system

Abstract

The system according to the embodiment aims to enable users to easily find services that suit what they want to do. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, a purchase unit, and a revenue unit. The reception unit inputs what the user wants to do. The analysis unit analyzes the information received by the reception unit and identifies appropriate services. The proposal unit proposes the services identified by the analysis unit to the user. The purchase unit handles the procedures for the user to purchase the services proposed by the proposal unit. The revenue unit manages revenue distribution from the service provider.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it takes a lot of time and effort for users to find a service that suits what they want to do.

[0005] The system according to the embodiment aims to enable users to easily find services that suit what they want to do. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a purchase unit, and a revenue unit. The reception unit inputs what the user wants to do. The analysis unit analyzes the information received by the reception unit and identifies an appropriate service. The proposal unit proposes the service identified by the analysis unit to the user. The purchase unit handles the process for the user to purchase the service proposed by the proposal unit. The revenue unit manages revenue distribution from the service provider. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily find a service that suits what he or she wants to do. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI system according to an embodiment of the present invention proposes optimal services for a user's "desires," supports the user's service purchase procedures, and manages revenue shares from service providers. In this AI system, the user inputs "desires," and the AI ​​analyzes the content and proposes optimal services. When the user purchases the proposed service, the system receives a revenue share from the service provider. This frees the user from the hassle of searching, allowing them to easily find what they want, and allows the system operator to earn revenue. For example, when a user inputs "I want to travel," this information is entered into a reception unit. An analysis unit analyzes the information received by the reception unit and identifies optimal services. The service identified by the analysis unit is proposed to the user by a proposal unit. When the user purchases the proposed service, the purchase unit manages the process. Finally, the revenue share from the service provider is managed by a revenue unit. This allows users to easily find what they want, and the system operator to earn revenue. This allows the AI ​​system to propose optimal services for a user's "desires," supports the purchase process, and manages revenue.

[0029] The AI ​​system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a purchase unit, and a revenue unit. The reception unit inputs what the user "wants to do." Examples of what the user "wants to do" include, but are not limited to, traveling, shopping, and studying. The reception unit, for example, accepts an input from the user such as "I want to go on a trip." The reception unit can also input the user's specific hopes and requests. The analysis unit analyzes the information accepted by the reception unit and identifies an optimal service. The analysis unit uses AI to analyze what the user "wants to do" and identifies an optimal service. For example, the analysis unit identifies information such as a travel plan, accommodations, and tourist spots in response to an input such as "I want to go on a trip." The proposal unit proposes the service identified by the analysis unit to the user. The proposal unit presents the optimal service to the user. For example, the proposal unit presents information such as a travel plan, accommodations, and tourist spots to the user. The purchase unit supports the user in purchasing the service proposed by the proposal unit. The purchase unit supports the user in purchasing the service. For example, the purchasing unit handles the purchasing procedures for accommodation reservations and travel plans. The revenue unit manages revenue shares from service providers. The revenue unit receives revenue shares from service providers and distributes them to the system operator. As a result, the AI ​​system according to the embodiment can propose optimal services for the user's "desires," support the purchasing procedures, and manage revenue.

[0030] The reception unit can analyze the user's past input history and provide an input completion function. For example, the reception unit can automatically display as candidates "things to do" that the user has frequently input in the past. The reception unit can also prioritize and suggest input methods (such as voice or text) that the user has used in the past. Furthermore, the reception unit can predict and suggest "things to do" to be used during a specific time period based on the user's past input history. This can improve the efficiency of user input by utilizing the past input history. The input completion function can be realized, for example, by technologies such as predictive input and autocomplete. Some or all of the above-mentioned 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 input data into a generation AI and have the generation AI execute the input completion function.

[0031] The reception unit can automatically generate related questions and collect detailed information based on the user's input. For example, if the user inputs "I want to go on a trip," the reception unit can automatically generate detailed questions such as the destination, budget, and travel period. Furthermore, if the user inputs "I want to start a new hobby," the reception unit can automatically generate questions such as questions about areas of interest, budget, and time constraints. Furthermore, if the user inputs "I want to eat healthy," the reception unit can automatically generate questions such as dietary preferences, allergy information, and budget. This enables more accurate service suggestions by collecting detailed information based on the user's input. The generation of related questions is realized, for example, by a technology such as a question generation algorithm. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input data into a generation AI and cause the generation AI to generate related questions.

[0032] The reception unit can prioritize suggesting region-specific services based on the user's geographical location information. For example, if the user is in a particular city, the reception unit prioritizes suggesting services available in that city. Furthermore, if the user is traveling, the reception unit can prioritize suggesting tourist spots and restaurants near the user's current location. Furthermore, if the user is at home, the reception unit can prioritize suggesting nearby services and delivery services. This allows region-specific services to be suggested by taking the user's geographical location information into consideration. The geographical location information can be acquired using technologies such as GPS data and IP addresses. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to suggest region-specific services.

[0033] The reception unit can analyze the user's social media activity and automatically suggest related "things to do." For example, if the user posts frequently about "travel" on social media, the reception unit can suggest travel-related services. Furthermore, if the user posts frequently about "cooking," the reception unit can suggest cooking classes or recipe services. Furthermore, if the user posts frequently about "fitness," the reception unit can suggest gyms or fitness programs. By analyzing social media activity, suggestions based on the user's interests become possible. The analysis of social media activity is performed using data such as the content of posts and the number of likes. 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 social media data into a generation AI and have the generation AI suggest related "things to do."

