system

Abstract

The system according to this embodiment aims to analyze the user's past data and provide feedback in real time. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a feedback unit. The data collection unit collects the user's past data. The analysis unit analyzes the data collected by the data collection unit. The feedback unit provides real-time feedback of the analysis results obtained by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , ,

[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, real-time feedback based on the user's past data is not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's past data and provide real-time feedback.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects the user's past data. The analysis unit analyzes the data collected by the collection unit. The feedback unit provides real-time feedback on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's past data and provide feedback 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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device, 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 preference change simulation system according to an embodiment of the present invention is a system that simulates changes in a user's preferences using past data and physiological responses, and provides real-time feedback. This system collects the user's past data, analyzes it using AI, and provides real-time feedback. For example, the preference change simulation system collects the user's chat data, search history, physiological response data from a smartwatch, etc. Next, the preference change simulation system analyzes the collected data using AI. The AI ​​integrates past data and real-time physiological responses to analyze changes in the user's preferences with high accuracy. Furthermore, the preference change simulation system provides real-time feedback to the user based on the analysis results. For example, by understanding their own interests and concerns and communicating accordingly, users can improve their relationships with others. Also, by understanding changes in their preferences in real time, users can promote self-growth. In this way, the preference change simulation system can analyze changes in the user's preferences with high accuracy and deepen self-understanding.

[0029] The preference change simulation system according to this embodiment comprises a data collection unit, an analysis unit, and a feedback unit. The data collection unit collects the user's past data. The data collection unit can collect, for example, chat data, search history, and physiological response data. For example, the data collection unit can obtain chat data via an API and search history from browser history data. The data collection unit can also obtain physiological response data from a smartwatch via Bluetooth®. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to integrate past data with real-time physiological responses and analyzes changes in the user's preferences with high accuracy. For example, the analysis unit uses machine learning algorithms to analyze the data and predict changes in the user's interests and concerns. The analysis unit can also use natural language processing technology to analyze chat data and detect changes in the user's emotions and topics. The feedback unit provides real-time feedback of the analysis results obtained by the analysis unit. For example, the feedback unit sends a notification to the user's smartphone and displays the analysis results. The feedback unit can also provide feedback to the user's smartwatch via vibration or voice. The feedback unit, for example, visually displays changes in the user's preferences using graphs and charts, helping the user deepen their self-understanding. As a result, the preference change simulation system according to this embodiment can analyze changes in the user's preferences with high accuracy and deepen their self-understanding.

[0030] The data collection unit collects the user's past data. Specifically, the data collection unit can collect chat data, search history, physiological response data, and more. Chat data is obtained via an API and used to analyze the user's conversation content, frequency of use, and frequency of occurrence of specific keywords. Search history is collected from browser history data and includes information on keywords the user has searched for in the past and websites they have visited. This allows for an understanding of changes in the user's interests and concerns. Furthermore, the data collection unit obtains physiological response data from smartwatches via Bluetooth. Smartwatches monitor physiological data such as heart rate, skin temperature, and stress levels in real time and transmit this data to the data collection unit. This allows for an understanding of changes in the user's emotional state and stress levels. The data collection unit centrally manages this diverse data and stores it in a database for provision to the analysis unit. The frequency and accuracy of data collection can be adjusted according to user settings and system requirements, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to efficiently and effectively collect diverse user data and improve the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it uses AI to integrate historical data with real-time physiological responses to analyze changes in user preferences with high accuracy. For example, it uses machine learning algorithms to analyze data and predict changes in user interests and concerns. Machine learning algorithms learn patterns from historical data and predict future trends based on new data. Furthermore, it uses natural language processing technology to analyze chat data and detect changes in user emotions and topics. Natural language processing technology is used to extract emotions and intentions from text data and understand changes in the user's psychological state and interests. The analysis unit combines these technologies to analyze changes in user preferences from multiple angles and provide more accurate results. In addition, by integrating historical and real-time data, the analysis unit tracks changes in user preferences in real time and enables rapid feedback. This allows the analysis unit to analyze changes in user preferences with high accuracy and improve the reliability and usefulness of the entire system.

