System

Abstract

An object of a system according to an embodiment is to analyze customer behavior and provide personalized content even in the cookieless era.SOLUTION: A system according to an embodiment includes an interactive AI content provider, a first party data collector, and a personalized content provider. The interactive AI content provider analyzes the natural language input from the user and generates an appropriate response based on the content of the input. The first party data-collection unit collects first party AI such as user action histories, purchase histories and access histories via the interactive data-type content-provision unit. The personalized content provider provides personalized content based on the data collected by the first party data collector.SELECTED DRAWING: Figure 1

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 are not yet fully capable of analyzing customer behavior and providing personalized content in the cookie-less era, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze customer behavior and provide personalized content even in the cookie-less era. [Means for solving the problem]

[0006] A system according to an embodiment includes an interactive AI content providing unit, a first-party data collecting unit, and a personalized content providing unit. The interactive AI content providing unit analyzes natural language input from a user and generates an appropriate response based on the content. The first-party data collecting unit collects first-party data such as the user's behavioral history, purchase history, and access history through the interactive AI content providing unit. The personalized content providing unit provides personalized content based on the data collected by the first-party data collecting unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze customer behavior and provide personalized content even in the cookie-less era. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A customer behavior analysis system according to an embodiment of the present invention evolves content that customers use on a daily basis into an interactive AI system, collects first-party data such as customer behavior history, purchase history, and access history, and provides personalized, optimal sponsored content. This enables the customer behavior analysis system to realize new customer behavior analysis and marketing strategies in the cookie-less era.

[0029] A customer behavior analysis system according to an embodiment includes a conversational AI content provider, a first-party data collector, and a personalized content provider. The conversational AI content provider analyzes natural language input from a user and generates an appropriate response based on the content. For example, when a user inputs, "Tell me more about this news story," the generation AI provides detailed information related to the news. The generation AI can also generate accurate answers to user questions. The first-party data collector collects first-party data, such as a user's behavioral history, purchase history, and access history, through the conversational AI content provider. For example, the first-party data collector collects data such as which news articles the user read, which products the user inquired about, and which links the user clicked. The personalized content provider provides personalized content based on the data collected by the first-party data collector. For example, the personalized content provider recommends related news articles and products based on the user's past news articles and purchases. This enables the customer behavior analysis system to realize new customer behavior analysis and marketing strategies in the cookie-less era.

[0030] The conversational AI content providing unit can learn the user's past conversation history and customize the tone and style of the conversation based on the user's preferences and interests. For example, the conversational AI content providing unit uses a generation AI to analyze the user's past conversation history and learn the user's preferred topics and vocabulary. For example, it identifies frequently used phrases and topics of interest to the user and customizes the tone and style of the conversation based on these. This makes it possible to provide a more personalized conversational experience by customizing the tone and style of the conversation based on the user's preferences and interests.

[0031] The conversational AI content provider analyzes the content of the user's conversation and can predict and proactively provide the information the user will want. For example, the conversational AI content provider uses a generation AI to analyze the content of the user's conversation in real time and predict the information the user will want next. For example, if the user is talking about traveling, the system will suggest the next recommended travel destination. This allows the system to predict the information the user will want and proactively provide it, improving user convenience.

[0032] The conversational AI content provider can work with voice assistants and smart speakers to provide information through voice dialogue. For example, the conversational AI content provider can link conversational AI content with a voice assistant, and when a user asks a question by voice, the generation AI provides an appropriate response. For example, when a user asks, "What's the weather like today?", the generation AI provides weather information by voice. This can improve user convenience by working with voice assistants and smart speakers to provide information through voice dialogue.

[0033] The interactive AI content provider can be applied to the field of education to provide customized learning content according to a student's learning progress. The interactive AI content provider can, for example, apply interactive AI content to the field of education to provide customized learning content according to a student's learning progress. For example, the generation AI analyzes a student's past learning history and proposes an individual learning plan. This can be applied to the field of education to provide customized learning content according to a student's learning progress, thereby improving learning effectiveness.