[0034] The analysis unit can improve the accuracy of the analysis based on the user's past behavioral data. For example, the analysis unit can suggest optimal services based on the user's history of services used in the past. The analysis unit can also predict and suggest services that the user will use during a specific time period based on the user's past behavioral data. Furthermore, the analysis unit can analyze the user's past behavioral data and suggest the most efficient service. This improves the accuracy of the analysis by utilizing past behavioral data. The use of past behavioral data is performed using, for example, data such as past purchase history and browsing history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavioral data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0035] The analysis unit can identify an appropriate service by combining multiple analysis methods based on the user's input content. For example, the analysis unit can analyze the user's input content using natural language processing to identify relevant services. The analysis unit can also analyze the user's input content using a machine learning algorithm to identify the optimal service. Furthermore, the analysis unit can analyze the user's input content using data mining technology to identify the optimal service. This enables more accurate service proposals by combining multiple analysis methods. The combination of multiple analysis methods is achieved using techniques such as clustering and regression analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI and have the generation AI execute a combination of multiple analysis methods.

[0036] The analysis unit can prioritize analyzing region-specific data based on the user's geographical location information. For example, if the user is in a particular city, the analysis unit prioritizes analyzing services available in that city. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing tourist spots and restaurants near the user's current location. Furthermore, if the user is at home, the analysis unit can prioritize analyzing nearby services and delivery services. This allows region-specific data to be prioritized by taking the user's geographical location information into consideration. Geographical location information can be acquired using technologies such as GPS data and IP addresses. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's geographical location data into the generation AI and cause the generation AI to analyze the region-specific data.

[0037] The analysis unit can analyze a user's social media activity and incorporate related data into the analysis. For example, if a user posts a lot about "travel" on social media, the analysis unit can prioritize analyzing travel-related data. Furthermore, if a user posts a lot about "cooking," the analysis unit can prioritize analyzing cooking-related data. Furthermore, if a user posts a lot about "fitness," the analysis unit can prioritize analyzing fitness-related data. This analysis of social media activity enables analysis based on the user's interests. The analysis of social media activity is performed using data such as the content of posts and the number of likes. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media data into a generation AI and have the generation AI analyze the related data.

[0038] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past selection history. The suggestion unit, for example, suggests an optimal service based on the history of services selected by the user in the past. The suggestion unit can also predict and suggest services that the user will use during a specific time period from the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and suggest the most efficient service. In this way, the accuracy of the suggestion is improved by utilizing the past selection history. The use of the past selection history is performed using data such as past purchase history and browsing history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past selection data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0039] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the user's current situation. For example, if the user is traveling, the suggestion unit can apply an algorithm that prioritizes suggesting travel-related services. Furthermore, if the user is at home, the suggestion unit can apply an algorithm that prioritizes suggesting nearby services or delivery services. Furthermore, if the user is participating in a specific event, the suggestion unit can apply an algorithm that prioritizes suggesting services related to the event. This enables suggestions based on the user's current situation. The current situation is acquired using data such as the user's current geographic location and current activity details. The suggestion algorithm is applied using technologies such as collaborative filtering and content-based filtering. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's current situation data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0040] When making a suggestion, the suggestion unit can prioritize suggesting region-specific services based on the user's geographical location information. For example, if the user is in a particular city, the suggestion unit prioritizes suggesting services available in that city. Furthermore, if the user is traveling, the suggestion unit can prioritize suggesting tourist spots and restaurants near the user's current location. Furthermore, if the user is at home, the suggestion unit can prioritize suggesting nearby services and delivery services. This allows region-specific services to be suggested by taking the user's geographical location information into consideration. The geographical location information can be acquired using technologies such as GPS data and IP addresses. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's geographical location data into the generation AI and cause the generation AI to suggest region-specific services.

[0041] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related services. For example, if the user posts a lot about "travel" on social media, the suggestion unit can suggest travel-related services. Furthermore, if the user posts a lot about "cooking," the suggestion unit can also suggest cooking classes or recipe services. Furthermore, if the user posts a lot about "fitness," the suggestion unit can also suggest gyms or fitness programs. By analyzing social media activity, suggestions based on the user's interests become possible. The analysis of social media activity is performed using data such as the content of posts and the number of likes. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's social media data into a generation AI and have the generation AI suggest related services.

[0042] At the time of purchase, the purchasing unit can suggest an appropriate purchasing procedure by referring to the user's past purchase history. The purchasing unit can suggest an optimal purchasing procedure, for example, based on the history of services the user has previously purchased. The purchasing unit can also predict and suggest services that the user will use during a specific time period based on the user's past purchase history. Furthermore, the purchasing unit can analyze the user's past purchase history and suggest the most efficient purchasing procedure. This improves the efficiency of the purchasing procedure by utilizing the past purchase history. The past purchase history is used, for example, using data such as past purchased products and purchase dates and times. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the user's past purchase data into a generation AI and have the generation AI suggest an appropriate purchasing procedure.

[0043] The purchasing unit can apply different purchasing procedure algorithms depending on the user's current situation when making a purchase. For example, if the user is traveling, the purchasing unit can apply an algorithm that prioritizes the purchase of travel-related services. Furthermore, if the user is at home, the purchasing unit can apply an algorithm that prioritizes the purchase of local services or delivery services. Furthermore, if the user is attending a specific event, the purchasing unit can apply an algorithm that prioritizes the purchase of services related to the event. This enables a purchasing procedure that is tailored to the user's current situation. The current situation can be acquired using data such as the user's current geographic location and current activities. The application of the purchasing procedure algorithm can be achieved using technologies such as one-click purchasing and installment payments. Some or all of the above-described processing in the purchasing unit can be performed using, for example, AI, or without AI. For example, the purchasing unit can input the user's current situation data into a generation AI and have the generation AI apply the purchasing procedure algorithm.

