system

Abstract

The system according to this embodiment aims to analyze user data and provide appropriate suggestions in real time. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a real-time analysis unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The real-time analysis unit analyzes the user's location information and behavioral data in real time.

Description

Technical Field

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[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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the user data has not been fully utilized effectively to make appropriate proposals in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze user data and make appropriate proposals in real time.

Means for Solving the Problems

[0007] The system according to this embodiment can analyze user data and provide appropriate suggestions in real time. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 AI ​​Life Concierge System according to an embodiment of the present invention is a service that utilizes AI technology to make users' lives more convenient and enjoyable. This AI Life Concierge System uses natural language processing (NLP) to analyze chat content from messaging apps and extract user needs and event information. Next, it uses machine learning to analyze purchase data from electronic payment systems and learn users' purchasing patterns and preferences. Furthermore, it uses a recommender system to recommend relevant products and services based on the learned user preferences and behavioral patterns. In addition, it uses real-time data analysis to analyze users' location information and behavioral data in real time and make appropriate suggestions on the spot. By combining these AI technologies, it deeply understands users' lifestyle habits and preferences and provides information and services that are useful in various everyday situations. For example, it can provide information about stores and services that users use on a daily basis, making users' lives more convenient and enjoyable. In this way, the AI ​​Life Concierge System can make users' lives more convenient and enjoyable.

[0029] The AI ​​life concierge system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a real-time analysis unit. The data collection unit collects user data. For example, the data collection unit can analyze chat content from messaging apps to extract user needs and event information. The data collection unit can also analyze purchase data from electronic payment systems to learn user purchasing patterns and preferences. Furthermore, the data collection unit can collect user location information and behavioral data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can use natural language processing technology to analyze chat content from messaging apps to extract user needs and event information. Furthermore, the analysis unit can use machine learning technology to analyze purchase data from electronic payment systems to learn user purchasing patterns and preferences. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the suggestion unit can use a recommender system to recommend relevant products and services based on the learned user preferences and behavioral patterns. The real-time analysis unit analyzes user location information and behavioral data in real time and makes appropriate suggestions on the spot. The real-time analysis unit can, for example, recommend nearby stores and services based on the user's current location information. This allows the AI ​​life concierge system according to the embodiment to make the user's life more convenient and enjoyable.

[0030] The data collection unit collects user data. For example, it can analyze chat content from messaging apps to extract user needs and event information. Specifically, it uses the messaging app's API to obtain user chat content and analyzes the text data using natural language processing technology. This allows it to extract information such as what topics users are interested in and what events they want to participate in. The data collection unit can also analyze purchase data from electronic payment systems to learn user purchasing patterns and preferences. Specifically, it obtains transaction data from electronic payment systems and analyzes the user's purchase history using machine learning algorithms. This allows it to understand patterns such as what products users prefer to buy and when they make purchases. Furthermore, the data collection unit can collect user location information and behavioral data. Specifically, it uses the smartphone's GPS function to obtain the user's current location and records their movement history. This allows it to understand behavioral patterns such as what places users frequently visit and what routes they take. This data is stored on a cloud server and made accessible to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use natural language processing technology to analyze chat content from messaging apps and extract user needs and event information. Specifically, it uses techniques such as tokenization, morphological analysis, and contextual analysis to analyze text data in detail and understand user intent and emotions. The analysis unit can also use machine learning technology to analyze purchase data from electronic payment systems and learn user purchasing patterns and preferences. Specifically, it uses clustering and classification algorithms to group user purchase histories and identify user groups with common characteristics. Furthermore, the analysis unit can analyze user location information and behavioral data to understand user movement patterns and behavioral tendencies. For example, it can use time series analysis to analyze how often users visit specific locations at specific times and predict behavioral patterns. In this way, the analysis unit can analyze the collected data from multiple angles and gain a detailed understanding of user needs and behavioral patterns. In addition, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and predictions. For example, based on past purchase data, it can predict purchasing trends during specific seasons or events and formulate future marketing strategies. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, 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 Proposal Department makes suggestions based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can use a recommender system to suggest relevant products and services based on learned user preferences and behavioral patterns. Specifically, it uses collaborative filtering and content-based recommendation algorithms to analyze the user's past behavioral data and data of similar users to suggest the most suitable products and services. For example, it lists highly relevant products and services based on products the user has previously purchased and services they have viewed, and notifies the user. The Proposal Department can also provide real-time suggestions tailored to the user's current situation and needs. For example, if the user is in a specific location, it provides information on events and stores related to that location. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it tracks whether the user purchased a suggested product or used a suggested service, and adjusts the algorithm based on the results. This allows the Proposal Department to always provide the best possible suggestions to the user and improve user satisfaction. In addition, the Proposal Department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also email, SMS, and in-app messages. This allows the proposal department to provide users with proposals quickly and reliably, making their lives more convenient and enjoyable.

