 238 of the robot 414, and processes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates saving advice based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated advice to the user. The reception unit is realized, for example, by the control unit 46A of the robot 414 and receives user requests. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting the usage history based on the estimated emotions.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, it prioritizes the analysis of payment history related to stress reduction. The analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. In addition, if the user is relaxed, it can analyze the overall payment history in a balanced manner. The analysis unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is in a hurry, it can prioritize the analysis of important payment history. The analysis unit records the user's voice and estimates the emotion using voice analysis technology. In this way, by determining the analysis priority based on the user's emotions, it is possible to prioritize the analysis of more important data.

[0104] The providing unit can estimate the user's emotions and adjust the way in which advice is provided based on the estimated user emotions. For example, if the user is nervous, simple, highly visible advice is provided. The providing unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. It can also provide detailed advice if the user is relaxed. The providing unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, it can provide concise advice that focuses on the main points. The providing unit records the user's voice and estimates the user's emotions using voice analysis technology. This allows the system to provide more appropriate advice by adjusting the way in which advice is provided based on the user's emotions.

[0105] The collection unit can estimate the user's emotions and adjust the timing of collecting usage history based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect the usage history when the user is relaxed. The collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. Also, if the user is relaxed, the collection unit can immediately collect the usage history and perform real-time analysis. The collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the collection timing can be shortened to collect data quickly. The collection unit records the user's voice and estimates the emotion using voice analysis technology. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions.

[0106] The generation unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can provide advice to proceed at a leisurely pace. The generation unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. If the user is in a hurry, the generation unit can also provide advice that emphasizes the shortest route. The generation unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is excited, the generation unit can provide advice with visually stimulating effects. The generation unit records the user's voice and estimates the emotion using voice analysis technology. This allows the generation unit to provide more appropriate advice by adjusting the way the advice is presented based on the user's emotions.

[0107] The reception unit can estimate the user's emotions and adjust the method of receiving requests based on the estimated user emotions. For example, if the user is nervous, it provides a simple interface and minimizes input steps. The reception unit collects the user's biometric data (heart rate and electrodermal activity) using sensors and estimates the user's emotions using an emotion estimation algorithm. If the user is relaxed, it can also provide detailed input options and suggest a customizable input method. The reception unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, it can prioritize voice input and quickly accept requests. The reception unit records the user's voice and estimates the user's emotions using voice analysis technology. This allows the system to adjust the method of receiving requests based on the user's emotions, thereby enabling more appropriate requests to be received.

[0108] The collection unit can analyze the user's past usage history and select the optimal collection method. For example, the collection unit prioritizes collection of history from stores that the user frequently visits. The collection unit analyzes the user's past usage history and prioritizes collection of payment history from stores that the user frequently visits. The collection unit can also focus on collecting history from the user's usage during a specific time period. The collection unit analyzes the user's past usage patterns and prioritizes collection of payment history made during a specific time period. The collection unit can also analyze the user's past usage patterns and select the most efficient collection method. The collection unit selects the optimal collection method based on the user's past usage patterns. In this way, the optimal data collection method can be selected by analyzing the past usage history.

[0109] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis is performed on important payment histories. The analysis unit analyzes the user's payment history and performs a detailed analysis on important payment histories. A brief analysis can also be performed on general payment histories. The analysis unit analyzes the user's payment history and performs a brief analysis on general payment histories. Furthermore, a detailed analysis according to the category can be performed on payment histories belonging to a specific category. The analysis unit analyzes the user's payment history and performs a detailed analysis according to the category on payment histories belonging to a specific category. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0110] When providing advice, the providing unit can analyze the user's past reactions and select the optimal method of providing advice. For example, the providing unit can prioritize providing the form of advice that the user has preferred in the past. The providing unit can also provide advice by referring to the content of advice that the user has responded well to in the past. The providing unit can analyze the user's past reactions and provide advice by referring to the content of advice that has responded well to in the past. Furthermore, the providing unit can analyze the user's past reactions and select the most effective method of providing advice. The providing unit analyzes the user's past reactions and selects the most effective method of providing advice. In this way, the optimal method of providing advice can be selected by analyzing the user's past reactions.

[0111] When receiving a request, the reception unit can select the optimal reception method by referring to the user's past request history. For example, requests that the user has frequently input in the past are automatically displayed as candidates. The reception unit analyzes the user's past request history and automatically displays frequently input requests as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit analyzes the user's past request history and preferentially suggests input methods that have been used. Furthermore, the reception unit can predict and suggest requests that will be used in a specific time period from the user's past request history. The reception unit analyzes the user's past request history and predicts and suggests requests that will be used in a specific time period. In this way, the optimal reception method can be selected by referring to the past request history.

[0112] When providing advice, the providing unit can select an appropriate method of providing advice based on the user's geographical location information. For example, if the user is in a specific area, the providing unit provides money-saving advice for that area. The providing unit acquires the user's geographical location information and provides money-saving advice for that area. Furthermore, if the user is traveling, the providing unit can also provide money-saving advice for the travel destination based on the user's geographical location information. Furthermore, if the user is at home, the providing unit can also provide money-saving advice for nearby stores. The providing unit provides money-saving advice for nearby stores based on the user's geographical location information. In this way, the optimal method of providing advice can be selected by taking geographical location information into consideration.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The collection unit collects the user's usage history, which may include, for example, daily shopping and restaurant payment history, credit card payment history, cash payment history, and electronic payment card payment information. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms to identify user spending patterns, spending trends, and spending predictions. Step 3: The generator generates energy saving advice based on the data analyzed by the analyzer. The energy saving advice includes specific action suggestions and a simulation of the energy saving effect. Step 4: The providing unit provides the advice generated by the generating unit. The providing method may include email, app notification, dashboard display, etc. Step 5: The reception unit interactively receives a user request. For example, the user inputs a request such as "I want to eat a snack once every two days," and the reception unit transmits the request to the generation unit.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user usage histories; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates saving advice based on the data analyzed by the analysis unit; a providing unit that provides the advice generated by the generating unit; A reception unit that interactively receives requests from users. A system characterized by:

2. The collecting unit Collect shopping and restaurant payment history 2. The system of claim 1.

3. The analysis unit Analyze the collected data and generate specific savings recommendations 2. The system of claim 1.

4. The reception unit Accepting user requests 2. The system of claim 1.

5. The providing unit Providing advice tailored to the user's needs 2. The system of claim 1.

6. The analysis unit Parsing electronic payment card payment information 2. The system of claim 1.

7. The providing unit Propose savings by switching to a gold card 2. The system of claim 1.

8. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting usage history based on the estimated user emotions.

2. The system of claim 1.

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