[0034] The first-party data collection unit can analyze a user's behavioral history, identify behavioral patterns, and predict future behavior. For example, the first-party data collection unit uses a generative AI to analyze a user's behavioral history and identify behavioral patterns. For example, it can find that users tend to visit specific websites at specific times of the day. This allows for more effective marketing measures to be implemented by analyzing a user's behavioral history, identifying behavioral patterns, and predicting future behavior.

[0035] The first-party data collection unit can suggest specific promotions to increase the user's purchasing motivation based on the user's purchasing history. For example, the first-party data collection unit uses a generation AI to analyze the user's purchasing history and suggest specific promotions to increase the user's purchasing motivation. For example, the first-party data collection unit can provide discount coupons related to products the user has previously purchased. This makes it possible to increase sales by suggesting specific promotions to increase the user's purchasing motivation based on the user's purchasing history.

[0036] The first-party data collection unit can analyze a user's access history and automatically recommend new content that the user will be interested in. For example, the first-party data collection unit uses a generation AI to analyze a user's access history and automatically recommend new content that the user will be interested in. For example, it can suggest new articles on websites that the user frequently visits. This makes it possible to improve user satisfaction by analyzing a user's access history and automatically recommending new content that the user will be interested in.

[0037] The first-party data collection unit can link the first-party data with a health management app to provide a personalized healthcare plan based on the user's health condition. The first-party data collection unit, for example, links the first-party data with a health management app to provide a personalized healthcare plan based on the user's health condition. For example, the first-party data collection unit may suggest an optimal exercise plan based on the user's exercise history and dietary records. This allows the first-party data to be linked with the health management app to provide a personalized healthcare plan based on the user's health condition, thereby assisting the user in managing their health.

[0038] The first-party data collection unit can link the first-party data with smart home devices to realize automated home management based on the user's lifestyle habits. For example, the first-party data collection unit can link the first-party data with smart home devices to realize automated home management based on the user's lifestyle habits. For example, if the user has a habit of returning home at a specific time, the first-party data collection unit can automatically adjust the lighting and air conditioning to match that time. In this way, linking the first-party data with smart home devices to realize automated home management based on the user's lifestyle habits can improve the user's quality of life.

[0039] The personalized content providing unit learns the user's interests and concerns in real time and can always provide the latest personalized content. For example, the personalized content providing unit uses a generation AI to learn the user's interests and concerns in real time and always provide the latest personalized content. For example, if a user begins to become interested in a new topic, the unit provides the latest information related to that topic. This makes it possible to learn the user's interests and concerns in real time and always provide the latest personalized content, thereby improving user satisfaction.

[0040] The personalized content providing unit can be applied to the entertainment field and recommend movies and music that match the user's preferences. For example, the generation AI analyzes the user's past viewing history and recommends movies and music that match the user's preferences. For example, if the user frequently watches movies of a particular genre, new movies in that genre can be recommended. This can be applied to the entertainment field and can improve user satisfaction by recommending movies and music that match the user's preferences.

[0041] The personalized content providing unit can be applied to travel planning and suggest travel destinations based on the user's past travel history and interests. For example, the personalized content providing unit uses a generation AI to analyze the user's past travel history and suggest travel destinations based on the user's interests. For example, it can recommend new travel destinations related to cities and tourist spots that the user has previously visited. This can be applied to travel planning and suggest travel destinations based on the user's past travel history and interests, thereby improving user satisfaction.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The customer behavior analysis system can also collect a user's health data and provide personalized content based on their health condition. For example, it can recommend health-related articles and recipes based on the user's exercise history and food records. It can also analyze the user's sleep data and provide advice on improving sleep quality. This can support the user's health management by providing personalized content based on the user's health condition.

