system

Abstract

The system according to this embodiment aims to automatically generate context based on the user's behavior history and provide it to the chat AI. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's behavior history. The analysis unit analyzes the behavior history collected by the collection unit. The generation unit generates a context based on the results analyzed by the analysis unit. The provision unit provides the context generated by the generation unit to the chat AI.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the generation of context based on the user's behavior history is often performed manually and is not efficient.

[0005] The system according to the embodiment aims to automatically generate context based on the user's behavior history and provide it to the chat AI.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's behavior history. The analysis unit analyzes the behavior history collected by the collection unit. The generation unit generates a context based on the results analyzed by the analysis unit. The provision unit provides the context generated by the generation unit to the chat AI. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate context based on the user's behavior history and provide it to the chat AI. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The context generation system according to an embodiment of the present invention is a system in which a generating AI automatically generates context when a user converses with a chat AI. When a user requests the chat AI to "create a travel plan that's perfect for me!", the generating AI analyzes the user's behavioral history on social networking platforms and e-commerce sites and proposes an optimal travel plan based on information such as the user's family structure, nearest station, car ownership status, and past travel history. For example, it can suggest tourist destinations the user has never visited before or tourist destinations suitable for families. The advantage of this system is that users can efficiently use the chat AI without having to type long messages. Furthermore, by utilizing diverse data provided by social networking platforms and e-commerce sites, it has a competitive advantage that other companies cannot imitate. For example, it can provide more personalized context based on the user's interests, purchase history, and location information. This system can be applied not only to travel planning but also to various other scenarios. For example, by generating context based on the user's behavioral history, it can provide more convenient and efficient services such as restaurant reservations and shopping advice. In this way, the context generation system automatically generates context based on the user's behavioral history and provides it to the chat AI, allowing users to use the chat AI efficiently.

[0029] The context generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's behavioral history. Behavioral history includes, but is not limited to, website browsing history and application usage history. For example, the collection unit collects website browsing history from browser history data. The collection unit can also collect application usage history from mobile device log data. Furthermore, the collection unit can also collect purchase history from e-commerce sites. For example, the collection unit obtains data on products previously purchased by the user from the database of the e-commerce site. The analysis unit analyzes the behavioral history collected by the collection unit. The analysis is performed using, but is not limited to, data mining techniques and statistical analysis methods. For example, the analysis unit uses data mining techniques to analyze the user's behavioral patterns. The analysis unit can also use statistical analysis methods to identify the user's interests. Furthermore, the analysis unit can cluster the user's behavioral history using machine learning algorithms. The generation unit generates context based on the results analyzed by the analysis unit. The generation is performed, for example, using a generative AI, but is not limited to such examples. For example, the generation unit uses a generative AI to generate context based on the user's interests. The generation unit can also use a generative AI to generate context based on the user's past behavior patterns. The generation unit can also use a generative AI to generate context based on the user's location information. The provisioning unit provides the context generated by the generation unit to the chat AI. The provision is performed, for example, via an API, but is not limited to such examples. For example, the provisioning unit sends the generated context to the chat AI's API. The provisioning unit can also provide the generated context through a web service. The provisioning unit can also provide the generated context through a mobile application. As a result, the context generation system according to the embodiment automatically generates context based on the user's behavior history and provides it to the chat AI, allowing the user to use the chat AI efficiently.Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and provision unit may be performed using AI, for example, or without AI. For example, the collection unit can use AI to analyze website browsing history and collect relevant data in order to collect user behavior history. The analysis unit can use AI to apply data mining techniques to analyze the collected behavior history and identify user behavior patterns. The generation unit can use generation AI to generate context based on user interests in order to generate context based on the analysis results. The provision unit can use AI to select the optimal provision method and provide the generated context to the chat AI.

[0030] The data collection unit collects user behavior history. This behavior history includes, but is not limited to, website browsing history and app usage history. For example, the data collection unit collects website browsing history from browser history data. Specifically, browser history data includes the URLs of web pages visited by the user, the date and time of visit, and the duration of stay. By collecting this data, it is possible to understand what kind of information the user is interested in. The data collection unit can also collect app usage history from mobile device log data. Mobile device log data records app launch time, usage time, and usage frequency, and by collecting this data, it is possible to understand which apps the user uses and to what extent. Furthermore, the data collection unit can also collect purchase history from e-commerce sites. For example, the data collection unit obtains data on products that the user has purchased in the past from the database of an e-commerce site. Purchase history includes the type of product purchased, price, and date and time of purchase, and by collecting this data, it is possible to understand the user's purchasing trends and interests. The data collection unit collects behavior history from these diverse data sources and provides basic data for understanding user behavior patterns in detail. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by enhancing data collection during specific time periods or event periods, more detailed behavioral history can be obtained. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the behavioral history collected by the data collection unit. The analysis is performed using, for example, data mining techniques and statistical analysis methods, but is not limited to these examples. Specifically, data mining techniques are used to analyze user behavior patterns. Data mining techniques include clustering, association rules, and decision trees, and by using these to analyze user behavior data, common patterns and trends can be identified. The analysis unit can also use statistical analysis methods to identify user interests. For example, regression analysis and principal component analysis can be used to extract categories and topics of interest from user behavior data. Furthermore, the analysis unit can cluster user behavior history using machine learning algorithms. Clustering algorithms include K-means and hierarchical clustering, which can be used to group users based on similar behavioral patterns. This allows the analysis unit to quickly and accurately analyze the collected data and understand user behavior patterns and interests. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and predictions. For example, past behavioral data can be used to predict changes in user interests over a specific period, which can then be used as a reference for future context generation. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal behavior, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term trend analysis and anomaly detection, improving the overall reliability and accuracy of the system.

[0032] The generation unit generates context based on the results analyzed by the analysis unit. Generation is performed using, for example, generative AI, but is not limited to such examples. Specifically, it generates context based on the user's interests using generative AI. Generative AI can use natural language processing techniques to generate text and information based on the user's behavioral history and interests. For example, it can generate relevant articles and product information based on data of web pages the user has previously viewed and products they have purchased. The generation unit can also use generative AI to generate context based on the user's past behavioral patterns. For example, if a user tends to perform a particular activity at a specific time of day, it can generate information related to that time period. Furthermore, the generation unit can use generative AI to generate context based on the user's location information. For example, if a user is in a specific location, it can provide information and services related to that location. The generative AI integrates this data and uses advanced algorithms to generate the most relevant context for the user. This allows the generation unit to generate and provide highly accurate context based on the user's behavioral history and interests. Furthermore, the generation unit can evaluate the quality of the generated context and make corrections or improvements as needed. For example, if the generated context does not meet the user's expectations, the generation AI's algorithm can be adjusted to generate a more appropriate context. This allows the generation unit to consistently provide high-quality context and improve user satisfaction.

[0033] The provider unit provides the context generated by the generator unit to the chat AI. This provision is, for example, via an API, but is not limited to this example. Specifically, the provider unit sends the generated context to the chat AI's API. The chat AI then interacts with the user based on the provided context. For example, if a user asks the chat AI a question, the chat AI can generate an appropriate answer based on the context generated by the provider unit. The provider unit can also provide the generated context through web services. For example, the generated context can be displayed on a website or web application when a user performs a specific action. Furthermore, the provider unit can provide the generated context through mobile applications. For example, relevant information can be provided to the user in real time using the notification function of a mobile application. Using these diverse means, the provider unit can quickly and effectively deliver the generated context to the user. Furthermore, the provider unit can collect user feedback and continuously improve the accuracy and effectiveness of the delivery method. For example, it can analyze how users reacted to the provided context and optimize the delivery method. The provider unit can also reliably transmit information using multiple communication methods. For example, in addition to APIs, web services, and mobile applications, important information can be reliably delivered by using email and SMS in conjunction. This allows the service provider to quickly and reliably provide context to users, improving user convenience.

[0034] The interest collection unit can collect users' interests and purchase history. For example, the interest collection unit can collect survey results. For example, the interest collection unit can collect data from surveys answered by users and identify their interests. The interest collection unit can also collect social media activity. For example, the interest collection unit can analyze content posted by users on social media and identify their interests. The interest collection unit can also collect purchase data from e-commerce sites. For example, the interest collection unit can collect data on products that users have purchased in the past and identify their interests. In this way, the interest collection unit can generate a more personalized context by collecting users' interests and purchase history. Some or all of the above processing in the interest collection unit may be performed using AI, for example, or not using AI. For example, the interest collection unit can analyze social media posts using AI and identify users' interests.