[0033] The Real-Time Analytics Department analyzes user location and behavioral data in real time and provides appropriate suggestions on the spot. Specifically, it can recommend nearby stores and services based on the user's current location. For example, if a user is in a shopping mall, the Real-Time Analytics Department can provide information on stores and sales within the mall and recommend stores that the user might be interested in. It can also provide information and services related to an event if the user is participating in that event. The Real-Time Analytics Department analyzes user behavioral data in real time and provides suggestions tailored to the user's current needs and circumstances. For example, if a user is traveling a specific route, it can suggest recommended restaurants and tourist spots along that route. Furthermore, the Real-Time Analytics Department can provide more personalized suggestions by considering the user's past behavioral data and preferences. For example, it can recommend similar stores and services based on data on stores the user has visited and services they have used in the past. This allows the Real-Time Analytics Department to consistently provide the best possible suggestions to users, improving user satisfaction. Additionally, the Real-Time Analytics Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can track whether the user visited a suggested store or used a suggested service and adjust the algorithm based on the results. This allows the real-time analysis unit to constantly provide users with optimal suggestions based on the latest information, making their lives more convenient and enjoyable.

[0034] The data collection unit can analyze the chat content of messaging apps and extract user needs and event information. For example, the data collection unit can use natural language processing technology to analyze text messages and extract user needs. The data collection unit can also use image analysis technology to analyze image messages and extract event information. Furthermore, the data collection unit can use speech recognition technology to analyze voice messages and extract user needs. This allows for the extraction of needs and event information from user chat content, enabling the provision of more appropriate services. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the chat content of messaging apps into a generation AI and have the generation AI perform the extraction of user needs and event information.

[0035] The data collection unit can analyze purchase data from electronic payment systems and learn users' purchasing patterns and preferences. For example, the data collection unit can use machine learning techniques to analyze purchase data such as purchase date and time, purchased items, and payment methods to learn users' purchasing patterns. The data collection unit can also analyze frequently purchased items and the timing of purchases to learn users' preferences. Furthermore, the data collection unit can predict users' purchasing trends based on purchase data. As a result, by analyzing users' purchase data, it is possible to learn users' preferences and purchasing patterns and make more appropriate suggestions. 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 input purchase data from electronic payment systems into a generating AI and have the generating AI learn users' purchasing patterns and preferences.

[0036] The recommendation unit can recommend relevant products and services based on the user's learned preferences and behavioral patterns. For example, the recommendation unit can use a recommender system to recommend products based on the user's past purchase history. The recommendation unit can also recommend services based on the user's interests. Furthermore, the recommendation unit can analyze the user's behavioral patterns and recommend relevant events and activities. This improves user satisfaction by recommending relevant products and services based on the user's preferences and behavioral patterns. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the learned user preferences and behavioral patterns into a generating AI and have the generating AI perform recommendations for relevant products and services.

[0037] The real-time analysis unit can analyze user location information and behavioral data in real time and make appropriate suggestions on the spot. For example, the real-time analysis unit can use GPS data to identify the user's current location and recommend nearby stores and services. It can also analyze the user's travel history and provide relevant information based on the places they have visited. Furthermore, the real-time analysis unit can analyze user behavioral data in real time and make suggestions tailored to the current situation. This enables appropriate suggestions tailored to the user's current situation by analyzing user location information and behavioral data in real time. Some or all of the above processing in the real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input user location information and behavioral data into a generating AI and have the generating AI perform real-time analysis and make suggestions.

[0038] The data collection unit can analyze the user's past chat history and select the optimal data collection method. For example, the data collection unit can select data to collect based on keywords that the user has frequently used in the past. The data collection unit can also determine the types of data to collect at specific time periods based on the user's past chat history. Furthermore, the data collection unit can analyze the user's past chat history and set priorities for the data to be collected. This allows for efficient data collection by selecting the optimal data collection method through analysis of the user's past chat history. 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 input the user's past chat history into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current interests and events during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. The data collection unit can also collect information related to events the user plans to attend. Furthermore, the data collection unit can filter out unnecessary data based on the user's current interests. This allows for the collection of more relevant data by filtering data based on the user's current interests and events. 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 input data about the user's current interests and events into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect event information related to the user's current location. It can also collect information on nearby stores and services based on the user's geographical location information. Furthermore, the data collection unit can collect region-specific promotional information by considering the user's location information. This allows for the priority collection of 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 input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activities of the user's social media followers and friends and collect relevant data. Furthermore, the data collection unit can analyze the content of the user's social media posts and collect data that might be of interest. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can set the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase data. It can also apply a natural language processing algorithm to chat data. Furthermore, it can apply a geographic information analysis algorithm to location data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. 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 input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, the analysis unit can set the priority of analysis according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. 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 input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can set the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The proposal unit can adjust the level of detail in its proposals based on the importance of the products. For example, it can provide detailed proposals for high-importance products and simplified proposals for low-importance products. Furthermore, it can prioritize proposals according to their importance. This allows for efficient proposals by adjusting the level of detail based on product importance. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of products into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0047] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit can apply an entertainment suggestion algorithm to entertainment-related products. It can also apply a health suggestion algorithm to health-related products. Furthermore, it can apply a food suggestion algorithm to food-related products. By applying different suggestion algorithms depending on the product category, more accurate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the product category into a generation AI and have the generation AI execute the application of different suggestion algorithms.