[0032] The feedback unit provides real-time feedback on the analysis results obtained by the analysis unit. Specifically, it sends notifications to the user's smartphone displaying the analysis results. Notifications are provided in the form of text messages, graphs, charts, etc., allowing the user to intuitively understand the analysis results. The feedback unit can also provide feedback to the user's smartwatch via vibration or voice. For example, if the user's stress level is high, the smartwatch will vibrate to draw attention and provide advice on how to relax. Furthermore, the feedback unit visually displays changes in the user's preferences in graphs and charts, helping the user deepen their self-understanding. This allows the user to grasp changes in their interests and preferences, and use this information for self-improvement or to discover new interests. The feedback unit can also collect user feedback and continuously improve the accuracy and usefulness of the analysis results. For example, based on the feedback provided by the user, it adjusts the analysis algorithm and the format of the feedback to provide more effective feedback. In this way, the feedback unit can provide users with quick and appropriate feedback, improving the overall usefulness of the system and user satisfaction.

[0033] The data collection unit can collect chat data, search history, and physiological response data. For example, the data collection unit can obtain chat data via an API and collect chat history for a specific period. The data collection unit can also collect search history from browser history data and extract search history containing specific keywords. Furthermore, the data collection unit can obtain physiological response data from a smartwatch via Bluetooth and collect data such as heart rate and skin electrical activity. This allows for a more accurate analysis of changes in user preferences by collecting data from diverse data sources. 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 chat data obtained via an API into an AI and have the AI ​​perform the process of extracting chat history containing specific keywords.

[0034] The analysis unit can analyze changes in user preferences with high accuracy by integrating historical data with real-time physiological responses. For example, the analysis unit can use machine learning algorithms to integrate historical data with real-time physiological responses and predict changes in user interests and preferences. The analysis unit can also use natural language processing technology to analyze chat data and detect changes in user emotions and topics. For example, the analysis unit can integrate historical chat data with real-time heart rate data to analyze changes in the user's stress level. The analysis unit can also integrate historical search history with real-time skin electrical activity data to analyze changes in user interests. This allows for highly accurate analysis of changes in user preferences by integrating historical data with real-time physiological responses. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input historical data and real-time physiological response data into an AI and have the AI ​​perform the process of analyzing changes in user preferences.

[0035] The feedback unit can provide real-time feedback to the user. For example, the feedback unit can send notifications to the user's smartphone and display analysis results. It can also provide feedback to the user's smartwatch via vibration or sound. For example, the feedback unit can visually display changes in the user's preferences using graphs and charts, helping the user deepen their self-understanding. The feedback unit can also notify the user of changes in their interests and concerns in real time, supporting them in taking action in response to those changes. By providing real-time feedback, users can deepen their self-understanding and build more satisfying relationships. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input analysis results into AI and have the AI ​​perform the process of generating appropriate feedback for the user.

[0036] The analysis unit can determine the degree of interest or concern in a particular topic. For example, the analysis unit can analyze chat data using natural language processing technology to determine the degree of user interest or concern in a particular topic. The analysis unit can also analyze search history using machine learning algorithms to predict changes in user interest in a particular topic. For example, if a user frequently searches for a particular keyword, the analysis unit can determine that the user has a high level of interest in that topic. The analysis unit can also determine that a user has a high level of interest in a particular topic if they engage in many chats about that topic. This allows for a more detailed analysis of changes in user preferences by determining the degree of interest or concern in a particular topic. 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 chat data and search history into an AI and have the AI ​​perform the process of determining the degree of interest or concern in a particular topic.

[0037] The feedback section can visually display changes in user preferences. For example, the feedback section can visually display changes in user preferences using graphs and charts, helping users deepen their self-understanding. The feedback section can also display changes in user interests and preferences in a dashboard format, allowing users to grasp these changes at a glance. For example, the feedback section can display changes in user preferences along a timeline, visually showing changes from the past to the present. The feedback section can also express the degree of user interest and preference using color and size, making it easy to understand visually. This makes it easier for users to deepen their self-understanding by visually displaying changes in user preferences. Some or all of the above processing in the feedback section may be performed using AI or not. For example, the feedback section can input analysis results into AI and have the AI ​​perform the process of generating graphs and charts for visual display.