[0044] At the time of purchase, the purchasing unit can prioritize region-specific purchasing procedures based on the user's geographical location information. For example, if the user is in a particular city, the purchasing unit prioritizes purchasing procedures for services available in that city. Furthermore, if the user is traveling, the purchasing unit can prioritize purchasing procedures for tourist spots and restaurants near the user's current location. Furthermore, if the user is at home, the purchasing unit can prioritize purchasing procedures for nearby services and delivery services. This allows region-specific purchasing procedures to be proposed by taking the user's geographical location information into consideration. Geographical location information can be acquired using technologies such as GPS data and IP addresses. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or without AI. For example, the purchasing unit can input the user's geographical location data into a generation AI and cause the generation AI to suggest region-specific purchasing procedures.

[0045] The purchasing unit can analyze the user's social media activity at the time of purchase and suggest related purchase procedures. For example, if the user posts frequently about "travel" on social media, the purchasing unit can suggest purchase procedures for travel-related services. Furthermore, if the user posts frequently about "cooking," the purchasing unit can suggest purchase procedures for cooking classes or recipe services. Furthermore, if the user posts frequently about "fitness," the purchasing unit can suggest purchase procedures for gyms or fitness programs. Thus, analyzing social media activity enables purchase procedures based on the user's interests. The analysis of social media activity is performed using data such as the content of posts and the number of likes. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without AI. For example, the purchasing unit can input the user's social media data into a generation AI and have the generation AI execute related purchase procedure suggestions.

[0046] During revenue management, the revenue department can select an appropriate revenue management method by referring to past revenue data. For example, the revenue department can propose an optimal revenue distribution method based on past revenue data. The revenue department can also analyze and propose patterns of revenue increase during specific time periods from past revenue data. Furthermore, the revenue department can analyze past revenue data and propose the most efficient revenue management method. By utilizing past revenue data, the accuracy of revenue management can be improved. The use of past revenue data is performed using, for example, data such as past revenue amounts and revenue sources. Some or all of the above-described processing in the revenue department may be performed using, for example, AI, or may be performed without AI. For example, the revenue department can input past revenue data into a generation AI and have the generation AI select an appropriate revenue management method.

[0047] The revenue department can allocate revenue by taking into account the service provider's attribute information during revenue management. The revenue department, for example, adjusts the revenue allocation ratio depending on the size of the service provider. The revenue department can also adjust the revenue allocation method depending on the type of service provided by the service provider. Furthermore, the revenue department can adjust the revenue allocation ratio based on the service provider's past performance. This enables more equitable revenue allocation by taking into account the service provider's attribute information. The attribute information is used, for example, using data such as the size and industry of the service provider. Some or all of the above-mentioned processing in the revenue department may be performed using, for example, AI, or may be performed without AI. For example, the revenue department can input the service provider's attribute data into a generation AI and have the generation AI adjust the revenue allocation.

[0048] The revenue unit can allocate revenue based on the geographic location information of the service provider during revenue management. For example, if the service provider is in a particular city, the revenue unit can prioritize revenue distribution in that city. Furthermore, if the service provider is traveling, the revenue unit can prioritize revenue distribution in the vicinity of the service provider's current location. Furthermore, if the service provider is at home, the revenue unit can prioritize revenue distribution in the vicinity. This enables region-specific revenue distribution by taking into account the geographic location information of the service provider. The geographic location information can be acquired using technologies such as GPS data or IP addresses. Some or all of the above-described processing in the revenue unit can be performed using, for example, AI, or without AI. For example, the revenue unit can input the geographic location data of the service provider into the generation AI and have the generation AI adjust the revenue distribution.

[0049] During revenue management, the revenue department can analyze the service provider's social media activity and propose relevant revenue management methods. For example, if the service provider posts many times about "travel" on social media, the revenue department can propose travel-related revenue management methods. Furthermore, if the service provider posts many times about "cooking," the revenue department can propose cooking-related revenue management methods. Furthermore, if the service provider posts many times about "fitness," the revenue department can propose fitness-related revenue management methods. This enables revenue management based on the service provider's interests by analyzing social media activity. The analysis of social media activity is performed using data such as the content of posts and the number of likes. Some or all of the above-described processing in the revenue department may be performed using, for example, AI, or may be performed without AI. For example, the revenue department can input the service provider's social media data into a generation AI and have the generation AI propose relevant revenue management methods.

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

[0051] The reception unit can automatically display related past user feedback based on the user's input. For example, if a user inputs "I want to go on a trip," feedback and reviews from users who made similar requests in the past can be displayed. If a user inputs "I want to start a new hobby," personal experiences and advice from users who made similar requests in the past can be displayed. Furthermore, if a user inputs "I want to eat healthy," recipes and meal plans from users who made similar requests in the past can be displayed. This allows the user to make more appropriate choices by referring to the experiences of other users. Displaying past feedback is performed using data from, for example, a feedback database or a review site. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input data into a generation AI and cause the generation AI to display related feedback.

[0052] The reception unit can automatically display related trend information based on the user's input. For example, if the user inputs "I want to travel," current travel trends and popular tourist destinations can be displayed. If the user inputs "I want to start a new hobby," current hobby trends and popular hobbies can be displayed. Furthermore, if the user inputs "I want to eat healthy," current healthy eating trends and popular recipes can be displayed. This allows the user to make more appropriate choices by referring to the latest trend information. The display of trend information is performed, for example, using data from a trend database or social media. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without AI. For example, the reception unit can input the user's input data into a generation AI and cause the generation AI to display related trend information.

[0053] The reception unit can automatically display related news articles based on the user's input. For example, if the user inputs "I want to go on a trip," news articles about recent trips can be displayed. Furthermore, if the user inputs "I want to start a new hobby," news articles about recent hobbies can be displayed. Furthermore, if the user inputs "I want to eat healthy," news articles about recent healthy eating can be displayed. This allows the user to make more appropriate choices by referring to the latest news information. The display of news articles is performed, for example, using data from a news database or a news site. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without AI. For example, the reception unit can input the user's input data into a generation AI and cause the generation AI to display related news articles.