[0044] The customer behavior analysis system can also provide event information based on the user's hobbies and interests. For example, if the user is interested in music, it can provide information on nearby concerts and live events. If the user is interested in sports, it can provide information on local sporting events and matches. This can improve the quality of the user's life by providing event information based on the user's hobbies and interests.

[0045] The customer behavior analysis system can also predict future purchasing behavior based on a user's purchasing history and provide promotions at the appropriate time. For example, if a user regularly purchases a certain product, the system can predict when the user will repurchase that product and provide discount coupons. It can also recommend new products related to products the user has previously purchased. This allows sales to be increased by predicting future purchasing behavior based on the user's purchasing history and providing promotions at the appropriate time.

[0046] The customer behavior analysis system can also suggest new hobbies and activities that the user may be interested in based on the user's behavioral history. For example, if the user is interested in outdoor activities, it can provide information on new hiking trails and campsites. If the user is interested in cooking, it can provide information on new recipes and cooking classes. In this way, suggesting new hobbies and activities based on the user's behavioral history can improve the user's quality of life.

[0047] Customer behavior analysis systems can also recommend new technologies and gadgets that a user may be interested in based on the user's behavioral history. For example, if a user is interested in smart home devices, they can provide information about new smart home devices. Or, if a user is interested in the latest smartphones, they can provide reviews and purchase links for those smartphones. In this way, recommending new technologies and gadgets based on the user's behavioral history can improve user satisfaction.

[0048] The customer behavior analysis system can also provide new business opportunities and investment information that may interest the user based on the user's behavioral history. For example, if the user is interested in a particular industry, the system can provide the latest trends and investment opportunities in that industry. Also, if the user is interested in startups, the system can provide information on new startups and investment targets. In this way, by providing new business opportunities and investment information based on the user's behavioral history, user satisfaction can be improved.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The conversational AI content provider analyzes the natural language input from the user and generates an appropriate response based on the content. For example, if the user inputs "Tell me more about this news," the AI ​​provider will provide detailed information related to that news. It can also generate accurate answers to the user's questions. Step 2: The first-party data collection unit collects first-party data such as user behavioral history, purchase history, and access history through the conversational AI content provision unit. For example, it collects data such as which news articles users read, which product details they inquired about, and which links they clicked. Step 3: The personalized content provider provides personalized content based on the data collected by the first-party data collector, for example, recommending related news articles or products based on the news articles or products the user has previously read or purchased.

[0051] (Example 2) A customer behavior analysis system according to an embodiment of the present invention evolves content that customers use on a daily basis into an interactive AI system, collects first-party data such as customer behavior history, purchase history, and access history, and provides personalized, optimal sponsored content. This enables the customer behavior analysis system to realize new customer behavior analysis and marketing strategies in the cookie-less era.

[0052] A customer behavior analysis system according to an embodiment includes a conversational AI content provider, a first-party data collector, and a personalized content provider. The conversational AI content provider analyzes natural language input from a user and generates an appropriate response based on the content. For example, when a user inputs, "Tell me more about this news story," the generation AI provides detailed information related to the news. The generation AI can also generate accurate answers to user questions. The first-party data collector collects first-party data, such as a user's behavioral history, purchase history, and access history, through the conversational AI content provider. For example, the first-party data collector collects data such as which news articles the user read, which products the user inquired about, and which links the user clicked. The personalized content provider provides personalized content based on the data collected by the first-party data collector. For example, the personalized content provider recommends related news articles and products based on the user's past news articles and purchases. This enables the customer behavior analysis system to realize new customer behavior analysis and marketing strategies in the cookie-less era.

[0053] The conversational AI content providing unit can learn the user's past conversation history and customize the tone and style of the conversation based on the user's preferences and interests. For example, the conversational AI content providing unit uses a generation AI to analyze the user's past conversation history and learn the user's preferred topics and vocabulary. For example, it identifies frequently used phrases and topics of interest to the user and customizes the tone and style of the conversation based on these. This makes it possible to provide a more personalized conversational experience by customizing the tone and style of the conversation based on the user's preferences and interests.