[0038] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit may prioritize using data collection methods that the user has frequently used in the past (e.g., specific time slots or devices). The collection unit can also identify the most effective data collection timing from the user's past data collection history and create a collection plan. For example, the collection unit may analyze the user's history of collecting data at specific time slots and perform data collection during those times. The collection unit may also analyze the user's history of using specific devices to collect data and prioritize the use of those devices. Furthermore, the collection unit may analyze the user's past data collection history and propose new collection methods to improve data quality. This allows for the selection of the optimal collection method and improvement of data quality by analyzing the user's past data collection history. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit may input past data collection history into AI and have the AI ​​perform the process of selecting the optimal collection method.

[0039] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is working, the unit can collect only work-related data and filter out other data. The unit can also prioritize collecting data related to a hobby if the user is engrossed in that hobby. For example, if the user is working, the unit can prioritize collecting data containing work-related keywords. Furthermore, if the user is on a break, the unit can collect data related to relaxation and filter out data to help avoid stress. This allows for the collection of highly relevant data by filtering data based on the user's current activities and areas of interest. 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 the user's current activities and areas of interest into an AI and have the AI ​​perform the filtering process.

[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, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of data around their home. 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, or not. For example, the data collection unit can input the user's geographical location information into an AI and have the AI ​​perform the process of prioritizing 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, if a user frequently posts on social media about a particular topic, the data collection unit will prioritize collecting data related to that topic. The data collection unit can also collect data related to an event if the user participates in that event. Furthermore, if the user belongs to a particular group, the data collection unit can collect data related to that group. This allows for the collection of relevant data and a more accurate analysis of changes in the user's preferences by analyzing their social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into an AI and have the AI ​​perform the process of collecting 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 can evaluate the importance of the data and perform a detailed analysis on highly important data to provide deep insights. The analysis unit can also perform a simplified analysis on less important data to provide only the minimum necessary information. For example, the analysis unit can perform a detailed analysis on highly important data to provide deep insights. The analysis unit can also perform a simplified analysis on less important data to provide only the minimum necessary information. Furthermore, the analysis unit can perform an analysis of moderately important data to provide balanced information. In this way, by adjusting the level of detail of the analysis based on the importance of the data, the necessary information can be provided at the appropriate level of detail. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the process of adjusting 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 natural language processing algorithm to text data to perform semantic analysis. Furthermore, the analysis unit can apply a biometric analysis algorithm to physiological response data to evaluate health status. In addition, the analysis unit can apply a Geographic Information System (GIS) algorithm to location data to analyze movement patterns. This allows for the application of different analysis algorithms depending on the data category, providing optimal analysis results for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input text data, physiological response data, and location data into an AI and have the AI ​​perform the process of applying the appropriate analysis algorithm for each category.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and provide real-time feedback. The analysis unit can also analyze historical data to grasp long-term trends. For example, the analysis unit can prioritize the analysis of the most recent data and provide real-time feedback. The analysis unit can also analyze historical data to grasp long-term trends. Furthermore, the analysis unit can analyze data for a specific period and evaluate specific events or situations during that period. This enables real-time feedback and the understanding of long-term trends by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​perform the process of determining the analysis priority.

[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 to provide important insights. It can also analyze data with moderate relevance next to provide supplementary information. For example, the analysis unit can prioritize the analysis of highly relevant data to provide important insights. It can also analyze data with moderate relevance next to provide supplementary information. Furthermore, the analysis unit can analyze data with low relevance last to maintain overall balance. This allows for the priority provision of important insights by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the process of adjusting the order of analysis.

[0046] The feedback unit can analyze the user's past feedback history to select the optimal feedback method when providing feedback. For example, the feedback unit may prioritize using feedback methods that the user has preferred in the past. Furthermore, the feedback unit can identify and apply the most effective feedback method from the user's past feedback history. In addition, the feedback unit can analyze the user's past feedback history and propose new feedback methods. This allows the feedback unit to select the optimal feedback method and provide effective feedback by analyzing the user's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input past feedback history into an AI and have the AI ​​perform the process of selecting the optimal feedback method.