[0054] The analysis unit can automatically display related past analysis results based on the user's input. For example, if a user inputs "I want to go on a trip," the analysis unit can display analysis results of users who made similar requests in the past. If a user inputs "I want to start a new hobby," the analysis unit can display analysis results of users who made similar requests in the past. Furthermore, if a user inputs "I want to eat healthy," the analysis unit can display analysis results of users who made similar requests in the past. This allows the user to make more appropriate choices by referring to the analysis results of other users. Displaying past analysis results is performed, for example, using an analysis database or an archive of analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's input data into a generation AI and cause the generation AI to display related analysis results.

[0055] The analysis unit can automatically display related statistical data based on the user's input. For example, if the user inputs "I want to go on a trip," statistical data related to travel can be displayed. If the user inputs "I want to start a new hobby," statistical data related to hobbies can be displayed. Furthermore, if the user inputs "I want to eat healthy," statistical data related to healthy eating can be displayed. This allows the user to make more appropriate choices by referring to the statistical data. The display of statistical data is performed, for example, using statistical databases or government statistical data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's input data into a generation AI and cause the generation AI to display related statistical data.

[0056] The analysis unit can automatically display the opinions of related experts based on the user's input. For example, if the user inputs "I want to travel," the analysis unit can display the opinions of travel experts. If the user inputs "I want to start a new hobby," the analysis unit can display the opinions of hobby experts. If the user inputs "I want to eat healthy," the analysis unit can display the opinions of health food experts. This allows the user to make more appropriate choices by referring to the opinions of experts. The expert opinions are displayed, for example, using an expert database or blog posts by experts. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's input data into a generation AI and cause the generation AI to display the opinions of related experts.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit inputs what the user "wants to do." Examples of what the user "wants to do" include, but are not limited to, traveling, shopping, studying, etc. The reception unit receives the user's input such as "I want to go on a trip." The user can also input specific wishes and requests. Step 2: The analysis unit analyzes the information received by the reception unit and identifies the most suitable service. The analysis unit uses AI to analyze what the user wants to do, and for example, when someone says, "I want to go on a trip," it identifies information such as travel plans, accommodations, and tourist spots. Step 3: The suggestion unit suggests the services identified by the analysis unit to the user. The suggestion unit presents the user with optimal services, such as travel plans, accommodations, and tourist destination information. Step 4: The purchase unit supports the user in purchasing the service proposed by the proposal unit. The purchase unit supports the user in purchasing the service, for example, booking accommodation or purchasing a travel plan. Step 5: The revenue department manages the revenue share from the service provider. The revenue department receives the revenue share from the service provider and distributes it to the system operator.

[0059] (Example 2) An AI system according to an embodiment of the present invention proposes optimal services for a user's "desires," supports the user's service purchase procedures, and manages revenue shares from service providers. In this AI system, the user inputs "desires," and the AI ​​analyzes the content and proposes optimal services. When the user purchases the proposed service, the system receives a revenue share from the service provider. This frees the user from the hassle of searching, allowing them to easily find what they want, and allows the system operator to earn revenue. For example, when a user inputs "I want to travel," this information is entered into a reception unit. An analysis unit analyzes the information received by the reception unit and identifies optimal services. The service identified by the analysis unit is proposed to the user by a proposal unit. When the user purchases the proposed service, the purchase unit manages the process. Finally, the revenue share from the service provider is managed by a revenue unit. This allows users to easily find what they want, and the system operator to earn revenue. This allows the AI ​​system to propose optimal services for a user's "desires," supports the purchase process, and manages revenue.

[0060] The AI ​​system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a purchase unit, and a revenue unit. The reception unit inputs what the user "wants to do." Examples of what the user "wants to do" include, but are not limited to, traveling, shopping, and studying. The reception unit, for example, accepts an input from the user such as "I want to go on a trip." The reception unit can also input the user's specific hopes and requests. The analysis unit analyzes the information accepted by the reception unit and identifies an optimal service. The analysis unit uses AI to analyze what the user "wants to do" and identifies an optimal service. For example, the analysis unit identifies information such as a travel plan, accommodations, and tourist spots in response to an input such as "I want to go on a trip." The proposal unit proposes the service identified by the analysis unit to the user. The proposal unit presents the optimal service to the user. For example, the proposal unit presents information such as a travel plan, accommodations, and tourist spots to the user. The purchase unit supports the user in purchasing the service proposed by the proposal unit. The purchase unit supports the user in purchasing the service. For example, the purchasing unit handles the purchasing procedures for accommodation reservations and travel plans. The revenue unit manages revenue shares from service providers. The revenue unit receives revenue shares from service providers and distributes them to the system operator. As a result, the AI ​​system according to the embodiment can propose optimal services for the user's "desires," support the purchasing procedures, and manage revenue.

[0061] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and allow the user to quickly input what they want to do. This can improve the user experience by optimizing the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0062] The reception unit can analyze the user's past input history and provide an input completion function. For example, the reception unit can automatically display as candidates "things to do" that the user has frequently input in the past. The reception unit can also prioritize and suggest input methods (such as voice or text) that the user has used in the past. Furthermore, the reception unit can predict and suggest "things to do" to be used during a specific time period based on the user's past input history. This can improve the efficiency of user input by utilizing the past input history. The input completion function can be realized, for example, by technologies such as predictive input and autocomplete. Some or all of the above-mentioned 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 input data into a generation AI and have the generation AI execute the input completion function.