[0035] The location information collection unit can collect the user's location information. For example, the location information collection unit collects GPS data. For example, the location information collection unit obtains GPS data from the user's smartphone and identifies the location. The location information collection unit can also collect Wi-Fi location information. For example, the location information collection unit collects information about the Wi-Fi network to which the user is connected and identifies the location. The location information collection unit can also collect location information using Bluetooth® beacons. For example, the location information collection unit receives signals from Bluetooth beacons near the user and identifies the location. This allows the location information collection unit to generate a more appropriate context by collecting the user's location information. Some or all of the above processing in the location information collection unit may be performed using AI, for example, or without AI. For example, the location information collection unit can analyze GPS data with AI to identify the user's location.

[0036] The verification unit can allow the user to confirm the generated context. For example, the verification unit can display a confirmation screen to the user. For example, the verification unit can display a screen showing the generated context on the user's device and ask the user for confirmation. The verification unit can also collect user feedback. For example, the verification unit can provide an interface for the user to provide feedback on the generated context. The verification unit can also save the user's confirmation results. For example, the verification unit can save the context data confirmed by the user so that it can be referenced later. In this way, the verification unit can provide context that aligns with the user's intentions by allowing the user to confirm the generated context. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can analyze user feedback with AI and evaluate the quality of the generated context.

[0037] The data collection unit can collect behavioral history on social networking platforms and e-commerce sites. For example, the data collection unit collects data from social networking platforms. For example, the data collection unit collects data such as content posted by users on social networking platforms, likes, and comments. The data collection unit can also collect data from e-commerce sites. For example, the data collection unit collects data on products purchased by users on e-commerce sites, as well as browsing history. The data collection unit can also collect user behavioral history in real time. For example, the data collection unit collects data on the user's current activities. This allows the data collection unit to utilize a wider variety of data by collecting behavioral history on social networking platforms and e-commerce sites. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can analyze data from social networking platforms using AI to identify user behavioral history.

[0038] The analysis unit can analyze the collected behavioral history and identify information such as the user's family structure, nearest train station, car ownership status, and past travel history. For example, the analysis unit can analyze the collected behavioral history using data mining techniques. For example, the analysis unit can analyze the user's social media posts to identify the user's family structure. The analysis unit can also analyze the user's location data to identify the user's nearest train station. The analysis unit can also analyze the user's purchase history on e-commerce sites to identify the user's car ownership status. The analysis unit can also analyze data from the user's travel booking sites to identify the user's past travel history. This allows the analysis unit to generate a more accurate context by identifying detailed information about the user. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the collected behavioral history using AI to identify information such as the user's family structure and nearest train station.

[0039] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from social networking services (SNS) platforms that the user has frequently used in the past. For example, the data collection unit can collect data from SNS platforms where the user has made many posts in the past. The data collection unit can also concentrate data collection during specific time periods based on the user's past behavior history. For example, the data collection unit can collect data on when the user has performed many activities during specific time periods in the past. The data collection unit can also analyze the user's past behavior patterns and select the most efficient data collection method. For example, the data collection unit can analyze the user's past behavior patterns to identify the optimal timing and method of data collection. This enables the data collection unit to efficiently collect data by analyzing past behavior history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze past behavior history using AI and select the optimal data collection method.

[0040] The data collection unit can filter the collected behavioral history based on the user's current living situation and areas of interest. For example, the data collection unit can prioritize collecting behavioral history related to topics the user is currently interested in. For example, the data collection unit can collect data related to topics the user has recently searched for. The data collection unit can also adjust the data it collects according to the user's living situation (e.g., at work, on vacation). For example, the data collection unit can collect data on activities the user performed while at work. The data collection unit can also filter and collect highly relevant behavioral history based on the user's areas of interest. For example, the data collection unit can collect data related to hobbies and topics the user is interested in. This allows the data collection unit to collect highly relevant data by filtering the data based on the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can analyze the user's areas of interest using AI and filter and collect highly relevant behavioral history.

[0041] The data collection unit can prioritize the collection of highly relevant history by considering the user's geographical location when collecting behavioral history. For example, the data collection unit can prioritize the collection of behavioral history related to the user's current location. For example, the data collection unit can collect data on activities performed around the user's current location. The data collection unit can also collect highly relevant behavioral history based on the user's past location information. For example, the data collection unit can collect data related to places the user has visited in the past. The data collection unit can also prioritize the collection of behavioral history related to places the user has visited. For example, the data collection unit can collect data on tourist attractions and restaurants the user has visited in the past. In this way, the data collection unit can collect highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze the user's geographical location information using AI and prioritize the collection of highly relevant behavioral history.

[0042] The data collection unit can analyze a user's social media activity and collect relevant history when collecting behavioral history. For example, the data collection unit can prioritize collecting behavioral history related to content recently posted by the user. For example, the data collection unit can collect data related to content recently posted by the user. The data collection unit can also collect behavioral history based on the activity of accounts that the user follows. For example, the data collection unit can collect content posted by accounts that the user follows. The data collection unit can also collect behavioral history related to groups and events that the user participates in. For example, the data collection unit can collect data related to groups and events that the user participates in. This allows the data collection unit to collect highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can analyze a user's social media activity using AI and collect relevant behavioral history.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral history. For example, the analysis unit performs a detailed analysis on highly important behavioral history. For example, the analysis unit performs a detailed data analysis on the user's important behavioral history. The analysis unit can also perform a simplified analysis on less important behavioral history. For example, the analysis unit performs a simple statistical analysis on the user's less important behavioral history. The analysis unit can also determine the priority of the analysis according to the importance of the behavioral history. For example, the analysis unit prioritizes the analysis of highly important behavioral history and postpones the analysis of less important behavioral history. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the behavioral history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to analyze the importance of behavioral history and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the behavioral history during analysis. For example, the analysis unit can apply a travel-specific analysis algorithm to behavioral history related to travel. For example, the analysis unit can apply a travel-specific data analysis algorithm to a user's travel history. The analysis unit can also apply a purchase behavior analysis algorithm to behavioral history related to shopping. For example, the analysis unit can apply a purchase behavior analysis algorithm to a user's purchase history. The analysis unit can also apply a social media analysis algorithm to behavioral history related to social media activity. For example, the analysis unit can apply a social media analysis algorithm to a user's social media activity. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the category of the behavioral history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the categories of behavioral history using AI and apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the timing of behavioral history submissions. For example, the analysis unit may prioritize analyzing recent behavioral history. For example, the analysis unit may prioritize analyzing data on activities the user has recently performed. The analysis unit can also concentrate its analysis on a specific period. For example, the analysis unit may concentrate its analysis on data on activities the user performed during a specific period. Furthermore, the analysis unit can adjust the analysis schedule based on the timing of behavioral history submissions. For example, the analysis unit may adjust the analysis schedule based on when the user submitted their behavioral history. This allows the analysis unit to perform efficient analysis by determining the priority of analysis based on the timing of behavioral history submissions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit may use AI to analyze the timing of behavioral history submissions and determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the behavioral history during analysis. For example, the analysis unit may prioritize analyzing highly relevant behavioral history. For example, the analysis unit may prioritize analyzing behavioral history related to the user's current interests. The analysis unit can also postpone analyzing less relevant behavioral history. For example, the analysis unit may postpone analyzing behavioral history that is not relevant to the user's interests. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the behavioral history. For example, the analysis unit may dynamically adjust the order of analysis based on the user's interests and behavioral patterns. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the behavioral history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the relevance of behavioral history using AI and adjust the order of analysis.

[0047] The generation unit can adjust the level of detail of the generated context based on the importance of the behavioral history during generation. For example, the generation unit can generate a detailed context based on high-importance behavioral history. For example, the generation unit can generate a context that includes detailed explanations and information for important user behavioral history. The generation unit can also generate a simplified context based on low-importance behavioral history. For example, the generation unit can generate a context that includes simple explanations and information for less important user behavioral history. The generation unit can also determine the priority of the contexts to be generated according to the importance of the behavioral history. For example, the generation unit can prioritize the generation of contexts based on high-importance behavioral history and postpone the generation of contexts based on low-importance behavioral history. This allows the generation unit to efficiently generate contexts by adjusting the level of detail of the context based on the importance of the behavioral history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can analyze the importance of the behavioral history using a generation AI and adjust the level of detail of the generated context.

[0048] The generation unit can apply different generation algorithms depending on the category of the behavioral history during generation. For example, the generation unit can apply a travel-specific generation algorithm to behavioral history related to travel. For example, the generation unit can apply a travel-specific generation algorithm to a user's travel history. The generation unit can also apply a purchase behavior generation algorithm to behavioral history related to shopping. For example, the generation unit can apply a purchase behavior generation algorithm to a user's purchase history. The generation unit can also apply a social media generation algorithm to behavioral history related to social media activity. For example, the generation unit can apply a social media generation algorithm to a user's social media activity. This allows the generation unit to generate more accurate context by applying different generation algorithms depending on the category of the behavioral history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can analyze the category of the behavioral history using a generation AI and apply an appropriate generation algorithm.