[0054] The conversational AI content provider can estimate the user's real-time emotional state and generate a response that corresponds to that emotional state. For example, the generation AI analyzes the user's input and voice tone to estimate the user's emotional state in real time. For example, if the user is excited, the AI ​​responds in a calm tone. This allows the AI ​​to generate a response that corresponds to the user's real-time emotional state, providing a more personalized conversational experience.

[0055] The conversational AI content provider analyzes the content of the user's conversation and can predict and proactively provide the information the user will want. For example, the conversational AI content provider uses a generation AI to analyze the content of the user's conversation in real time and predict the information the user will want next. For example, if the user is talking about traveling, the system will suggest the next recommended travel destination. This allows the system to predict the information the user will want and proactively provide it, improving user convenience.

[0056] The conversational AI content provider can work with voice assistants and smart speakers to provide information through voice dialogue. For example, the conversational AI content provider can link conversational AI content with a voice assistant, and when a user asks a question by voice, the generation AI provides an appropriate response. For example, when a user asks, "What's the weather like today?", the generation AI provides weather information by voice. This can improve user convenience by working with voice assistants and smart speakers to provide information through voice dialogue.

[0057] The interactive AI content provider can be applied to the field of education to provide customized learning content according to a student's learning progress. The interactive AI content provider can, for example, apply interactive AI content to the field of education to provide customized learning content according to a student's learning progress. For example, the generation AI analyzes a student's past learning history and proposes an individual learning plan. This can be applied to the field of education to provide customized learning content according to a student's learning progress, thereby improving learning effectiveness.

[0058] The first-party data collection unit can analyze a user's behavioral history, identify behavioral patterns, and predict future behavior. For example, the first-party data collection unit uses a generative AI to analyze a user's behavioral history and identify behavioral patterns. For example, it can find that users tend to visit specific websites at specific times of the day. This allows for more effective marketing measures to be implemented by analyzing a user's behavioral history, identifying behavioral patterns, and predicting future behavior.

[0059] The first-party data collection unit can suggest specific promotions to increase the user's purchasing motivation based on the user's purchasing history. For example, the first-party data collection unit uses a generation AI to analyze the user's purchasing history and suggest specific promotions to increase the user's purchasing motivation. For example, the first-party data collection unit can provide discount coupons related to products the user has previously purchased. This makes it possible to increase sales by suggesting specific promotions to increase the user's purchasing motivation based on the user's purchasing history.

[0060] The first-party data collection unit can analyze a user's access history and automatically recommend new content that the user will be interested in. For example, the first-party data collection unit uses a generation AI to analyze a user's access history and automatically recommend new content that the user will be interested in. For example, it can suggest new articles on websites that the user frequently visits. This makes it possible to improve user satisfaction by analyzing a user's access history and automatically recommending new content that the user will be interested in.

[0061] The first-party data collection unit can link the first-party data with a health management app to provide a personalized healthcare plan based on the user's health condition. The first-party data collection unit, for example, links the first-party data with a health management app to provide a personalized healthcare plan based on the user's health condition. For example, the first-party data collection unit may suggest an optimal exercise plan based on the user's exercise history and dietary records. This allows the first-party data to be linked with the health management app to provide a personalized healthcare plan based on the user's health condition, thereby assisting the user in managing their health.

[0062] The first-party data collection unit can link the first-party data with smart home devices to realize automated home management based on the user's lifestyle habits. For example, the first-party data collection unit can link the first-party data with smart home devices to realize automated home management based on the user's lifestyle habits. For example, if the user has a habit of returning home at a specific time, the first-party data collection unit can automatically adjust the lighting and air conditioning to match that time. In this way, linking the first-party data with smart home devices to realize automated home management based on the user's lifestyle habits can improve the user's quality of life.