[0063] The reception unit can automatically generate related questions and collect detailed information based on the user's input. For example, if the user inputs "I want to go on a trip," the reception unit can automatically generate detailed questions such as the destination, budget, and travel period. Furthermore, if the user inputs "I want to start a new hobby," the reception unit can automatically generate questions such as questions about areas of interest, budget, and time constraints. Furthermore, if the user inputs "I want to eat healthy," the reception unit can automatically generate questions such as dietary preferences, allergy information, and budget. This enables more accurate service suggestions by collecting detailed information based on the user's input. The generation of related questions is realized, for example, by a technology such as a question generation algorithm. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input data into a generation AI and cause the generation AI to generate related questions.

[0064] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize relaxing services. Furthermore, if the user is excited, the reception unit can prioritize active services. Furthermore, if the user is tired, the reception unit can prioritize refreshing services. This enables more appropriate service suggestions by prioritizing input content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The reception unit can prioritize suggesting region-specific services based on the user's geographical location information. For example, if the user is in a particular city, the reception unit prioritizes suggesting services available in that city. Furthermore, if the user is traveling, the reception unit can prioritize suggesting tourist spots and restaurants near the user's current location. Furthermore, if the user is at home, the reception unit can prioritize suggesting nearby services and delivery services. This allows region-specific services to be suggested by taking the user's geographical location information into consideration. The geographical location information can be acquired using technologies such as GPS data and IP addresses. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to suggest region-specific services.

[0066] The reception unit can analyze the user's social media activity and automatically suggest related "things to do." For example, if the user posts frequently about "travel" on social media, the reception unit can suggest travel-related services. Furthermore, if the user posts frequently about "cooking," the reception unit can suggest cooking classes or recipe services. Furthermore, if the user posts frequently about "fitness," the reception unit can suggest gyms or fitness programs. By analyzing social media activity, suggestions based on the user's interests become possible. The analysis of social media activity is performed using data such as the content of posts and the number of likes. 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 social media data into a generation AI and have the generation AI suggest related "things to do."

[0067] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest multiple services. If the user is in a hurry, the analysis unit can also perform a quick analysis and suggest only one most appropriate service. Furthermore, if the user is excited, the analysis unit can prioritize analyzing visually appealing services. This allows for more appropriate service suggestions by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0068] The analysis unit can improve the accuracy of the analysis based on the user's past behavioral data. For example, the analysis unit can suggest optimal services based on the user's history of services used in the past. The analysis unit can also predict and suggest services that the user will use during a specific time period based on the user's past behavioral data. Furthermore, the analysis unit can analyze the user's past behavioral data and suggest the most efficient service. This improves the accuracy of the analysis by utilizing past behavioral data. The use of past behavioral data is performed using, for example, data such as past purchase history and browsing history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavioral data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0069] The analysis unit can identify an appropriate service by combining multiple analysis methods based on the user's input content. For example, the analysis unit can analyze the user's input content using natural language processing to identify relevant services. The analysis unit can also analyze the user's input content using a machine learning algorithm to identify the optimal service. Furthermore, the analysis unit can analyze the user's input content using data mining technology to identify the optimal service. This enables more accurate service proposals by combining multiple analysis methods. The combination of multiple analysis methods is achieved using techniques such as clustering and regression analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI and have the generation AI execute a combination of multiple analysis methods.

[0070] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This improves the user experience by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0071] The analysis unit can prioritize analyzing region-specific data based on the user's geographical location information. For example, if the user is in a particular city, the analysis unit prioritizes analyzing services available in that city. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing tourist spots and restaurants near the user's current location. Furthermore, if the user is at home, the analysis unit can prioritize analyzing nearby services and delivery services. This allows region-specific data to be prioritized by taking the user's geographical location information into consideration. Geographical location information can be acquired using technologies such as GPS data and IP addresses. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's geographical location data into the generation AI and cause the generation AI to analyze the region-specific data.

[0072] The analysis unit can analyze a user's social media activity and incorporate related data into the analysis. For example, if a user posts a lot about "travel" on social media, the analysis unit can prioritize analyzing travel-related data. Furthermore, if a user posts a lot about "cooking," the analysis unit can prioritize analyzing cooking-related data. Furthermore, if a user posts a lot about "fitness," the analysis unit can prioritize analyzing fitness-related data. This analysis of social media activity enables analysis based on the user's interests. The analysis of social media activity is performed using data such as the content of posts and the number of likes. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media data into a generation AI and have the generation AI analyze the related data.

[0073] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide a detailed explanation when the user is relaxed. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise and to-the-point suggestion when the user is excited. This can improve the user experience by adjusting the way suggestions are presented according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are presented.

[0074] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past selection history. The suggestion unit, for example, suggests an optimal service based on the history of services selected by the user in the past. The suggestion unit can also predict and suggest services that the user will use during a specific time period from the user's past selection history. Furthermore, the suggestion unit can analyze the user's past selection history and suggest the most efficient service. In this way, the accuracy of the suggestion is improved by utilizing the past selection history. The use of the past selection history is performed using data such as past purchase history and browsing history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past selection data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0075] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the user's current situation. For example, if the user is traveling, the suggestion unit can apply an algorithm that prioritizes suggesting travel-related services. Furthermore, if the user is at home, the suggestion unit can apply an algorithm that prioritizes suggesting nearby services or delivery services. Furthermore, if the user is participating in a specific event, the suggestion unit can apply an algorithm that prioritizes suggesting services related to the event. This enables suggestions based on the user's current situation. The current situation is acquired using data such as the user's current geographic location and current activity details. The suggestion algorithm is applied using technologies such as collaborative filtering and content-based filtering. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's current situation data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0076] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize relaxing services. Furthermore, if the user is excited, the suggestion unit can prioritize active services. Furthermore, if the user is tired, the suggestion unit can prioritize refreshing services. This enables more appropriate service suggestions by determining the priority of suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of suggestions.