[0048] The proposal department can determine the priority of proposals based on the product submission timing when submitting a proposal. For example, the proposal department may prioritize the newest products. It can also propose the latest products while referring to past products. Furthermore, the proposal department can set the priority of proposals according to the product submission timing. This allows for efficient proposals by determining the priority of proposals based on the product submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the product submission timing into a generating AI and have the generating AI determine the priority of proposals.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant products. It can also postpone suggesting less relevant products. Furthermore, the suggestion unit can set the order of suggestions according to the relevance of the products. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the products into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0050] The real-time analysis unit can perform analysis while considering user attribute information during real-time analysis. For example, the real-time analysis unit can perform appropriate analysis by considering the user's age and gender. It can also perform highly relevant analysis by considering the user's occupation and hobbies. Furthermore, the real-time analysis unit can perform personalized analysis by considering the user's lifestyle. This makes it possible to perform more personalized analysis by considering user attribute information. Some or all of the above processing in the real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input user attribute information into a generating AI and have the generating AI perform the analysis.

[0051] The real-time analysis unit can perform analysis while considering the geographical distribution of data during real-time analysis. For example, the real-time analysis unit can perform region-specific analysis based on the user's current location. It can also perform geographically relevant analysis by considering the user's past travel history. Furthermore, the real-time analysis unit can analyze geographical trends based on the user's location information. This allows for more accurate analysis by considering the geographical distribution of data. Some or all of the above processing in the real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0052] The real-time analysis unit can improve the accuracy of its analysis by referring to relevant literature during real-time analysis. For example, the real-time analysis unit can improve the accuracy of its analysis by referring to the latest research papers. It can also improve the accuracy of its analysis by referring to relevant patent documents. Furthermore, the real-time analysis unit can improve the accuracy of its analysis by referring to industry white papers. Thus, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input relevant literature into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The data collection unit collects user health data, and the analysis unit can analyze the collected health data to evaluate the user's health status. For example, the data collection unit can collect heart rate, steps, and sleep data from wearable devices. It can also collect user-entered records of meals and exercise. Furthermore, the data collection unit can collect the user's medical records and diagnostic results. This allows for the collection and analysis of user health data, enabling the evaluation of the user's health status and the provision of appropriate advice. For example, the analysis unit can analyze collected heart rate data to assess the user's stress level. It can also analyze collected sleep data to evaluate the quality of the user's sleep. Furthermore, it can analyze collected records of meals and exercise to evaluate the user's nutritional balance and exercise level. This allows for a comprehensive evaluation of the user's health status and the provision of appropriate advice.

[0055] The data collection unit can select the target of data collection based on the user's hobbies and interests. For example, if the user is interested in music, the data collection unit will prioritize collecting music-related data. Similarly, if the user is interested in sports, the data collection unit can prioritize collecting sports-related data. Furthermore, if the user is interested in travel, the data collection unit can prioritize collecting travel-related data. This allows for the collection of more relevant data by selecting the target of data collection based on the user's hobbies and interests. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's hobbies and interests into a generating AI and have the generating AI select the target of data collection.

[0056] The analysis unit can analyze a user's past behavioral data and predict their future behavior. For example, it can analyze a user's past purchase data to predict the next product they are most likely to buy. It can also analyze a user's past travel data to predict the next place they are most likely to visit. Furthermore, it can analyze a user's past chat data to predict the next topic they are most likely to discuss. By analyzing a user's past behavioral data, it becomes possible to predict their future behavior and provide more appropriate suggestions. 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 input a user's past behavioral data into a generating AI and have the generating AI perform predictions of future behavior.

[0057] The suggestion unit can analyze past user feedback to improve the accuracy of its suggestions. For example, it can analyze suggestions that users have previously given high ratings to and make similar suggestions. It can also analyze suggestions that users have previously given low ratings to avoid making similar suggestions. Furthermore, the suggestion unit can analyze past user feedback to adjust the content of its suggestions. By analyzing past user feedback, it is possible to improve the accuracy of suggestions and make more appropriate suggestions. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input past user feedback into a generation AI and have the generation AI perform the task of improving the accuracy of suggestions.