[0049] The generation unit can determine the priority of contexts to generate based on the timing of behavioral history submissions during generation. For example, the generation unit can prioritize generating contexts based on recent behavioral history. For example, the generation unit can prioritize generating contexts based on data of the user's recent activities. The generation unit can also concentrate on generating contexts based on behavioral history for a specific period. For example, the generation unit can generate contexts based on data of the user's activities during a specific period. The generation unit can also adjust the schedule of contexts to be generated based on the timing of behavioral history submissions. For example, the generation unit can adjust the schedule of contexts to be generated based on when the user submitted their behavioral history. This enables efficient context generation by allowing the generation unit to prioritize contexts based on the timing of behavioral history submissions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can analyze the timing of behavioral history submissions using a generation AI and determine the priority of contexts to be generated.

[0050] The generation unit can adjust the order of contexts generated based on the relevance of behavioral history during generation. For example, the generation unit can preferentially generate contexts based on highly relevant behavioral history. For example, the generation unit can preferentially generate contexts based on behavioral history related to the user's current interests. The generation unit can also postpone the generation of contexts based on less relevant behavioral history. For example, the generation unit can postpone the generation of contexts based on behavioral history that is less relevant to the user's interests. Furthermore, the generation unit can dynamically adjust the order of contexts generated based on the relevance of behavioral history. For example, the generation unit can dynamically adjust the order of contexts generated based on the user's interests and behavioral patterns. This enables efficient context generation by adjusting the order of contexts generated based on the relevance of behavioral history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can analyze the relevance of behavioral history using a generation AI and adjust the order of contexts to be generated.

[0051] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. For example, the service provider may select the optimal display method based on the display methods that the user has previously preferred. The service provider may also suggest a specific display method based on the user's past operation history. For example, the service provider may analyze the user's past operation history and suggest a specific display method. The service provider may also dynamically adjust the optimal display method based on the user's operation history. For example, the service provider may dynamically adjust the optimal display method based on the user's operation history. In this way, the service provider can provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may analyze the user's past operation history using AI and select the optimal display method.

[0052] The service provider can select the optimal display method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, when the user is using a smartphone, the service provider can provide a text display optimized for the screen size. The service provider can also provide a display method optimized for a larger screen if the user is using a tablet. For example, when the user is using a tablet, the service provider can provide a graphical display optimized for a larger screen. The service provider can also provide a concise and highly visible display method if the user is using a smartwatch. For example, when the user is using a smartwatch, the service provider can provide a concise and highly visible text display. In this way, the service provider can provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can analyze the user's device information with AI and select the optimal display method.

[0053] The interest collection unit can analyze a user's past interest history and select the optimal collection method. For example, the interest collection unit can prioritize collecting topics that the user has frequently shown interest in in the past. For example, the interest collection unit can collect data related to topics that the user has shown a lot of interest in in the past. The interest collection unit can also concentrate collection during specific time periods based on the user's past interest history. For example, the interest collection unit can collect data that the user has shown a lot of interest in during specific time periods in the past. The interest collection unit can also analyze a user's past interest patterns and select the most efficient collection method. For example, the interest collection unit can analyze a user's past interest patterns to identify the optimal collection timing and method. This enables the interest collection unit to efficiently collect data by analyzing past interest history. Some or all of the above processing in the interest collection unit may be performed using AI, for example, or without AI. For example, the interest collection unit can analyze past interest history using AI and select the optimal collection method.

[0054] The interest collection unit can prioritize collecting highly relevant interests by considering the user's geographical location information when collecting interests. For example, the interest collection unit can prioritize collecting interests related to the user's current location. For example, the interest collection unit can collect data related to activities the user has performed around their current location. The interest collection unit can also collect highly relevant interests based on the user's past location information. For example, the interest collection unit can collect data related to places the user has visited in the past. The interest collection unit can also prioritize collecting interests related to places the user has visited. For example, the interest collection unit can collect data related to tourist attractions or restaurants the user has visited in the past. In this way, the interest collection unit can collect highly relevant data by considering the user's geographical location information. Some or all of the above processing in the interest collection unit may be performed using AI, for example, or without AI. For example, the interest collection unit can analyze the user's geographical location information using AI and prioritize collecting highly relevant interests.

[0055] The location information collection unit can analyze the user's past location history and select the optimal collection method. For example, the location information collection unit can prioritize collecting location information of places the user has frequently visited in the past. For example, the location information collection unit can collect data of places the user has visited many times in the past. The location information collection unit can also concentrate collection during specific time periods based on the user's past location history. For example, the location information collection unit can collect data of places the user has visited many times during specific time periods in the past. The location information collection unit can also analyze the user's past location patterns and select the most efficient collection method. For example, the location information collection unit can analyze the user's past location patterns to identify the optimal collection timing and method. This enables the location information collection unit to efficiently collect data by analyzing past location history. Some or all of the above processing in the location information collection unit may be performed using AI, for example, or without AI. For example, the location information collection unit can analyze past location history using AI and select the optimal collection method.

[0056] The location information collection unit can prioritize the collection of highly relevant location information by considering the user's geographical location when collecting location information. For example, the location information collection unit can prioritize the collection of location information related to the user's current location. For example, the location information collection unit can collect data related to activities performed by the user around their current location. The location information collection unit can also collect highly relevant location information based on the user's past location information. For example, the location information collection unit can collect data related to places the user has visited in the past. The location information collection unit can also prioritize the collection of location information related to places the user has visited. For example, the location information collection unit can collect data related to tourist attractions or restaurants the user has visited in the past. In this way, the location information collection unit can collect highly relevant data by considering the user's geographical location information. Some or all of the above processing in the location information collection unit may be performed using AI, for example, or without AI. For example, the location information collection unit can analyze the user's geographical location information using AI and prioritize the collection of highly relevant location information.

[0057] The verification unit can select the optimal display method by referring to the user's past operation history during verification. For example, the verification unit may prioritize providing display methods that the user has previously preferred. For example, the verification unit may select the optimal display method based on the display methods the user has previously preferred. The verification unit can also suggest a specific display method based on the user's past operation history. For example, the verification unit may analyze the user's past operation history and suggest a specific display method. The verification unit can also dynamically adjust the optimal display method based on the user's operation history. For example, the verification unit may dynamically adjust the optimal display method based on the user's operation history. In this way, the verification unit can provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit may analyze the user's past operation history using AI and select the optimal display method.

[0058] The verification unit can select the optimal display method by considering the user's device information during verification. For example, if the user is using a smartphone, the verification unit can provide a display method that matches the screen size. For example, when the user is using a smartphone, the verification unit can provide a text display optimized for the screen size. The verification unit can also provide a display method optimized for a larger screen if the user is using a tablet. For example, when the user is using a tablet, the verification unit can provide a graphical display optimized for a larger screen. The verification unit can also provide a concise and highly visible display method if the user is using a smartwatch. For example, when the user is using a smartwatch, the verification unit can provide a concise and highly visible text display. In this way, the verification unit can provide the optimal display method by considering the user's device information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can analyze the user's device information using AI and select the optimal display method.

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

[0060] The data collection unit can collect not only the user's behavioral history but also their health data. For example, it can collect heart rate, steps, and sleep data from the user's smartwatch or fitness tracker. It can also collect the user's food and exercise records. This allows the data collection unit to generate more personalized context based on the user's health status. For example, if the user is feeling stressed, it can suggest a relaxing travel plan. If the user prefers healthy eating, it can suggest health-conscious restaurants.

[0061] The generation unit can generate context based on the user's hobbies and skills, in addition to their behavioral history. For example, if the user likes music, the generation unit can suggest travel plans that include information on music festivals and live concerts. Similarly, if the user likes sports, it can suggest travel plans that include sporting events and activities. Furthermore, if the user is interested in art and culture, it can suggest travel plans that include museums and historical sites. This allows the generation unit to provide more personalized context based on the user's hobbies and skills.

[0062] The data collection unit can collect user social network information in addition to user behavior history. For example, it can collect data from the social media accounts of the user's friends and family. It can also collect information about groups and events the user is participating in. This allows the data collection unit to generate more personalized context based on the user's social network information. For example, if the user is traveling with friends, it can suggest a travel plan based on the friends' interests. Similarly, if the user is traveling with family, it can suggest a travel plan that includes activities the whole family can enjoy.

[0063] The generation unit can generate context based on the user's behavior history as well as their past feedback. For example, it can analyze feedback previously provided by the user and generate context based on the user's preferences and requests. It can also suggest similar content and services based on content and services the user has previously rated. Furthermore, it can consider areas where the user was dissatisfied in the past and provide improved context. In this way, the generation unit can leverage the user's past feedback to provide more satisfying context.