[0077] When making a suggestion, the suggestion unit can prioritize suggesting region-specific services based on the user's geographical location information. For example, if the user is in a particular city, the suggestion unit prioritizes suggesting services available in that city. Furthermore, if the user is traveling, the suggestion unit can prioritize suggesting tourist spots and restaurants near the user's current location. Furthermore, if the user is at home, the suggestion unit can prioritize suggesting nearby services and delivery services. This allows region-specific services to be suggested by taking the user's geographical location information into consideration. The geographical location information can be acquired using technologies such as GPS data and IP addresses. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's geographical location data into the generation AI and cause the generation AI to suggest region-specific services.

[0078] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related services. For example, if the user posts a lot about "travel" on social media, the suggestion unit can suggest travel-related services. Furthermore, if the user posts a lot about "cooking," the suggestion unit can also suggest cooking classes or recipe services. Furthermore, if the user posts a lot about "fitness," the suggestion unit can also suggest gyms or fitness programs. By analyzing social media activity, suggestions based on the user's interests become possible. The analysis of social media activity is performed using data such as the content of posts and the number of likes. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's social media data into a generation AI and have the generation AI suggest related services.

[0079] The purchasing unit can estimate the user's emotions and adjust the purchase checkout interface based on the estimated user emotions. For example, if the user is feeling stressed, the purchasing unit can provide a simple and intuitive interface and minimize the purchase steps. Furthermore, if the user is relaxed, the purchasing unit can provide detailed purchase options and suggest customizable purchase methods. Furthermore, if the user is in a hurry, the purchasing unit can prioritize voice input to quickly complete the purchase checkout. This improves the user experience by adjusting the purchase checkout interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 purchasing unit can be performed using AI, for example, or without AI. For example, the purchasing unit can input user emotion data into the generation AI and have the generation AI adjust the purchase checkout interface.

[0080] At the time of purchase, the purchasing unit can suggest an appropriate purchasing procedure by referring to the user's past purchase history. The purchasing unit can suggest an optimal purchasing procedure, for example, based on the history of services the user has previously purchased. The purchasing unit can also predict and suggest services that the user will use during a specific time period based on the user's past purchase history. Furthermore, the purchasing unit can analyze the user's past purchase history and suggest the most efficient purchasing procedure. This improves the efficiency of the purchasing procedure by utilizing the past purchase history. The past purchase history is used, for example, using data such as past purchased products and purchase dates and times. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the user's past purchase data into a generation AI and have the generation AI suggest an appropriate purchasing procedure.

[0081] The purchasing unit can apply different purchasing procedure algorithms depending on the user's current situation when making a purchase. For example, if the user is traveling, the purchasing unit can apply an algorithm that prioritizes the purchase of travel-related services. Furthermore, if the user is at home, the purchasing unit can apply an algorithm that prioritizes the purchase of local services or delivery services. Furthermore, if the user is attending a specific event, the purchasing unit can apply an algorithm that prioritizes the purchase of services related to the event. This enables a purchasing procedure that is tailored to the user's current situation. The current situation can be acquired using data such as the user's current geographic location and current activities. The application of the purchasing procedure algorithm can be achieved using technologies such as one-click purchasing and installment payments. Some or all of the above-described processing in the purchasing unit can be performed using, for example, AI, or without AI. For example, the purchasing unit can input the user's current situation data into a generation AI and have the generation AI apply the purchasing procedure algorithm.

[0082] The purchasing unit can estimate the user's emotions and prioritize the purchase process based on the estimated user emotions. For example, if the user is feeling stressed, the purchasing unit can prioritize purchasing procedures for services that will help them relax. Furthermore, if the user is excited, the purchasing unit can prioritize purchasing procedures for active services. Furthermore, if the user is tired, the purchasing unit can prioritize purchasing procedures for services that will help them refresh. This allows for more appropriate service purchases by prioritizing the purchase process based on the user's emotions. The emotion estimation is achieved 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 purchasing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the purchasing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the purchase process.

[0083] At the time of purchase, the purchasing unit can prioritize region-specific purchasing procedures based on the user's geographical location information. For example, if the user is in a particular city, the purchasing unit prioritizes purchasing procedures for services available in that city. Furthermore, if the user is traveling, the purchasing unit can prioritize purchasing procedures for tourist spots and restaurants near the user's current location. Furthermore, if the user is at home, the purchasing unit can prioritize purchasing procedures for nearby services and delivery services. This allows region-specific purchasing procedures to be proposed by taking the user's geographical location information into consideration. Geographical location information can be acquired using technologies such as GPS data and IP addresses. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or without AI. For example, the purchasing unit can input the user's geographical location data into a generation AI and cause the generation AI to suggest region-specific purchasing procedures.

[0084] The purchasing unit can analyze the user's social media activity at the time of purchase and suggest related purchase procedures. For example, if the user posts frequently about "travel" on social media, the purchasing unit can suggest purchase procedures for travel-related services. Furthermore, if the user posts frequently about "cooking," the purchasing unit can suggest purchase procedures for cooking classes or recipe services. Furthermore, if the user posts frequently about "fitness," the purchasing unit can suggest purchase procedures for gyms or fitness programs. Thus, analyzing social media activity enables purchase procedures based on the user's interests. The analysis of social media activity is performed using data such as the content of posts and the number of likes. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without AI. For example, the purchasing unit can input the user's social media data into a generation AI and have the generation AI execute related purchase procedure suggestions.