[0058] The real-time analysis unit can perform real-time analysis while considering the user's current activity status. For example, if the user is exercising, the real-time analysis unit will prioritize analyzing data related to exercise. It can also prioritize analyzing data related to work if the user is working. Furthermore, if the user is resting, the real-time analysis unit can prioritize analyzing data related to relaxation. This allows for more appropriate real-time analysis by considering the user's current activity status. Some or all of the above processing in the real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input the user's current activity status into a generating AI and have the generating AI perform the real-time analysis.

[0059] The suggestion unit can adjust the content of its suggestions by taking into account the user's geographical location. For example, the suggestion unit can suggest products and services related to the user's current location. It can also suggest nearby stores and services based on the user's geographical location. Furthermore, the suggestion unit can suggest region-specific promotional information by taking into account the user's location. This makes it possible to make more relevant suggestions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location into a generating AI and have the generating AI adjust the content of the suggestions.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects user data. For example, the data collection unit can analyze chat content from messaging apps to extract user needs and event information. It can also analyze purchase data from electronic payment systems to learn user purchasing patterns and preferences. Furthermore, the data collection unit can collect user location information and behavioral data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use natural language processing technology to analyze chat content from messaging apps and extract user needs and event information. The analysis unit can also use machine learning technology to analyze purchase data from electronic payment systems and learn user purchasing patterns and preferences. Step 3: The proposal unit makes proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit can use a recommender system to recommend relevant products and services based on learned user preferences and behavioral patterns. Step 4: The real-time analysis unit analyzes the user's location information and behavioral data in real time and makes appropriate suggestions on the spot. For example, the real-time analysis unit can recommend nearby stores and services based on the user's current location information.

[0062] (Example of form 2) The AI ​​Life Concierge System according to an embodiment of the present invention is a service that utilizes AI technology to make users' lives more convenient and enjoyable. This AI Life Concierge System uses natural language processing (NLP) to analyze chat content from messaging apps and extract user needs and event information. Next, it uses machine learning to analyze purchase data from electronic payment systems and learn users' purchasing patterns and preferences. Furthermore, it uses a recommender system to recommend relevant products and services based on the learned user preferences and behavioral patterns. In addition, it uses real-time data analysis to analyze users' location information and behavioral data in real time and make appropriate suggestions on the spot. By combining these AI technologies, it deeply understands users' lifestyle habits and preferences and provides information and services that are useful in various everyday situations. For example, it can provide information about stores and services that users use on a daily basis, making users' lives more convenient and enjoyable. In this way, the AI ​​Life Concierge System can make users' lives more convenient and enjoyable.

[0063] The AI ​​life concierge system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a real-time analysis unit. The data collection unit collects user data. For example, the data collection unit can analyze chat content from messaging apps to extract user needs and event information. The data collection unit can also analyze purchase data from electronic payment systems to learn user purchasing patterns and preferences. Furthermore, the data collection unit can collect user location information and behavioral data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can use natural language processing technology to analyze chat content from messaging apps to extract user needs and event information. Furthermore, the analysis unit can use machine learning technology to analyze purchase data from electronic payment systems to learn user purchasing patterns and preferences. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the suggestion unit can use a recommender system to recommend relevant products and services based on the learned user preferences and behavioral patterns. The real-time analysis unit analyzes user location information and behavioral data in real time and makes appropriate suggestions on the spot. The real-time analysis unit can, for example, recommend nearby stores and services based on the user's current location information. This allows the AI ​​life concierge system according to the embodiment to make the user's life more convenient and enjoyable.

[0064] The data collection unit collects user data. For example, it can analyze chat content from messaging apps to extract user needs and event information. Specifically, it uses the messaging app's API to obtain user chat content and analyzes the text data using natural language processing technology. This allows it to extract information such as what topics users are interested in and what events they want to participate in. The data collection unit can also analyze purchase data from electronic payment systems to learn user purchasing patterns and preferences. Specifically, it obtains transaction data from electronic payment systems and analyzes the user's purchase history using machine learning algorithms. This allows it to understand patterns such as what products users prefer to buy and when they make purchases. Furthermore, the data collection unit can collect user location information and behavioral data. Specifically, it uses the smartphone's GPS function to obtain the user's current location and records their movement history. This allows it to understand behavioral patterns such as what places users frequently visit and what routes they take. This data is stored on a cloud server and made accessible to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, the data collection unit can respond flexibly to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0065] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use natural language processing technology to analyze chat content from messaging apps and extract user needs and event information. Specifically, it uses techniques such as tokenization, morphological analysis, and contextual analysis to analyze text data in detail and understand user intent and emotions. The analysis unit can also use machine learning technology to analyze purchase data from electronic payment systems and learn user purchasing patterns and preferences. Specifically, it uses clustering and classification algorithms to group user purchase histories and identify user groups with common characteristics. Furthermore, the analysis unit can analyze user location information and behavioral data to understand user movement patterns and behavioral tendencies. For example, it can use time series analysis to analyze how often users visit specific locations at specific times and predict behavioral patterns. In this way, the analysis unit can analyze the collected data from multiple angles and gain a detailed understanding of user needs and behavioral patterns. In addition, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and predictions. For example, based on past purchase data, it can predict purchasing trends during specific seasons or events and formulate future marketing strategies. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, 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.