[0064] The data collection unit can collect user device information in addition to user behavior history. For example, it can collect information on the type and settings of the device the user is using. It can also collect usage and performance data of the user's device. This allows the data collection unit to generate more appropriate context based on the user's device information. For example, if the user is using a smartphone, it can provide a context optimized for smartphones. If the user is using a tablet, it can provide a context optimized for tablets. Furthermore, if the user is using a smartwatch, it can provide a context optimized for smartwatches.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The collection unit collects the user's behavioral history. This behavioral history includes website browsing history, app usage history, and e-commerce site purchase history. For example, the collection unit collects website browsing history from browser history data, app usage history from mobile device log data, and purchase history from e-commerce site databases. Step 2: The analysis unit analyzes the behavioral history collected by the collection unit. The analysis is performed using data mining techniques, statistical analysis methods, and machine learning algorithms. For example, the analysis unit uses data mining techniques to analyze user behavior patterns, statistical analysis methods to identify user interests, and machine learning algorithms to cluster the behavioral history. Step 3: The generation unit generates context based on the results analyzed by the analysis unit. Generation is performed using a generation AI, which generates context based on the user's interests, past behavior patterns, and location information. Step 4: The providing unit provides the context generated by the generating unit to the chat AI. The provision is done via API, sending the generated context to the chat AI's API. It can also be provided via web services or mobile applications.

[0067] (Example of form 2) The context generation system according to an embodiment of the present invention is a system in which a generating AI automatically generates context when a user converses with a chat AI. When a user requests the chat AI to "create a travel plan that's perfect for me!", the generating AI analyzes the user's behavioral history on social networking platforms and e-commerce sites and proposes an optimal travel plan based on information such as the user's family structure, nearest station, car ownership status, and past travel history. For example, it can suggest tourist destinations the user has never visited before or tourist destinations suitable for families. The advantage of this system is that users can efficiently use the chat AI without having to type long messages. Furthermore, by utilizing diverse data provided by social networking platforms and e-commerce sites, it has a competitive advantage that other companies cannot imitate. For example, it can provide more personalized context based on the user's interests, purchase history, and location information. This system can be applied not only to travel planning but also to various other scenarios. For example, by generating context based on the user's behavioral history, it can provide more convenient and efficient services such as restaurant reservations and shopping advice. In this way, the context generation system automatically generates context based on the user's behavioral history and provides it to the chat AI, allowing users to use the chat AI efficiently.

[0068] The context generation system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's behavioral history. Behavioral history includes, but is not limited to, website browsing history and application usage history. For example, the collection unit collects website browsing history from browser history data. The collection unit can also collect application usage history from mobile device log data. Furthermore, the collection unit can also collect purchase history from e-commerce sites. For example, the collection unit obtains data on products previously purchased by the user from the database of the e-commerce site. The analysis unit analyzes the behavioral history collected by the collection unit. The analysis is performed using, but is not limited to, data mining techniques and statistical analysis methods. For example, the analysis unit uses data mining techniques to analyze the user's behavioral patterns. The analysis unit can also use statistical analysis methods to identify the user's interests. Furthermore, the analysis unit can cluster the user's behavioral history using machine learning algorithms. The generation unit generates context based on the results analyzed by the analysis unit. The generation is performed, for example, using a generative AI, but is not limited to such examples. For example, the generation unit uses a generative AI to generate context based on the user's interests. The generation unit can also use a generative AI to generate context based on the user's past behavior patterns. The generation unit can also use a generative AI to generate context based on the user's location information. The provisioning unit provides the context generated by the generation unit to the chat AI. The provision is performed, for example, via an API, but is not limited to such examples. For example, the provisioning unit sends the generated context to the chat AI's API. The provisioning unit can also provide the generated context through a web service. The provisioning unit can also provide the generated context through a mobile application. As a result, the context generation system according to the embodiment automatically generates context based on the user's behavior history and provides it to the chat AI, allowing the user to use the chat AI efficiently.Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and provision unit may be performed using AI, for example, or without AI. For example, the collection unit can use AI to analyze website browsing history and collect relevant data in order to collect user behavior history. The analysis unit can use AI to apply data mining techniques to analyze the collected behavior history and identify user behavior patterns. The generation unit can use generation AI to generate context based on user interests in order to generate context based on the analysis results. The provision unit can use AI to select the optimal provision method and provide the generated context to the chat AI.

[0069] The data collection unit collects user behavior history. This behavior history includes, but is not limited to, website browsing history and app usage history. For example, the data collection unit collects website browsing history from browser history data. Specifically, browser history data includes the URLs of web pages visited by the user, the date and time of visit, and the duration of stay. By collecting this data, it is possible to understand what kind of information the user is interested in. The data collection unit can also collect app usage history from mobile device log data. Mobile device log data records app launch time, usage time, and usage frequency, and by collecting this data, it is possible to understand which apps the user uses and to what extent. Furthermore, the data collection unit can also collect purchase history from e-commerce sites. For example, the data collection unit obtains data on products that the user has purchased in the past from the database of an e-commerce site. Purchase history includes the type of product purchased, price, and date and time of purchase, and by collecting this data, it is possible to understand the user's purchasing trends and interests. The data collection unit collects behavior history from these diverse data sources and provides basic data for understanding user behavior patterns in detail. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, by enhancing data collection during specific time periods or event periods, more detailed behavioral history can be obtained. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0070] The analysis unit analyzes the behavioral history collected by the data collection unit. The analysis is performed using, for example, data mining techniques and statistical analysis methods, but is not limited to these examples. Specifically, data mining techniques are used to analyze user behavior patterns. Data mining techniques include clustering, association rules, and decision trees, and by using these to analyze user behavior data, common patterns and trends can be identified. The analysis unit can also use statistical analysis methods to identify user interests. For example, regression analysis and principal component analysis can be used to extract categories and topics of interest from user behavior data. Furthermore, the analysis unit can cluster user behavior history using machine learning algorithms. Clustering algorithms include K-means and hierarchical clustering, which can be used to group users based on similar behavioral patterns. This allows the analysis unit to quickly and accurately analyze the collected data and understand user behavior patterns and interests. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and predictions. For example, past behavioral data can be used to predict changes in user interests over a specific period, which can then be used as a reference for future context generation. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal behavior, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term trend analysis and anomaly detection, improving the overall reliability and accuracy of the system.

[0071] The generation unit generates context based on the results analyzed by the analysis unit. Generation is performed using, for example, generative AI, but is not limited to such examples. Specifically, it generates context based on the user's interests using generative AI. Generative AI can use natural language processing techniques to generate text and information based on the user's behavioral history and interests. For example, it can generate relevant articles and product information based on data of web pages the user has previously viewed and products they have purchased. The generation unit can also use generative AI to generate context based on the user's past behavioral patterns. For example, if a user tends to perform a particular activity at a specific time of day, it can generate information related to that time period. Furthermore, the generation unit can use generative AI to generate context based on the user's location information. For example, if a user is in a specific location, it can provide information and services related to that location. The generative AI integrates this data and uses advanced algorithms to generate the most relevant context for the user. This allows the generation unit to generate and provide highly accurate context based on the user's behavioral history and interests. Furthermore, the generation unit can evaluate the quality of the generated context and make corrections or improvements as needed. For example, if the generated context does not meet the user's expectations, the generation AI's algorithm can be adjusted to generate a more appropriate context. This allows the generation unit to consistently provide high-quality context and improve user satisfaction.

[0072] The provider unit provides the context generated by the generator unit to the chat AI. This provision is, for example, via an API, but is not limited to this example. Specifically, the provider unit sends the generated context to the chat AI's API. The chat AI then interacts with the user based on the provided context. For example, if a user asks the chat AI a question, the chat AI can generate an appropriate answer based on the context generated by the provider unit. The provider unit can also provide the generated context through web services. For example, the generated context can be displayed on a website or web application when a user performs a specific action. Furthermore, the provider unit can provide the generated context through mobile applications. For example, relevant information can be provided to the user in real time using the notification function of a mobile application. Using these diverse means, the provider unit can quickly and effectively deliver the generated context to the user. Furthermore, the provider unit can collect user feedback and continuously improve the accuracy and effectiveness of the delivery method. For example, it can analyze how users reacted to the provided context and optimize the delivery method. The provider unit can also reliably transmit information using multiple communication methods. For example, in addition to APIs, web services, and mobile applications, important information can be reliably delivered by using email and SMS in conjunction. This allows the service provider to quickly and reliably provide context to users, improving user convenience.

[0073] The interest collection unit can collect users' interests and purchase history. For example, the interest collection unit can collect survey results. For example, the interest collection unit can collect data from surveys answered by users and identify their interests. The interest collection unit can also collect social media activity. For example, the interest collection unit can analyze content posted by users on social media and identify their interests. The interest collection unit can also collect purchase data from e-commerce sites. For example, the interest collection unit can collect data on products that users have purchased in the past and identify their interests. In this way, the interest collection unit can generate a more personalized context by collecting users' interests and purchase history. Some or all of the above processing in the interest collection unit may be performed using AI, for example, or not using AI. For example, the interest collection unit can analyze social media posts using AI and identify users' interests.