[0085] The revenue unit can estimate a user's emotions and adjust the revenue management method based on the estimated user emotions. For example, the revenue unit can provide detailed revenue management options when the user is relaxed. The revenue unit can also provide concise and quick revenue management options when the user is in a hurry. Furthermore, the revenue unit can provide visually appealing revenue management options when the user is excited. This improves the efficiency of revenue management by adjusting the revenue management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 revenue unit can be performed using AI, or without AI. For example, the revenue unit can input user emotion data into the generation AI and have the generation AI adjust the revenue management method.

[0086] During revenue management, the revenue department can select an appropriate revenue management method by referring to past revenue data. For example, the revenue department can propose an optimal revenue distribution method based on past revenue data. The revenue department can also analyze and propose patterns of revenue increase during specific time periods from past revenue data. Furthermore, the revenue department can analyze past revenue data and propose the most efficient revenue management method. By utilizing past revenue data, the accuracy of revenue management can be improved. The use of past revenue data is performed using, for example, data such as past revenue amounts and revenue sources. Some or all of the above-described processing in the revenue department may be performed using, for example, AI, or may be performed without AI. For example, the revenue department can input past revenue data into a generation AI and have the generation AI select an appropriate revenue management method.

[0087] The revenue department can allocate revenue by taking into account the service provider's attribute information during revenue management. The revenue department, for example, adjusts the revenue allocation ratio depending on the size of the service provider. The revenue department can also adjust the revenue allocation method depending on the type of service provided by the service provider. Furthermore, the revenue department can adjust the revenue allocation ratio based on the service provider's past performance. This enables more equitable revenue allocation by taking into account the service provider's attribute information. The attribute information is used, for example, using data such as the size and industry of the service provider. Some or all of the above-mentioned processing in the revenue department may be performed using, for example, AI, or may be performed without AI. For example, the revenue department can input the service provider's attribute data into a generation AI and have the generation AI adjust the revenue allocation.

[0088] The revenue unit can estimate a user's emotions and determine revenue sharing priorities based on the estimated user emotions. For example, if the user is stressed, the revenue unit can quickly distribute revenue. The revenue unit can also provide detailed revenue sharing options if the user is relaxed. Furthermore, the revenue unit can provide visually appealing revenue sharing options if the user is excited. This enables more appropriate revenue sharing by determining revenue sharing priorities according to the user's emotions. Emotion estimation is achieved 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 revenue unit can be performed using AI, or without AI. For example, the revenue unit can input user emotion data into the generation AI and have the generation AI determine revenue sharing priorities.

[0089] The revenue unit can allocate revenue based on the geographic location information of the service provider during revenue management. For example, if the service provider is in a particular city, the revenue unit can prioritize revenue distribution in that city. Furthermore, if the service provider is traveling, the revenue unit can prioritize revenue distribution in the vicinity of the service provider's current location. Furthermore, if the service provider is at home, the revenue unit can prioritize revenue distribution in the vicinity. This enables region-specific revenue distribution by taking into account the geographic location information of the service provider. The geographic location information can be acquired using technologies such as GPS data or IP addresses. Some or all of the above-described processing in the revenue unit can be performed using, for example, AI, or without AI. For example, the revenue unit can input the geographic location data of the service provider into the generation AI and have the generation AI adjust the revenue distribution.

[0090] During revenue management, the revenue department can analyze the service provider's social media activity and propose relevant revenue management methods. For example, if the service provider posts many times about "travel" on social media, the revenue department can propose travel-related revenue management methods. Furthermore, if the service provider posts many times about "cooking," the revenue department can propose cooking-related revenue management methods. Furthermore, if the service provider posts many times about "fitness," the revenue department can propose fitness-related revenue management methods. This enables revenue management based on the service provider's interests by analyzing social media activity. The analysis of social media activity is performed using data such as the content of posts and the number of likes. Some or all of the above-described processing in the revenue department may be performed using, for example, AI, or may be performed without AI. For example, the revenue department can input the service provider's social media data into a generation AI and have the generation AI propose relevant revenue management methods. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, proposal unit, purchase unit, and revenue unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and allows the user to input "what they want to do." The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The proposal unit is implemented, for example, by the output device 40 of the smart device 14 and proposes services identified by the analysis unit to the user. The purchase unit is implemented, for example, by the control unit 46A of the smart device 14 and supports the user in purchasing the proposed services. The revenue unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and manages revenue sharing from service providers. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, purchase unit, and revenue 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 reception unit is realized by the microphone 238 of the smart glasses 214 and can input the user's "desires." The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The proposal unit is realized, for example, by the speaker 240 of the smart glasses 214 and proposes services identified by the analysis unit to the user. The purchase unit is realized, for example, by the control unit 46A of the smart glasses 214 and supports the user in purchasing the proposed services. The revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages revenue sharing from service providers. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, purchase unit, and revenue unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and allows the user to input "what they want to do." The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The proposal unit is realized, for example, by the speaker 240 of the headset terminal 314 and proposes services identified by the analysis unit to the user. The purchase unit is realized, for example, by the control unit 46A of the headset terminal 314 and supports the user in purchasing the proposed services. The revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages revenue sharing from service providers. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, purchase unit, and revenue unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and can input the user's "desires." The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The proposal unit is realized, for example, by the speaker 240 of the robot 414 and proposes services identified by the analysis unit to the user. The purchase unit is realized, for example, by the control unit 46A of the robot 414 and supports the user in purchasing the proposed services. The revenue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages revenue sharing from service providers.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The reception unit can automatically display related past user feedback based on the user's input. For example, if a user inputs "I want to go on a trip," feedback and reviews from users who made similar requests in the past can be displayed. If a user inputs "I want to start a new hobby," personal experiences and advice from users who made similar requests in the past can be displayed. Furthermore, if a user inputs "I want to eat healthy," recipes and meal plans from users who made similar requests in the past can be displayed. This allows the user to make more appropriate choices by referring to the experiences of other users. Displaying past feedback is performed using data from, for example, a feedback database or a review site. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input data into a generation AI and cause the generation AI to display related feedback.