[0066] The Proposal Department makes suggestions based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can use a recommender system to suggest relevant products and services based on learned user preferences and behavioral patterns. Specifically, it uses collaborative filtering and content-based recommendation algorithms to analyze the user's past behavioral data and data of similar users to suggest the most suitable products and services. For example, it lists highly relevant products and services based on products the user has previously purchased and services they have viewed, and notifies the user. The Proposal Department can also provide real-time suggestions tailored to the user's current situation and needs. For example, if the user is in a specific location, it provides information on events and stores related to that location. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it tracks whether the user purchased a suggested product or used a suggested service, and adjusts the algorithm based on the results. This allows the Proposal Department to always provide the best possible suggestions to the user and improve user satisfaction. In addition, the Proposal Department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also email, SMS, and in-app messages. This allows the proposal department to provide users with proposals quickly and reliably, making their lives more convenient and enjoyable.

[0067] The Real-Time Analytics Department analyzes user location and behavioral data in real time and provides appropriate suggestions on the spot. Specifically, it can recommend nearby stores and services based on the user's current location. For example, if a user is in a shopping mall, the Real-Time Analytics Department can provide information on stores and sales within the mall and recommend stores that the user might be interested in. It can also provide information and services related to an event if the user is participating in that event. The Real-Time Analytics Department analyzes user behavioral data in real time and provides suggestions tailored to the user's current needs and circumstances. For example, if a user is traveling a specific route, it can suggest recommended restaurants and tourist spots along that route. Furthermore, the Real-Time Analytics Department can provide more personalized suggestions by considering the user's past behavioral data and preferences. For example, it can recommend similar stores and services based on data on stores the user has visited and services they have used in the past. This allows the Real-Time Analytics Department to consistently provide the best possible suggestions to users, improving user satisfaction. Additionally, the Real-Time Analytics Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can track whether the user visited a suggested store or used a suggested service and adjust the algorithm based on the results. This allows the real-time analysis unit to constantly provide users with optimal suggestions based on the latest information, making their lives more convenient and enjoyable.

[0068] The data collection unit can analyze the chat content of messaging apps and extract user needs and event information. For example, the data collection unit can use natural language processing technology to analyze text messages and extract user needs. The data collection unit can also use image analysis technology to analyze image messages and extract event information. Furthermore, the data collection unit can use speech recognition technology to analyze voice messages and extract user needs. This allows for the extraction of needs and event information from user chat content, enabling the provision of more appropriate services. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the chat content of messaging apps into a generation AI and have the generation AI perform the extraction of user needs and event information.

[0069] The data collection unit can analyze purchase data from electronic payment systems and learn users' purchasing patterns and preferences. For example, the data collection unit can use machine learning techniques to analyze purchase data such as purchase date and time, purchased items, and payment methods to learn users' purchasing patterns. The data collection unit can also analyze frequently purchased items and the timing of purchases to learn users' preferences. Furthermore, the data collection unit can predict users' purchasing trends based on purchase data. As a result, by analyzing users' purchase data, it is possible to learn users' preferences and purchasing patterns and make more appropriate suggestions. 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 input purchase data from electronic payment systems into a generating AI and have the generating AI learn users' purchasing patterns and preferences.

[0070] The recommendation unit can recommend relevant products and services based on the user's learned preferences and behavioral patterns. For example, the recommendation unit can use a recommender system to recommend products based on the user's past purchase history. The recommendation unit can also recommend services based on the user's interests. Furthermore, the recommendation unit can analyze the user's behavioral patterns and recommend relevant events and activities. This improves user satisfaction by recommending relevant products and services based on the user's preferences and behavioral patterns. Some or all of the above processes in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the learned user preferences and behavioral patterns into a generating AI and have the generating AI perform recommendations for relevant products and services.