[0074] The location information collection unit can collect the user's location information. For example, the location information collection unit can collect GPS data. For example, the location information collection unit can obtain GPS data from the user's smartphone and identify the location. The location information collection unit can also collect Wi-Fi location information. For example, the location information collection unit can collect information about the Wi-Fi network to which the user is connected and identify the location. The location information collection unit can also collect location information using Bluetooth beacons. For example, the location information collection unit can receive signals from Bluetooth beacons near the user and identify the location. This allows the location information collection unit to generate a more appropriate context by collecting the user's location information. Some or all of the above processing in the location information collection unit may be performed using AI, for example, or without AI. For example, the location information collection unit can analyze GPS data with AI to identify the user's location.

[0075] The verification unit can allow the user to confirm the generated context. For example, the verification unit can display a confirmation screen to the user. For example, the verification unit can display a screen showing the generated context on the user's device and ask the user for confirmation. The verification unit can also collect user feedback. For example, the verification unit can provide an interface for the user to provide feedback on the generated context. The verification unit can also save the user's confirmation results. For example, the verification unit can save the context data confirmed by the user so that it can be referenced later. In this way, the verification unit can provide context that aligns with the user's intentions by allowing the user to confirm the generated context. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can analyze user feedback with AI and evaluate the quality of the generated context.

[0076] The data collection unit can collect behavioral history on social networking platforms and e-commerce sites. For example, the data collection unit collects data from social networking platforms. For example, the data collection unit collects data such as content posted by users on social networking platforms, likes, and comments. The data collection unit can also collect data from e-commerce sites. For example, the data collection unit collects data on products purchased by users on e-commerce sites, as well as browsing history. The data collection unit can also collect user behavioral history in real time. For example, the data collection unit collects data on the user's current activities. This allows the data collection unit to utilize a wider variety of data by collecting behavioral history on social networking platforms and e-commerce sites. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can analyze data from social networking platforms using AI to identify user behavioral history.

[0077] The analysis unit can analyze the collected behavioral history and identify information such as the user's family structure, nearest train station, car ownership status, and past travel history. For example, the analysis unit can analyze the collected behavioral history using data mining techniques. For example, the analysis unit can analyze the user's social media posts to identify the user's family structure. The analysis unit can also analyze the user's location data to identify the user's nearest train station. The analysis unit can also analyze the user's purchase history on e-commerce sites to identify the user's car ownership status. The analysis unit can also analyze data from the user's travel booking sites to identify the user's past travel history. This allows the analysis unit to generate a more accurate context by identifying detailed information about the user. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the collected behavioral history using AI to identify information such as the user's family structure and nearest train station.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of collecting behavioral history based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows the data collection unit to reduce the burden on the user by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0079] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from social networking services (SNS) platforms that the user has frequently used in the past. For example, the data collection unit can collect data from SNS platforms where the user has made many posts in the past. The data collection unit can also concentrate data collection during specific time periods based on the user's past behavior history. For example, the data collection unit can collect data on when the user has performed many activities during specific time periods in the past. The data collection unit can also analyze the user's past behavior patterns and select the most efficient data collection method. For example, the data collection unit can analyze the user's past behavior patterns to identify the optimal timing and method of data collection. This enables the data collection unit to efficiently collect data by analyzing past behavior history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze past behavior history using AI and select the optimal data collection method.

[0080] The data collection unit can filter the collected behavioral history based on the user's current living situation and areas of interest. For example, the data collection unit can prioritize collecting behavioral history related to topics the user is currently interested in. For example, the data collection unit can collect data related to topics the user has recently searched for. The data collection unit can also adjust the data it collects according to the user's living situation (e.g., at work, on vacation). For example, the data collection unit can collect data on activities the user performed while at work. The data collection unit can also filter and collect highly relevant behavioral history based on the user's areas of interest. For example, the data collection unit can collect data related to hobbies and topics the user is interested in. This allows the data collection unit to collect highly relevant data by filtering the data based on the user's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can analyze the user's areas of interest using AI and filter and collect highly relevant behavioral history.

[0081] The data collection unit can estimate the user's emotions and determine the priority of behavioral history to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting behavioral history related to relaxing activities. For example, the data collection unit will collect data related to places and activities where the user can relax. Also, if the user is excited, the data collection unit can prioritize collecting behavioral history related to entertainment and events. For example, the data collection unit will collect data on activities the user performed when excited. Furthermore, if the user is tired, the data collection unit can prioritize collecting behavioral history related to rest and relaxation. For example, the data collection unit will collect data on relaxation activities the user performed when tired. In this way, the data collection unit can collect more appropriate data by prioritizing behavioral history according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's emotions and determine the priority of the behavioral history to be collected.

[0082] The data collection unit can prioritize the collection of highly relevant history by considering the user's geographical location when collecting behavioral history. For example, the data collection unit can prioritize the collection of behavioral history related to the user's current location. For example, the data collection unit can collect data on activities performed around the user's current location. The data collection unit can also collect highly relevant behavioral history based on the user's past location information. For example, the data collection unit can collect data related to places the user has visited in the past. The data collection unit can also prioritize the collection of behavioral history related to places the user has visited. For example, the data collection unit can collect data on tourist attractions and restaurants the user has visited in the past. In this way, the data collection unit can collect highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze the user's geographical location information using AI and prioritize the collection of highly relevant behavioral history.

[0083] The data collection unit can analyze a user's social media activity and collect relevant history when collecting behavioral history. For example, the data collection unit can prioritize collecting behavioral history related to content recently posted by the user. For example, the data collection unit can collect data related to content recently posted by the user. The data collection unit can also collect behavioral history based on the activity of accounts that the user follows. For example, the data collection unit can collect content posted by accounts that the user follows. The data collection unit can also collect behavioral history related to groups and events that the user participates in. For example, the data collection unit can collect data related to groups and events that the user participates in. This allows the data collection unit to collect highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can analyze a user's social media activity using AI and collect relevant behavioral history.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, when the user is relaxed, the analysis unit can provide analysis results using detailed data and graphs. Also, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, when the user is in a hurry, the analysis unit can provide a short report summarizing the key points. Also, if the user is excited, the analysis unit can provide analysis results using visually stimulating graphics. For example, when the user is excited, the analysis unit can provide analysis results using colorful graphs and infographics. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the user's emotions using AI and adjust the way the analysis is expressed.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral history. For example, the analysis unit performs a detailed analysis on highly important behavioral history. For example, the analysis unit performs a detailed data analysis on the user's important behavioral history. The analysis unit can also perform a simplified analysis on less important behavioral history. For example, the analysis unit performs a simple statistical analysis on the user's less important behavioral history. The analysis unit can also determine the priority of the analysis according to the importance of the behavioral history. For example, the analysis unit prioritizes the analysis of highly important behavioral history and postpones the analysis of less important behavioral history. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the behavioral history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to analyze the importance of behavioral history and adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the behavioral history during analysis. For example, the analysis unit can apply a travel-specific analysis algorithm to behavioral history related to travel. For example, the analysis unit can apply a travel-specific data analysis algorithm to a user's travel history. The analysis unit can also apply a purchase behavior analysis algorithm to behavioral history related to shopping. For example, the analysis unit can apply a purchase behavior analysis algorithm to a user's purchase history. The analysis unit can also apply a social media analysis algorithm to behavioral history related to social media activity. For example, the analysis unit can apply a social media analysis algorithm to a user's social media activity. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the category of the behavioral history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the categories of behavioral history using AI and apply an appropriate analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is in a hurry, the analysis unit can provide a short, summary report. The analysis unit can also provide a detailed analysis when the user is relaxed. For example, if the user is relaxed, the analysis unit can provide an analysis using detailed data and graphs. The analysis unit can also provide a visually stimulating analysis when the user is excited. For example, if the user is excited, the analysis unit can provide an analysis using colorful graphs and infographics. In this way, the analysis unit can provide the user with the most optimal analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to analyze the user's emotions and adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the timing of behavioral history submissions. For example, the analysis unit may prioritize analyzing recent behavioral history. For example, the analysis unit may prioritize analyzing data on activities the user has recently performed. The analysis unit can also concentrate its analysis on a specific period. For example, the analysis unit may concentrate its analysis on data on activities the user performed during a specific period. Furthermore, the analysis unit can adjust the analysis schedule based on the timing of behavioral history submissions. For example, the analysis unit may adjust the analysis schedule based on when the user submitted their behavioral history. This allows the analysis unit to perform efficient analysis by determining the priority of analysis based on the timing of behavioral history submissions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit may use AI to analyze the timing of behavioral history submissions and determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the behavioral history during analysis. For example, the analysis unit may prioritize analyzing highly relevant behavioral history. For example, the analysis unit may prioritize analyzing behavioral history related to the user's current interests. The analysis unit can also postpone analyzing less relevant behavioral history. For example, the analysis unit may postpone analyzing behavioral history that is not relevant to the user's interests. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the behavioral history. For example, the analysis unit may dynamically adjust the order of analysis based on the user's interests and behavioral patterns. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the behavioral history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the relevance of behavioral history using AI and adjust the order of analysis.