[0093] The reception unit can estimate the user's emotions and adjust the confirmation message for the input content based on the estimated user emotions. For example, if the user is stressed, a simple, positive confirmation message can be displayed. Alternatively, if the user is relaxed, a detailed confirmation message can be displayed. Furthermore, if the user is in a hurry, a short message can be displayed to allow the user to complete the confirmation quickly. This improves the user experience by adjusting the confirmation message according to the user's emotions. The 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 can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0094] The reception unit can automatically display related trend information based on the user's input. For example, if the user inputs "I want to travel," current travel trends and popular tourist destinations can be displayed. If the user inputs "I want to start a new hobby," current hobby trends and popular hobbies can be displayed. Furthermore, if the user inputs "I want to eat healthy," current healthy eating trends and popular recipes can be displayed. This allows the user to make more appropriate choices by referring to the latest trend information. The display of trend information is performed, for example, using data from a trend database or social media. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without AI. For example, the reception unit can input the user's input data into a generation AI and cause the generation AI to display related trend information.

[0095] The reception unit can automatically display related news articles based on the user's input. For example, if the user inputs "I want to go on a trip," news articles about recent trips can be displayed. Furthermore, if the user inputs "I want to start a new hobby," news articles about recent hobbies can be displayed. Furthermore, if the user inputs "I want to eat healthy," news articles about recent healthy eating can be displayed. This allows the user to make more appropriate choices by referring to the latest news information. The display of news articles is performed, for example, using data from a news database or a news site. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without AI. For example, the reception unit can input the user's input data into a generation AI and cause the generation AI to display related news articles.

[0096] The reception unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user is feeling stressed, an encouraging message can be displayed. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, quick feedback can be provided. This improves the user experience by providing feedback according to the user's emotions. 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 reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0097] The analysis unit can automatically display related past analysis results based on the user's input. For example, if a user inputs "I want to go on a trip," the analysis unit can display analysis results of users who made similar requests in the past. If a user inputs "I want to start a new hobby," the analysis unit can display analysis results of users who made similar requests in the past. Furthermore, if a user inputs "I want to eat healthy," the analysis unit can display analysis results of users who made similar requests in the past. This allows the user to make more appropriate choices by referring to the analysis results of other users. Displaying past analysis results is performed, for example, using an analysis database or an archive of analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's input data into a generation AI and cause the generation AI to display related analysis results.

[0098] The analysis unit can estimate the user's emotions and provide feedback of the analysis results based on the estimated user emotions. For example, if the user is stressed, simple, positive feedback can be provided. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, quick feedback can be provided. This improves the user experience by providing feedback according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0099] The analysis unit can automatically display related statistical data based on the user's input. For example, if the user inputs "I want to go on a trip," statistical data related to travel can be displayed. If the user inputs "I want to start a new hobby," statistical data related to hobbies can be displayed. Furthermore, if the user inputs "I want to eat healthy," statistical data related to healthy eating can be displayed. This allows the user to make more appropriate choices by referring to the statistical data. The display of statistical data is performed, for example, using statistical databases or government statistical data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's input data into a generation AI and cause the generation AI to display related statistical data.

[0100] The analysis unit can automatically display the opinions of related experts based on the user's input. For example, if the user inputs "I want to travel," the analysis unit can display the opinions of travel experts. If the user inputs "I want to start a new hobby," the analysis unit can display the opinions of hobby experts. If the user inputs "I want to eat healthy," the analysis unit can display the opinions of health food experts. This allows the user to make more appropriate choices by referring to the opinions of experts. The expert opinions are displayed, for example, using an expert database or blog posts by experts. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's input data into a generation AI and cause the generation AI to display the opinions of related experts.

[0101] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is stressed, a simple and quick notification method can be provided. If the user is relaxed, a detailed notification method can be provided. Furthermore, if the user is in a hurry, a notification method that focuses on the main points can be provided. This allows for an improved user experience by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit inputs what the user "wants to do." Examples of what the user "wants to do" include, but are not limited to, traveling, shopping, studying, etc. The reception unit receives the user's input such as "I want to go on a trip." The user can also input specific wishes and requests. Step 2: The analysis unit analyzes the information received by the reception unit and identifies the most suitable service. The analysis unit uses AI to analyze what the user wants to do, and for example, when someone says, "I want to go on a trip," it identifies information such as travel plans, accommodations, and tourist spots. Step 3: The suggestion unit suggests the services identified by the analysis unit to the user. The suggestion unit presents the user with optimal services, such as travel plans, accommodations, and tourist destination information. Step 4: The purchase unit supports the user in purchasing the service proposed by the proposal unit. The purchase unit supports the user in purchasing the service, for example, booking accommodation or purchasing a travel plan. Step 5: The revenue department manages the revenue share from the service provider. The revenue department receives the revenue share from the service provider and distributes it to the system operator.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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, in order to avoid confusion and to 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 reception section for inputting what the user wants to do; an analysis unit that analyzes the information received by the reception unit and identifies an appropriate service; a suggestion unit that suggests the service identified by the analysis unit to a user; a purchasing unit for a user to purchase the service proposed by the proposal unit; A revenue unit that manages revenue distribution from service providers. A system characterized by:

2. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past input history and provide appropriate input completion functions 2. The system of claim 1.

4. The reception unit Based on user input, automatically generate relevant questions to gather more detailed information 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Prioritize localized services based on the user's geographic location 2. The system of claim 1.

7. The reception unit Analyzes users' social media activity and automatically suggests related things to do 2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting analysis algorithms based on the estimated user emotions 2. The system of claim 1.

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