[0071] The real-time analysis unit can analyze user location information and behavioral data in real time and make appropriate suggestions on the spot. For example, the real-time analysis unit can use GPS data to identify the user's current location and recommend nearby stores and services. It can also analyze the user's travel history and provide relevant information based on the places they have visited. Furthermore, the real-time analysis unit can analyze user behavioral data in real time and make suggestions tailored to the current situation. This enables appropriate suggestions tailored to the user's current situation by analyzing user location information and behavioral data in real time. Some or all of the above processing in the real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input user location information and behavioral data into a generating AI and have the generating AI perform real-time analysis and make suggestions.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to gather more detailed information. Furthermore, if the user is busy, the data collection unit can adjust the timing of data collection to match the user's schedule. This reduces the user's burden and enables more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0073] The data collection unit can analyze the user's past chat history and select the optimal data collection method. For example, the data collection unit can select data to collect based on keywords that the user has frequently used in the past. The data collection unit can also determine the types of data to collect at specific time periods based on the user's past chat history. Furthermore, the data collection unit can analyze the user's past chat history and set priorities for the data to be collected. This allows for efficient data collection by selecting the optimal data collection method through analysis of the user's past chat history. 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 input the user's past chat history into a generating AI and have the generating AI select the optimal data collection method.

[0074] The data collection unit can filter data based on the user's current interests and events during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. The data collection unit can also collect information related to events the user plans to attend. Furthermore, the data collection unit can filter out unnecessary data based on the user's current interests. This allows for the collection of more relevant data by filtering data based on the user's current interests and events. 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 input data about the user's current interests and events into a generating AI and have the generating AI perform the filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting entertainment-related data. Similarly, if the user is tired, the data collection unit may prioritize collecting relaxation-related data. Furthermore, if the user is stressed, the data collection unit may prioritize collecting data that helps reduce stress. This allows for more appropriate data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect event information related to the user's current location. It can also collect information on nearby stores and services based on the user's geographical location information. Furthermore, the data collection unit can collect region-specific promotional information by considering the user's location information. This allows for the priority collection of 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 input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activities of the user's social media followers and friends and collect relevant data. Furthermore, the data collection unit can analyze the content of the user's social media posts and collect data that might be of interest. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0078] 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. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can set the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase data. It can also apply a natural language processing algorithm to chat data. Furthermore, it can apply a geographic information analysis algorithm to location data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. 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 input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0081] 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 result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the latest data while referring to past data. Furthermore, the analysis unit can set the priority of analysis according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. 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 input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can set the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is excited, it can provide visually appealing suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0085] The proposal unit can adjust the level of detail in its proposals based on the importance of the products. For example, it can provide detailed proposals for high-importance products and simplified proposals for low-importance products. Furthermore, it can prioritize proposals according to their importance. This allows for efficient proposals by adjusting the level of detail based on product importance. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of products into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0086] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, the suggestion unit can apply an entertainment suggestion algorithm to entertainment-related products. It can also apply a health suggestion algorithm to health-related products. Furthermore, it can apply a food suggestion algorithm to food-related products. By applying different suggestion algorithms depending on the product category, more accurate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the product category into a generation AI and have the generation AI execute the application of different suggestion algorithms.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0088] The proposal department can determine the priority of proposals based on the product submission timing when submitting a proposal. For example, the proposal department may prioritize the newest products. It can also propose the latest products while referring to past products. Furthermore, the proposal department can set the priority of proposals according to the product submission timing. This allows for efficient proposals by determining the priority of proposals based on the product submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the product submission timing into a generating AI and have the generating AI determine the priority of proposals.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant products. It can also postpone suggesting less relevant products. Furthermore, the suggestion unit can set the order of suggestions according to the relevance of the products. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the products into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0090] The real-time analysis unit can estimate the user's emotions and adjust the criteria for real-time analysis based on the estimated emotions. For example, if the user is relaxed, the real-time analysis unit can perform a detailed real-time analysis. If the user is in a hurry, the real-time analysis unit can also perform a concise real-time analysis. Furthermore, if the user is excited, the real-time analysis unit can perform a visually engaging real-time analysis. By adjusting the criteria for real-time analysis based on the user's emotions, a more appropriate analysis becomes possible. 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 real-time analysis unit may be performed using AI, for example, or not using AI. For example, the real-time analysis unit can input user emotion data into a generative AI and have the generative AI adjust the criteria for real-time analysis.

[0091] The real-time analysis unit can perform analysis while considering user attribute information during real-time analysis. For example, the real-time analysis unit can perform appropriate analysis by considering the user's age and gender. It can also perform highly relevant analysis by considering the user's occupation and hobbies. Furthermore, the real-time analysis unit can perform personalized analysis by considering the user's lifestyle. This makes it possible to perform more personalized analysis by considering user attribute information. Some or all of the above processing in the real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input user attribute information into a generating AI and have the generating AI perform the analysis.