[0047] The feedback unit can customize the content of feedback based on the user's current situation. For example, if the user is working, the feedback unit will provide work-related feedback. It can also provide hobby-related feedback if the user is engrossed in a hobby. Furthermore, if the user is on a break, the feedback unit can provide relaxation-related feedback. This allows the feedback unit to provide optimal feedback by customizing the content based on the user's current situation. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's current situation into the AI ​​and have the AI ​​perform the process of customizing the feedback content.

[0048] The feedback unit can select the optimal feedback method by considering the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit can provide feedback relevant to that region. Furthermore, if the user is traveling, the feedback unit can provide feedback relevant to their travel destination. Additionally, if the user is at home, the feedback unit can provide feedback about their surroundings. This allows for the provision of highly relevant feedback by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's geographical location information into an AI and have the AI ​​perform the process of selecting the optimal feedback method.

[0049] The feedback unit can analyze the user's social media activity and suggest feedback content when providing feedback. For example, if the user frequently posts on social media about a particular topic, the feedback unit can provide feedback related to that topic. Furthermore, if the user participates in a particular event, the feedback unit can provide feedback related to that event. Additionally, if the user belongs to a particular group, the feedback unit can provide feedback related to that group. This allows for more accurate analysis of changes in the user's preferences by providing relevant feedback through analysis of the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's social media activity into an AI and have the AI ​​perform the process of suggesting feedback content.

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

[0051] The preference change simulation system can also collect and analyze the user's music playback history. The collection unit retrieves the user's playback history from music streaming services such as Spotify and Apple Music. The analysis unit uses the collected music playback history to analyze the user's musical preferences and how they change. For example, if a user frequently plays a particular genre of music, it can be determined that they have a high level of interest in that genre. The feedback unit can also suggest new music to the user based on the analysis results. This allows the system to understand changes in the user's musical preferences and provide a more personalized music experience.

[0052] The preference change simulation system can also collect and analyze users' purchase history. The collection unit retrieves user purchase history from online shopping sites such as Amazon and Rakuten. The analysis unit uses the collected purchase history to analyze the user's purchasing trends and how they change. For example, if a user frequently purchases products from a particular brand, it can be determined that the user has a high level of interest in that brand. The feedback unit can also suggest new products to the user based on the analysis results. This allows the system to understand changes in user purchasing trends and provide a more personalized shopping experience.

[0053] The preference change simulation system can also collect and analyze user exercise data. The data collection unit acquires user exercise data from, for example, fitness trackers or smartwatches. The analysis unit uses the collected exercise data to analyze the user's exercise habits and how they change. For example, if a user frequently performs a particular exercise, it can be determined that they have a high level of interest in that exercise. Furthermore, the feedback unit can suggest new exercise programs to the user based on the analysis results. This allows the system to understand changes in the user's exercise habits and provide a more personalized fitness experience.

[0054] The preference change simulation system can also collect and analyze the user's reading history. The collection unit retrieves the user's reading history from e-readers such as Kindle and Kobo. The analysis unit uses the collected reading history to analyze the user's reading preferences and how they change. For example, if a user frequently reads books of a particular genre, it can be determined that they have a high level of interest in that genre. The feedback unit can also suggest new books to the user based on the analysis results. This allows the system to understand changes in the user's reading preferences and provide a more personalized reading experience.

[0055] The preference change simulation system can also collect and analyze users' travel history. The collection unit obtains user travel history from sources such as travel booking websites and airline apps. The analysis unit uses the collected travel history to analyze the user's travel preferences and how they change. For example, if a user frequently visits a particular region, it can be determined that they have a high level of interest in that region. The feedback unit can also suggest new travel destinations to the user based on the analysis results. This allows the system to understand changes in the user's travel preferences and provide a more personalized travel experience.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects the user's past data. For example, it retrieves chat data via an API and collects search history from browser history data. It can also acquire physiological response data from a smartwatch via Bluetooth. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to integrate historical data with real-time physiological responses to analyze changes in user preferences with high accuracy. It also uses machine learning algorithms to analyze data and predict changes in user interests and concerns. Furthermore, it can use natural language processing technology to analyze chat data and detect changes in user emotions and topics. Step 3: The feedback unit provides real-time feedback on the analysis results obtained by the analysis unit. For example, it sends a notification to the user's smartphone and displays the analysis results. It can also provide feedback via vibration or sound to the user's smartwatch. Furthermore, it visually displays changes in the user's preferences in graphs and charts to help the user deepen their self-understanding.