[0063] The first-party data collection unit can use the emotion estimation function to collect data based on the user's emotional state and provide content that corresponds to the emotion. The first-party data collection unit, for example, uses the emotion estimation function to collect data based on the user's emotional state. For example, if the user is feeling stressed, the first-party data collection unit records the user's emotional state and provides content that helps the user relax. In this way, by using the emotion estimation function to collect data based on the user's emotional state and providing content that corresponds to the emotion, it is possible to improve user satisfaction.

[0064] The personalized content providing unit learns the user's interests and concerns in real time and can always provide the latest personalized content. For example, the personalized content providing unit uses a generation AI to learn the user's interests and concerns in real time and always provide the latest personalized content. For example, if a user begins to become interested in a new topic, the unit provides the latest information related to that topic. This makes it possible to learn the user's interests and concerns in real time and always provide the latest personalized content, thereby improving user satisfaction.

[0065] The personalized content providing unit can improve user satisfaction by estimating the user's emotional state and providing content that corresponds to the emotion. For example, the personalized content providing unit uses a generation AI to estimate the user's emotional state and provide content that corresponds to the emotion. For example, if the user is feeling stressed, relaxing music or videos can be recommended. In this way, by estimating the user's emotional state and providing content that corresponds to the emotion, user satisfaction can be improved.

[0066] The personalized content providing unit can be applied to the entertainment field and recommend movies and music that match the user's preferences. For example, the generation AI analyzes the user's past viewing history and recommends movies and music that match the user's preferences. For example, if the user frequently watches movies of a particular genre, new movies in that genre can be recommended. This can be applied to the entertainment field and can improve user satisfaction by recommending movies and music that match the user's preferences.

[0067] The personalized content providing unit can be applied to travel planning and suggest travel destinations based on the user's past travel history and interests. For example, the personalized content providing unit uses a generation AI to analyze the user's past travel history and suggest travel destinations based on the user's interests. For example, it can recommend new travel destinations related to cities and tourist spots that the user has previously visited. This can be applied to travel planning and suggest travel destinations based on the user's past travel history and interests, thereby improving user satisfaction.

[0068] The personalized content providing unit can use the emotion estimation function to provide advertisements and promotions that correspond to the user's emotions, thereby maximizing advertising effectiveness. The personalized content providing unit can, for example, use the emotion estimation function to provide advertisements and promotions that correspond to the user's emotions. For example, if the user is relaxed, an advertisement for a relaxation product that matches that emotion is displayed. In this way, the emotion estimation function can be used to provide advertisements and promotions that correspond to the user's emotions, thereby maximizing advertising effectiveness.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The customer behavior analysis system can also collect a user's health data and provide personalized content based on their health condition. For example, it can recommend health-related articles and recipes based on the user's exercise history and food records. It can also analyze the user's sleep data and provide advice on improving sleep quality. This can support the user's health management by providing personalized content based on the user's health condition.

[0071] The customer behavior analysis system can also provide event information based on the user's hobbies and interests. For example, if the user is interested in music, it can provide information on nearby concerts and live events. If the user is interested in sports, it can provide information on local sporting events and matches. This can improve the quality of the user's life by providing event information based on the user's hobbies and interests.

[0072] The customer behavior analysis system can also estimate the user's emotional state and provide feedback according to the emotion. For example, if the user is feeling stressed, it can suggest ways to relax or activities to relieve stress. Or, if the user is happy, it can suggest social media posts to share that emotion. This can improve user satisfaction by providing feedback according to the user's emotional state.

[0073] The customer behavior analysis system can also predict future purchasing behavior based on a user's purchasing history and provide promotions at the appropriate time. For example, if a user regularly purchases a certain product, the system can predict when the user will repurchase that product and provide discount coupons. It can also recommend new products related to products the user has previously purchased. This allows sales to be increased by predicting future purchasing behavior based on the user's purchasing history and providing promotions at the appropriate time.

[0074] The customer behavior analysis system can also estimate the user's emotional state and provide entertainment content that matches the user's emotions. For example, if the user is tired, it can recommend relaxing movies or music. If the user is excited, it can provide action movies or energetic music that matches the user's emotions. This can improve user satisfaction by providing entertainment content that matches the user's emotional state.