[0092] The real-time analysis unit can estimate the user's emotions and adjust the order in which the real-time analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the real-time analysis unit may prioritize displaying detailed analysis results. If the user is in a hurry, the real-time analysis unit may prioritize displaying concise analysis results. Furthermore, if the user is excited, the real-time analysis unit may prioritize displaying visually appealing analysis results. This allows for the provision of more appropriate information by adjusting the order in which the real-time analysis results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the real-time analysis unit may be performed using AI, or not using AI. For example, the real-time analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the real-time analysis results.

[0093] The real-time analysis unit can perform analysis while considering the geographical distribution of data during real-time analysis. For example, the real-time analysis unit can perform region-specific analysis based on the user's current location. It can also perform geographically relevant analysis by considering the user's past travel history. Furthermore, the real-time analysis unit can analyze geographical trends based on the user's location information. This allows for more accurate analysis by considering the geographical distribution of data. Some or all of the above processing in the real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0094] The real-time analysis unit can improve the accuracy of its analysis by referring to relevant literature during real-time analysis. For example, the real-time analysis unit can improve the accuracy of its analysis by referring to the latest research papers. It can also improve the accuracy of its analysis by referring to relevant patent documents. Furthermore, the real-time analysis unit can improve the accuracy of its analysis by referring to industry white papers. Thus, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input relevant literature into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The data collection unit collects user health data, and the analysis unit can analyze the collected health data to evaluate the user's health status. For example, the data collection unit can collect heart rate, steps, and sleep data from wearable devices. It can also collect user-entered records of meals and exercise. Furthermore, the data collection unit can collect the user's medical records and diagnostic results. This allows for the collection and analysis of user health data, enabling the evaluation of the user's health status and the provision of appropriate advice. For example, the analysis unit can analyze collected heart rate data to assess the user's stress level. It can also analyze collected sleep data to evaluate the quality of the user's sleep. Furthermore, it can analyze collected records of meals and exercise to evaluate the user's nutritional balance and exercise level. This allows for a comprehensive evaluation of the user's health status and the provision of appropriate advice.

[0097] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing data related to stress reduction. It can also prioritize analyzing data related to relaxation if the user is relaxed. Furthermore, if the user is excited, it can prioritize analyzing data related to entertainment. This allows for more appropriate analysis by prioritizing analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0098] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion unit can suggest products or services related to relaxation. If the user is relaxed, the suggestion unit can also suggest products or services related to entertainment. Furthermore, if the user is excited, the suggestion unit can also suggest products or services related to activities. By adjusting the content of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of its suggestions.

[0099] The real-time analysis unit can estimate the user's emotions and adjust the frequency of real-time analysis based on the estimated emotions. For example, if the user is stressed, the real-time analysis unit can reduce the frequency of real-time analysis to alleviate the user's burden. Conversely, if the user is relaxed, the real-time analysis unit can increase the frequency of real-time analysis to provide more detailed information. Furthermore, if the user is agitated, the real-time analysis unit can adjust the frequency of real-time analysis to provide appropriate information. In this way, by adjusting the frequency of real-time analysis based on the user's emotions, the user's burden can be reduced and more appropriate information can be provided. 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 real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input user emotion data into the generative AI and have the generative AI adjust the frequency of real-time analysis.

[0100] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can delay the timing of suggestions to reduce the user's burden. Conversely, if the user is relaxed, the suggestion unit can advance the timing of suggestions and provide more detailed information. Furthermore, if the user is excited, the suggestion unit can adjust the timing of suggestions to provide appropriate information. By adjusting the timing of suggestions based on the user's emotions, the user's burden is reduced and more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the timing of suggestions.

[0101] The data collection unit can select the target of data collection based on the user's hobbies and interests. For example, if the user is interested in music, the data collection unit will prioritize collecting music-related data. Similarly, if the user is interested in sports, the data collection unit can prioritize collecting sports-related data. Furthermore, if the user is interested in travel, the data collection unit can prioritize collecting travel-related data. This allows for the collection of more relevant data by selecting the target of data collection based on the user's hobbies and interests. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's hobbies and interests into a generating AI and have the generating AI select the target of data collection.

[0102] The analysis unit can analyze a user's past behavioral data and predict their future behavior. For example, it can analyze a user's past purchase data to predict the next product they are most likely to buy. It can also analyze a user's past travel data to predict the next place they are most likely to visit. Furthermore, it can analyze a user's past chat data to predict the next topic they are most likely to discuss. By analyzing a user's past behavioral data, it becomes possible to predict their future behavior and provide more appropriate suggestions. 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 input a user's past behavioral data into a generating AI and have the generating AI perform predictions of future behavior.