[0090] The generation unit can estimate the user's emotions and adjust how the generated context is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed context. For example, when the user is relaxed, the generation unit can generate a context that includes detailed explanations and information. The generation unit can also generate a concise context that gets straight to the point if the user is in a hurry. For example, when the user is in a hurry, the generation unit can generate a short, summary context. The generation unit can also generate a visually stimulating context if the user is excited. For example, when the user is excited, the generation unit can generate a context using colorful graphics or infographics. In this way, the generation unit can provide a context that is easy for the user to understand by adjusting how the context is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can use AI to analyze the user's emotions and adjust how the generated context is expressed.

[0091] The generation unit can adjust the level of detail of the generated context based on the importance of the behavioral history during generation. For example, the generation unit can generate a detailed context based on high-importance behavioral history. For example, the generation unit can generate a context that includes detailed explanations and information for important user behavioral history. The generation unit can also generate a simplified context based on low-importance behavioral history. For example, the generation unit can generate a context that includes simple explanations and information for less important user behavioral history. The generation unit can also determine the priority of the contexts to be generated according to the importance of the behavioral history. For example, the generation unit can prioritize the generation of contexts based on high-importance behavioral history and postpone the generation of contexts based on low-importance behavioral history. This allows the generation unit to efficiently generate contexts by adjusting the level of detail of the context based on the importance of the behavioral history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can analyze the importance of the behavioral history using a generation AI and adjust the level of detail of the generated context.

[0092] The generation unit can apply different generation algorithms depending on the category of the behavioral history during generation. For example, the generation unit can apply a travel-specific generation algorithm to behavioral history related to travel. For example, the generation unit can apply a travel-specific generation algorithm to a user's travel history. The generation unit can also apply a purchase behavior generation algorithm to behavioral history related to shopping. For example, the generation unit can apply a purchase behavior generation algorithm to a user's purchase history. The generation unit can also apply a social media generation algorithm to behavioral history related to social media activity. For example, the generation unit can apply a social media generation algorithm to a user's social media activity. This allows the generation unit to generate more accurate context by applying different generation algorithms depending on the category of the behavioral history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can analyze the category of the behavioral history using a generation AI and apply an appropriate generation algorithm.

[0093] The generation unit can estimate the user's emotions and adjust the length of the generated context based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise context. For example, if the user is in a hurry, the generation unit can generate a short, summarized context. The generation unit can also generate a longer context with detailed explanations if the user is relaxed. For example, if the user is relaxed, the generation unit can generate a longer context with detailed explanations and information. The generation unit can also generate a context with visually stimulating effects if the user is excited. For example, if the user is excited, the generation unit can generate a context using colorful graphics or infographics. In this way, the generation unit can provide the user with the optimal context by adjusting the length of the context according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can analyze the user's emotions using a generation AI and adjust the length of the generated context.

[0094] The generation unit can determine the priority of contexts to generate based on the timing of behavioral history submissions during generation. For example, the generation unit can prioritize generating contexts based on recent behavioral history. For example, the generation unit can prioritize generating contexts based on data of the user's recent activities. The generation unit can also concentrate on generating contexts based on behavioral history for a specific period. For example, the generation unit can generate contexts based on data of the user's activities during a specific period. The generation unit can also adjust the schedule of contexts to be generated based on the timing of behavioral history submissions. For example, the generation unit can adjust the schedule of contexts to be generated based on when the user submitted their behavioral history. This enables efficient context generation by allowing the generation unit to prioritize contexts based on the timing of behavioral history submissions. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can analyze the timing of behavioral history submissions using a generation AI and determine the priority of contexts to be generated.

[0095] The generation unit can adjust the order of contexts generated based on the relevance of behavioral history during generation. For example, the generation unit can preferentially generate contexts based on highly relevant behavioral history. For example, the generation unit can preferentially generate contexts based on behavioral history related to the user's current interests. The generation unit can also postpone the generation of contexts based on less relevant behavioral history. For example, the generation unit can postpone the generation of contexts based on behavioral history that is less relevant to the user's interests. Furthermore, the generation unit can dynamically adjust the order of contexts generated based on the relevance of behavioral history. For example, the generation unit can dynamically adjust the order of contexts generated based on the user's interests and behavioral patterns. This enables efficient context generation by adjusting the order of contexts generated based on the relevance of behavioral history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can analyze the relevance of behavioral history using a generation AI and adjust the order of contexts to be generated.

[0096] The service provider can estimate the user's emotions and adjust how the context is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display. For example, when the user is nervous, the service provider can provide a simple and highly visible text display. The service provider can also provide a display that includes detailed information if the user is relaxed. For example, when the user is relaxed, the service provider can provide a graphical display that includes detailed information. The service provider can also provide a concise display if the user is in a hurry. For example, when the user is in a hurry, the service provider can provide a short, concise text display. In this way, the service provider can provide the optimal display method for the user by adjusting how the context is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use AI to analyze the user's emotions and adjust how the context provided is displayed.

[0097] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. For example, the service provider may select the optimal display method based on the display methods that the user has previously preferred. The service provider may also suggest a specific display method based on the user's past operation history. For example, the service provider may analyze the user's past operation history and suggest a specific display method. The service provider may also dynamically adjust the optimal display method based on the user's operation history. For example, the service provider may dynamically adjust the optimal display method based on the user's operation history. In this way, the service provider can provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may analyze the user's past operation history using AI and select the optimal display method.

[0098] The service provider can estimate the user's emotions and adjust the operating procedures for the context provided based on the estimated user emotions. For example, if the user is nervous, the service provider can provide simple and intuitive operating procedures. For example, if the user is nervous, the service provider can provide a simple and intuitive step-by-step guide. The service provider can also provide detailed operating procedures if the user is relaxed. For example, if the user is relaxed, the service provider can provide a guide that includes detailed operating procedures. The service provider can also provide procedures that allow for quick operation if the user is in a hurry. For example, if the user is in a hurry, the service provider can provide shortcuts or simplified operating procedures. In this way, the service provider can provide the optimal operating procedures for the user by adjusting the operating procedures according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to analyze the user's emotions and adjust the operation procedures for the context being provided.

[0099] The service provider can select the optimal display method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, when the user is using a smartphone, the service provider can provide a text display optimized for the screen size. The service provider can also provide a display method optimized for a larger screen if the user is using a tablet. For example, when the user is using a tablet, the service provider can provide a graphical display optimized for a larger screen. The service provider can also provide a concise and highly visible display method if the user is using a smartwatch. For example, when the user is using a smartwatch, the service provider can provide a concise and highly visible text display. In this way, the service provider can provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can analyze the user's device information with AI and select the optimal display method.

[0100] The interest collection unit can estimate the user's emotions and adjust the timing of interest collection based on the estimated emotions. For example, if the user is stressed, the interest collection unit can reduce the collection frequency to lessen the user's burden. For example, when the user is stressed, the interest collection unit will collect data at a reduced frequency. Conversely, if the user is relaxed, the interest collection unit can increase the collection frequency to collect more detailed interests. For example, when the user is relaxed, the interest collection unit will collect data at a increased frequency. Furthermore, if the user is excited, the interest collection unit can prioritize collecting interests related to specific events or activities. For example, when the user is excited, the interest collection unit will collect data related to specific events or activities. In this way, the interest collection unit can reduce the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the interest collection unit may be performed using AI, for example, or without AI. For example, the interest collection unit can analyze the user's emotions using AI and adjust the timing of collection.

[0101] The interest collection unit can analyze a user's past interest history and select the optimal collection method. For example, the interest collection unit can prioritize collecting topics that the user has frequently shown interest in in the past. For example, the interest collection unit can collect data related to topics that the user has shown a lot of interest in in the past. The interest collection unit can also concentrate collection during specific time periods based on the user's past interest history. For example, the interest collection unit can collect data that the user has shown a lot of interest in during specific time periods in the past. The interest collection unit can also analyze a user's past interest patterns and select the most efficient collection method. For example, the interest collection unit can analyze a user's past interest patterns to identify the optimal collection timing and method. This enables the interest collection unit to efficiently collect data by analyzing past interest history. Some or all of the above processing in the interest collection unit may be performed using AI, for example, or without AI. For example, the interest collection unit can analyze past interest history using AI and select the optimal collection method.