[0075] The customer behavior analysis system can also suggest new hobbies and activities that the user may be interested in based on the user's behavioral history. For example, if the user is interested in outdoor activities, it can provide information on new hiking trails and campsites. If the user is interested in cooking, it can provide information on new recipes and cooking classes. In this way, suggesting new hobbies and activities based on the user's behavioral history can improve the user's quality of life.

[0076] The customer behavior analysis system can also estimate the user's emotional state and provide learning content that matches their emotions. For example, if the user lacks concentration, it can provide content that allows them to learn in a short amount of time. Also, if the user wants to increase their motivation, it can provide encouraging messages or success stories that match their emotions. In this way, by providing learning content that matches the user's emotional state, it is possible to improve learning effectiveness.

[0077] Customer behavior analysis systems can also recommend new technologies and gadgets that a user may be interested in based on the user's behavioral history. For example, if a user is interested in smart home devices, they can provide information about new smart home devices. Or, if a user is interested in the latest smartphones, they can provide reviews and purchase links for those smartphones. In this way, recommending new technologies and gadgets based on the user's behavioral history can improve user satisfaction.

[0078] The customer behavior analysis system can also estimate the user's emotional state and suggest travel plans that correspond to the user's emotions. For example, if the user wants to relax, it can recommend quiet resorts or hot springs. If the user is seeking adventure, it can offer active travel destinations and activities that match the user's emotions. This can improve user satisfaction by suggesting travel plans that correspond to the user's emotional state.

[0079] The customer behavior analysis system can also provide new business opportunities and investment information that may interest the user based on the user's behavioral history. For example, if the user is interested in a particular industry, the system can provide the latest trends and investment opportunities in that industry. Also, if the user is interested in startups, the system can provide information on new startups and investment targets. In this way, by providing new business opportunities and investment information based on the user's behavioral history, user satisfaction can be improved.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The conversational AI content provider analyzes the natural language input from the user and generates an appropriate response based on the content. For example, if the user inputs "Tell me more about this news," the AI ​​provider will provide detailed information related to that news. It can also generate accurate answers to the user's questions. Step 2: The first-party data collection unit collects first-party data such as user behavioral history, purchase history, and access history through the conversational AI content provision unit. For example, it collects data such as which news articles users read, which product details they inquired about, and which links they clicked. Step 3: The personalized content provider provides personalized content based on the data collected by the first-party data collector, for example, recommending related news articles or products based on the news articles or products the user has previously read or purchased.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] 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. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An interactive AI content provider that analyzes natural language input from users and generates appropriate responses based on the content of the input; a first-party data collection unit that collects first-party data such as user behavior history, purchase history, and access history through the conversation AI content providing unit; a personalized content providing unit that provides personalized content based on the data collected by the first-party data collecting unit. A system characterized by:

2. The interactive AI content providing unit Estimating a real-time emotional state of the user and generating a response according to the emotional state.

2. The system of claim 1.

3. The interactive AI content providing unit Linking with voice assistants and smart speakers to provide information through voice interaction 2. The system of claim 1.

4. The first-party data collection unit: Linking the first-party data with a health management app to provide a personalized health care plan based on the user's health condition 2. The system of claim 1.

5. The personalized content providing unit The emotional state of the user is estimated, and content corresponding to the emotional state is provided, thereby improving satisfaction of the user.

2. The system of claim 1.

6. The interactive AI content providing unit It is applied to the education field to provide customized learning content according to students' learning progress.

2. The system of claim 1.

7. The first-party data collection unit: Using an emotion estimation function, data is collected based on the user's emotional state, and content is provided according to the user's emotions.

2. The system of claim 1.

8. The personalized content providing unit Using emotion estimation function, advertisements and promotions are provided according to the user's emotions, maximizing advertising effectiveness.

2. The system of claim 1.

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