[0103] The suggestion unit can analyze past user feedback to improve the accuracy of its suggestions. For example, it can analyze suggestions that users have previously given high ratings to and make similar suggestions. It can also analyze suggestions that users have previously given low ratings to avoid making similar suggestions. Furthermore, the suggestion unit can analyze past user feedback to adjust the content of its suggestions. By analyzing past user feedback, it is possible to improve the accuracy of suggestions and make more appropriate suggestions. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input past user feedback into a generation AI and have the generation AI perform the task of improving the accuracy of suggestions.

[0104] The real-time analysis unit can perform real-time analysis while considering the user's current activity status. For example, if the user is exercising, the real-time analysis unit will prioritize analyzing data related to exercise. It can also prioritize analyzing data related to work if the user is working. Furthermore, if the user is resting, the real-time analysis unit can prioritize analyzing data related to relaxation. This allows for more appropriate real-time analysis by considering the user's current activity status. Some or all of the above processing in the real-time analysis unit may be performed using AI, for example, or without AI. For example, the real-time analysis unit can input the user's current activity status into a generating AI and have the generating AI perform the real-time analysis.

[0105] The suggestion unit can adjust the content of its suggestions by taking into account the user's geographical location. For example, the suggestion unit can suggest products and services related to the user's current location. It can also suggest nearby stores and services based on the user's geographical location. Furthermore, the suggestion unit can suggest region-specific promotional information by taking into account the user's location. This makes it possible to make more relevant suggestions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location into a generating AI and have the generating AI adjust the content of the suggestions.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The data collection unit collects user data. For example, the data collection unit can analyze chat content from messaging apps to extract user needs and event information. It can also analyze purchase data from electronic payment systems to learn user purchasing patterns and preferences. Furthermore, the data collection unit can collect user location information and behavioral data. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use natural language processing technology to analyze chat content from messaging apps and extract user needs and event information. The analysis unit can also use machine learning technology to analyze purchase data from electronic payment systems and learn user purchasing patterns and preferences. Step 3: The proposal unit makes proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit can use a recommender system to recommend relevant products and services based on learned user preferences and behavioral patterns. Step 4: The real-time analysis unit analyzes the user's location information and behavioral data in real time and makes appropriate suggestions on the spot. For example, the real-time analysis unit can recommend nearby stores and services based on the user's current location information.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, and real-time analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 38B of the smart device 14 and analyzes the chat content of the messaging application using the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts user needs and event information using natural language processing technology. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends relevant products and services using a recommender system. The real-time analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the user's location information and behavioral data in real time and makes appropriate suggestions on the spot. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, and real-time analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the chat content of the messaging application using the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts user needs and event information using natural language processing technology. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends relevant products and services using a recommender system. The real-time analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the user's location information and behavioral data in real time and makes appropriate suggestions on the spot. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, and real-time analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes the chat content of the messaging application using the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts user needs and event information using natural language processing technology. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends relevant products and services using a recommender system. The real-time analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the user's location information and behavioral data in real time and makes appropriate suggestions on the spot. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[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 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.

[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 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.

[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 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.

[0160] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, and real-time analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user data using the camera 42 and microphone 238 of the robot 414, and the control unit 46A analyzes the chat content of the messaging application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts user needs and event information using natural language processing technology. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends relevant products and services using a recommender system. The real-time analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the user's location information and behavioral data in real time and makes appropriate suggestions on the spot. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] (Note 1) A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, It includes a real-time analysis unit that analyzes user location information and behavioral data in real time. A system characterized by the following features. (Note 2) The aforementioned collection unit is Analyze chat content from messaging apps to extract user needs and event information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is By analyzing purchase data from electronic payment systems, we learn users' purchasing patterns and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on learned user preferences and behavioral patterns, it recommends relevant products and services. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned real-time analysis unit, We analyze user location and behavioral data in real time and provide appropriate suggestions on the spot. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past chat history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current interests and events. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the products are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned real-time analysis unit, It estimates user sentiment and adjusts real-time analysis criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned real-time analysis unit, During real-time analysis, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned real-time analysis unit, It estimates the user's sentiment and adjusts the order in which real-time analysis results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned real-time analysis unit, When performing real-time analysis, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned real-time analysis unit, During real-time analysis, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0180] 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 data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, It includes a real-time analysis unit that analyzes user location information and behavioral data in real time. A system characterized by the following features.

2. The aforementioned collection unit is Analyze chat content from messaging apps to extract user needs and event information. The system according to feature 1.

3. The aforementioned collection unit is By analyzing purchase data from electronic payment systems, we learn users' purchasing patterns and preferences. The system according to feature 1.

4. The aforementioned proposal section is, Based on learned user preferences and behavioral patterns, it recommends relevant products and services. The system according to feature 1.

5. The aforementioned real-time analysis unit, We analyze user location and behavioral data in real time and provide appropriate suggestions on the spot. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current interests and events. The system according to feature 1.

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