[0102] The interest collection unit can estimate the user's emotions and determine the priority of interests to collect based on the estimated emotions. For example, if the user is stressed, the interest collection unit will prioritize collecting interests related to relaxing activities. For example, if the user is excited, the interest collection unit will collect data related to relaxing activities. The interest collection unit can also prioritize collecting interests related to entertainment and events if the user is excited. For example, if the user is tired, the interest collection unit will collect data related to rest and relaxation. For example, if the user is tired, the interest collection unit will collect data related to rest and relaxation. This allows the interest collection unit to collect more appropriate data by prioritizing interests according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the interest collection unit may be performed using AI, for example, or without AI. For example, the interest collection unit can use AI to analyze the user's emotions and determine the priority of interests to collect.

[0103] The interest collection unit can prioritize collecting highly relevant interests by considering the user's geographical location information when collecting interests. For example, the interest collection unit can prioritize collecting interests related to the user's current location. For example, the interest collection unit can collect data related to activities the user has performed around their current location. The interest collection unit can also collect highly relevant interests based on the user's past location information. For example, the interest collection unit can collect data related to places the user has visited in the past. The interest collection unit can also prioritize collecting interests related to places the user has visited. For example, the interest collection unit can collect data related to tourist attractions or restaurants the user has visited in the past. In this way, the interest collection unit can collect highly relevant data by considering the user's geographical location information. Some or all of the above processing in the interest collection unit may be performed using AI, for example, or without AI. For example, the interest collection unit can analyze the user's geographical location information using AI and prioritize collecting highly relevant interests.

[0104] The location information collection unit can estimate the user's emotions and adjust the timing of location information collection based on the estimated emotions. For example, if the user is stressed, the location information collection unit can reduce the collection frequency to alleviate the user's burden. For example, when the user is stressed, the location information collection unit will collect data at a reduced frequency. Conversely, if the user is relaxed, the location information collection unit can increase the collection frequency to collect more detailed location information. For example, when the user is relaxed, the location information collection unit will collect data at a increased frequency. Furthermore, if the user is excited, the location information collection unit can prioritize collecting location information related to specific events or activities. For example, when the user is excited, the location information collection unit will collect data related to specific events or activities. In this way, the location information collection unit can reduce the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the location information collection unit may be performed using AI, for example, or without AI. For example, the location information collection unit can analyze the user's emotions using AI and adjust the timing of data collection.

[0105] The location information collection unit can analyze the user's past location history and select the optimal collection method. For example, the location information collection unit can prioritize collecting location information of places the user has frequently visited in the past. For example, the location information collection unit can collect data of places the user has visited many times in the past. The location information collection unit can also concentrate collection during specific time periods based on the user's past location history. For example, the location information collection unit can collect data of places the user has visited many times during specific time periods in the past. The location information collection unit can also analyze the user's past location patterns and select the most efficient collection method. For example, the location information collection unit can analyze the user's past location patterns to identify the optimal collection timing and method. This enables the location information collection unit to efficiently collect data by analyzing past location history. Some or all of the above processing in the location information collection unit may be performed using AI, for example, or without AI. For example, the location information collection unit can analyze past location history using AI and select the optimal collection method.

[0106] The location information collection unit can estimate the user's emotions and determine the priority of location information to collect based on the estimated emotions. For example, if the user is stressed, the location information collection unit will prioritize collecting location information of places where the user can relax. For example, the location information collection unit will collect data on places where the user can relax. Also, if the user is excited, the location information collection unit can prioritize collecting location information of places related to entertainment or events. For example, if the user is excited, the location information collection unit will collect data on places related to entertainment or events. Also, if the user is tired, the location information collection unit can prioritize collecting location information of places related to rest or relaxation. For example, if the user is tired, the location information collection unit will collect data on places related to rest or relaxation. In this way, the location information collection unit can collect more appropriate data by determining the priority of location information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the location information collection unit may be performed using AI, for example, or without AI. For example, the location information collection unit can analyze the user's emotions using AI and determine the priority of location information to collect.

[0107] The location information collection unit can prioritize the collection of highly relevant location information by considering the user's geographical location when collecting location information. For example, the location information collection unit can prioritize the collection of location information related to the user's current location. For example, the location information collection unit can collect data related to activities performed by the user around their current location. The location information collection unit can also collect highly relevant location information based on the user's past location information. For example, the location information collection unit can collect data related to places the user has visited in the past. The location information collection unit can also prioritize the collection of location information related to places the user has visited. For example, the location information collection unit can collect data related to tourist attractions or restaurants the user has visited in the past. In this way, the location information collection unit can collect highly relevant data by considering the user's geographical location information. Some or all of the above processing in the location information collection unit may be performed using AI, for example, or without AI. For example, the location information collection unit can analyze the user's geographical location information using AI and prioritize the collection of highly relevant location information.

[0108] The confirmation unit can estimate the user's emotions and adjust the display method of the confirmation based on the estimated user emotions. For example, if the user is nervous, the confirmation unit can provide a simple and highly visible display method. For example, if the user is nervous, the confirmation unit can provide a simple and highly visible text display. The confirmation unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the confirmation unit can provide a graphical display that includes detailed information. The confirmation unit can also provide a display method that gets to the point if the user is in a hurry. For example, if the user is in a hurry, the confirmation unit can provide a short text display that summarizes the key points. In this way, the confirmation unit can provide the optimal display method for the user by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is 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 processing in the confirmation unit may be performed using AI, for example, or without using AI. For example, the confirmation unit can use AI to analyze the user's emotions and adjust how the confirmation is displayed.

[0109] The verification unit can select the optimal display method by referring to the user's past operation history during verification. For example, the verification unit may prioritize providing display methods that the user has previously preferred. For example, the verification unit may select the optimal display method based on the display methods the user has previously preferred. The verification unit can also suggest a specific display method based on the user's past operation history. For example, the verification unit may analyze the user's past operation history and suggest a specific display method. The verification unit can also dynamically adjust the optimal display method based on the user's operation history. For example, the verification unit may dynamically adjust the optimal display method based on the user's operation history. In this way, the verification unit can provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit may analyze the user's past operation history using AI and select the optimal display method.

[0110] The confirmation unit can estimate the user's emotions and adjust the confirmation procedure based on the estimated emotions. For example, if the user is nervous, the confirmation unit can provide a simple and intuitive procedure. For example, if the user is nervous, the confirmation unit can provide a simple and intuitive step-by-step guide. The confirmation unit can also provide detailed procedures if the user is relaxed. For example, if the user is relaxed, the confirmation unit can provide a guide that includes detailed procedures. The confirmation unit can also provide procedures that allow for quick operation if the user is in a hurry. For example, if the user is in a hurry, the confirmation unit can provide shortcuts or simplified procedures. In this way, the confirmation unit can provide the optimal procedure for the user by adjusting the procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can use AI to analyze the user's emotions and adjust the confirmation procedure accordingly.

[0111] The verification unit can select the optimal display method by considering the user's device information during verification. For example, if the user is using a smartphone, the verification unit can provide a display method that matches the screen size. For example, when the user is using a smartphone, the verification unit can provide a text display optimized for the screen size. The verification unit can also provide a display method optimized for a larger screen if the user is using a tablet. For example, when the user is using a tablet, the verification unit can provide a graphical display optimized for a larger screen. The verification unit can also provide a concise and highly visible display method if the user is using a smartwatch. For example, when the user is using a smartwatch, the verification unit can provide a concise and highly visible text display. In this way, the verification unit can provide the optimal display method by considering the user's device information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can analyze the user's device information using AI and select the optimal display method.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The data collection unit can collect not only the user's behavioral history but also their health data. For example, it can collect heart rate, steps, and sleep data from the user's smartwatch or fitness tracker. It can also collect the user's food and exercise records. This allows the data collection unit to generate more personalized context based on the user's health status. For example, if the user is feeling stressed, it can suggest a relaxing travel plan. If the user prefers healthy eating, it can suggest health-conscious restaurants.

[0114] The analysis unit can estimate the user's emotions in addition to their behavioral history, and adjust the analysis results based on these estimated emotions. For example, if the user is feeling stressed, the analysis unit will prioritize analyzing relaxing activities. It can also prioritize analyzing data related to entertainment and events if the user is excited. Furthermore, if the user is tired, the analysis unit can prioritize analyzing data related to rest and relaxation. This allows the analysis unit to provide more appropriate context by adjusting the analysis results according to the user's emotions.

[0115] The generation unit can generate context based on the user's hobbies and skills, in addition to their behavioral history. For example, if the user likes music, the generation unit can suggest travel plans that include information on music festivals and live concerts. Similarly, if the user likes sports, it can suggest travel plans that include sporting events and activities. Furthermore, if the user is interested in art and culture, it can suggest travel plans that include museums and historical sites. This allows the generation unit to provide more personalized context based on the user's hobbies and skills.

[0116] The service provider can estimate the user's emotions and adjust how the context is displayed based on those emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display. For example, if the user is nervous, the service provider can provide a simple and highly visible text display. The service provider can also provide a display that includes detailed information if the user is relaxed. For example, if the user is relaxed, the service provider can provide a graphical display that includes detailed information. The service provider can also provide a display that gets to the point if the user is in a hurry. For example, if the user is in a hurry, the service provider can provide a short, concise text display that summarizes the key points. In this way, the service provider can provide the optimal display method for the user by adjusting how the context is displayed according to the user's emotions.

[0117] The data collection unit can collect user social network information in addition to user behavior history. For example, it can collect data from the social media accounts of the user's friends and family. It can also collect information about groups and events the user is participating in. This allows the data collection unit to generate more personalized context based on the user's social network information. For example, if the user is traveling with friends, it can suggest a travel plan based on the friends' interests. Similarly, if the user is traveling with family, it can suggest a travel plan that includes activities the whole family can enjoy.

[0118] The analysis unit can estimate the user's emotions in addition to their behavioral history, and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is relaxed, the analysis unit can provide analysis results using detailed data and graphs. Also, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is in a hurry, the analysis unit can provide a short report summarizing the key points. Also, if the user is excited, the analysis unit can provide analysis results using visually stimulating graphics. For example, if the user is excited, the analysis unit can provide analysis results using colorful graphs and infographics. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions.

[0119] The generation unit can generate context based on the user's behavior history as well as their past feedback. For example, it can analyze feedback previously provided by the user and generate context based on the user's preferences and requests. It can also suggest similar content and services based on content and services the user has previously rated. Furthermore, it can consider areas where the user was dissatisfied in the past and provide improved context. In this way, the generation unit can leverage the user's past feedback to provide more satisfying context.

[0120] The service provider can estimate the user's emotions and adjust the contextual operating procedures based on those emotions. For example, if the user is nervous, the service provider can provide simple and intuitive operating procedures. For example, if the user is nervous, the service provider can provide a simple and intuitive step-by-step guide. The service provider can also provide detailed operating procedures if the user is relaxed. For example, if the user is relaxed, the service provider can provide a guide that includes detailed operating procedures. The service provider can also provide procedures that allow for quick operation if the user is in a hurry. For example, if the user is in a hurry, the service provider can provide shortcuts and simplified operating procedures. In this way, the service provider can provide the optimal operating procedures for the user by adjusting the procedures according to the user's emotions.

[0121] The data collection unit can collect user device information in addition to user behavior history. For example, it can collect information on the type and settings of the device the user is using. It can also collect usage and performance data of the user's device. This allows the data collection unit to generate more appropriate context based on the user's device information. For example, if the user is using a smartphone, it can provide a context optimized for smartphones. If the user is using a tablet, it can provide a context optimized for tablets. Furthermore, if the user is using a smartwatch, it can provide a context optimized for smartwatches.

[0122] The analysis unit can estimate the user's emotions in addition to their behavioral history, and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is in a hurry, the analysis unit can provide a short, summary report. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide analysis results using detailed data and graphs. The analysis unit can also provide visually stimulating analysis results if the user is excited. For example, if the user is excited, the analysis unit can provide analysis results using colorful graphs and infographics. In this way, the analysis unit can provide the optimal analysis results for the user by adjusting the length of the analysis according to the user's emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The collection unit collects the user's behavioral history. This behavioral history includes website browsing history, app usage history, and e-commerce site purchase history. For example, the collection unit collects website browsing history from browser history data, app usage history from mobile device log data, and purchase history from e-commerce site databases. Step 2: The analysis unit analyzes the behavioral history collected by the collection unit. The analysis is performed using data mining techniques, statistical analysis methods, and machine learning algorithms. For example, the analysis unit uses data mining techniques to analyze user behavior patterns, statistical analysis methods to identify user interests, and machine learning algorithms to cluster the behavioral history. Step 3: The generation unit generates context based on the results analyzed by the analysis unit. Generation is performed using a generation AI, which generates context based on the user's interests, past behavior patterns, and location information. Step 4: The providing unit provides the context generated by the generating unit to the chat AI. The provision is done via API, sending the generated context to the chat AI's API. It can also be provided via web services or mobile applications.

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, interest collection unit, location information collection unit, confirmation unit, and emotion estimation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects behavioral history from the browser and applications of the smart device 14 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes behavioral patterns using the identification processing unit 290 of the data processing unit 12, and the generation unit generates a context using a generation AI. The provision unit provides the generated context to the chat AI, and the interest collection unit collects the user's interests and purchase history. The location information collection unit collects GPS data from the smart device 14, and the confirmation unit allows the user to confirm the generated context. The emotion estimation unit estimates the user's emotions using the camera and microphone of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, interest collection unit, location information collection unit, confirmation unit, and emotion estimation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects behavioral history from the browser or application of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes behavioral patterns using the identification processing unit 290 of the data processing unit 12, and the generation unit generates a context using a generation AI. The provision unit provides the generated context to the chat AI, and the interest collection unit collects the user's interests and purchase history. The location information collection unit collects GPS data from the smart glasses 214, and the confirmation unit allows the user to confirm the generated context. The emotion estimation unit estimates the user's emotions using the camera and microphone of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, interest collection unit, location information collection unit, confirmation unit, and emotion estimation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects behavioral history from the browser and applications of the headset terminal 314 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes behavioral patterns using the identification processing unit 290 of the data processing unit 12, and the generation unit generates context using the generation AI. The provision unit provides the generated context to the chat AI, and the interest collection unit collects the user's interests and purchase history. The location information collection unit collects GPS data from the headset terminal 314, and the confirmation unit allows the user to confirm the generated context. The emotion estimation unit estimates the user's emotions using the camera and microphone of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 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.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, interest collection unit, location information collection unit, confirmation unit, and emotion estimation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects behavioral history from the robot 414's browser or applications and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes behavioral patterns using the identification processing unit 290 of the data processing unit 12, and the generation unit generates context using the generation AI. The provision unit provides the generated context to the chat AI, and the interest collection unit collects the user's interests and purchase history. The location information collection unit collects GPS data from the robot 414, and the confirmation unit allows the user to confirm the generated context. The emotion estimation unit estimates the user's emotions using the robot 414's camera and microphone. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A collection unit that collects user behavior history, An analysis unit analyzes the behavioral history collected by the aforementioned collection unit, A generation unit generates a context based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the context generated by the generation unit to the chat AI. A system characterized by the following features. (Note 2) It includes an interest collection unit that collects user interests and purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a location information collection unit that collects location information. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a confirmation section that allows the user to confirm the generated context. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collecting browsing history on social media platforms and e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, By analyzing the collected behavioral history, information such as the user's family structure, nearest train station, car ownership status, and past travel history is identified. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting behavioral history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting behavioral history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting behavioral history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the behavioral history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts how the generated context is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, adjust the level of detail of the generated context based on the importance of the behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of the behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the context generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of the contexts to be generated is determined based on when the behavioral history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of contexts generated is adjusted based on the relevance of the behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the context is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the interaction steps of the context provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned interest collection unit, It estimates the user's emotions and adjusts the timing of collecting interest information based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned interest collection unit, Analyze the user's past interests and preferences to select the optimal data collection method. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned interest collection unit, It estimates the user's emotions and determines the priority of interests to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned interest collection unit, When collecting user interests, the system prioritizes collecting the most relevant interests by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned location information collection unit, The system estimates the user's emotions and adjusts the timing of location data collection based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned location information collection unit, Analyze the user's past location history and select the optimal data collection method. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned location information collection unit, It estimates the user's emotions and determines the priority of location data to collect based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned location information collection unit, When collecting location information, the system prioritizes collecting highly relevant location information, taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned verification unit is The system estimates the user's emotions and adjusts how confirmations are displayed based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned verification unit is During verification, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned verification unit is The system estimates the user's emotions and adjusts the confirmation procedure based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned verification unit is During verification, the optimal display method is selected considering the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user behavior history, An analysis unit analyzes the behavioral history collected by the aforementioned collection unit, A generation unit generates a context based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the context generated by the generation unit to the chat AI. A system characterized by the following features.

2. It includes an interest collection unit that collects user interests and purchase history. The system according to feature 1.

3. It includes a location information collection unit that collects location information. The system according to feature 1.

4. It includes a confirmation section that allows the user to confirm the generated context. The system according to feature 1.

5. The aforementioned collection unit is Collecting browsing history on social media platforms and e-commerce sites. The system according to feature 1.

6. The aforementioned analysis unit, By analyzing the collected behavioral history, information such as the user's family structure, nearest train station, car ownership status, and past travel history is identified. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting behavioral history based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting behavioral history, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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