[0058] (Example of form 2) The preference change simulation system according to an embodiment of the present invention is a system that simulates changes in a user's preferences using past data and physiological responses, and provides real-time feedback. This system collects the user's past data, analyzes it using AI, and provides real-time feedback. For example, the preference change simulation system collects the user's chat data, search history, physiological response data from a smartwatch, etc. Next, the preference change simulation system analyzes the collected data using AI. The AI ​​integrates past data and real-time physiological responses to analyze changes in the user's preferences with high accuracy. Furthermore, the preference change simulation system provides real-time feedback to the user based on the analysis results. For example, by understanding their own interests and concerns and communicating accordingly, users can improve their relationships with others. Also, by understanding changes in their preferences in real time, users can promote self-growth. In this way, the preference change simulation system can analyze changes in the user's preferences with high accuracy and deepen self-understanding.

[0059] The preference change simulation system according to this embodiment comprises a data collection unit, an analysis unit, and a feedback unit. The data collection unit collects the user's past data. The data collection unit can collect, for example, chat data, search history, and physiological response data. For example, the data collection unit can obtain chat data via an API and search history from browser history data. The data collection unit can also obtain physiological response data from a smartwatch via Bluetooth. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to integrate past data with real-time physiological responses and analyzes changes in the user's preferences with high accuracy. For example, the analysis unit uses machine learning algorithms to analyze the data and predict changes in the user's interests and concerns. The analysis unit can also use natural language processing technology to analyze chat data and detect changes in the user's emotions and topics. The feedback unit provides real-time feedback of the analysis results obtained by the analysis unit. For example, the feedback unit sends a notification to the user's smartphone and displays the analysis results. The feedback unit can also provide feedback to the user's smartwatch via vibration or voice. The feedback unit, for example, visually displays changes in the user's preferences using graphs and charts, helping the user deepen their self-understanding. As a result, the preference change simulation system according to this embodiment can analyze changes in the user's preferences with high accuracy and deepen their self-understanding.

[0060] The data collection unit collects the user's past data. Specifically, the data collection unit can collect chat data, search history, physiological response data, and more. Chat data is obtained via an API and used to analyze the user's conversation content, frequency of use, and frequency of occurrence of specific keywords. Search history is collected from browser history data and includes information on keywords the user has searched for in the past and websites they have visited. This allows for an understanding of changes in the user's interests and concerns. Furthermore, the data collection unit obtains physiological response data from smartwatches via Bluetooth. Smartwatches monitor physiological data such as heart rate, skin temperature, and stress levels in real time and transmit this data to the data collection unit. This allows for an understanding of changes in the user's emotional state and stress levels. The data collection unit centrally manages this diverse data and stores it in a database for provision to the analysis unit. The frequency and accuracy of data collection can be adjusted according to user settings and system requirements, allowing for flexible responses to specific situations and conditions. This enables the data collection unit to efficiently and effectively collect diverse user data and improve the overall system performance.

[0061] The analysis unit analyzes the data collected by the data collection unit. Specifically, it uses AI to integrate historical data with real-time physiological responses to analyze changes in user preferences with high accuracy. For example, it uses machine learning algorithms to analyze data and predict changes in user interests and concerns. Machine learning algorithms learn patterns from historical data and predict future trends based on new data. Furthermore, it uses natural language processing technology to analyze chat data and detect changes in user emotions and topics. Natural language processing technology is used to extract emotions and intentions from text data and understand changes in the user's psychological state and interests. The analysis unit combines these technologies to analyze changes in user preferences from multiple angles and provide more accurate results. In addition, by integrating historical and real-time data, the analysis unit tracks changes in user preferences in real time and enables rapid feedback. This allows the analysis unit to analyze changes in user preferences with high accuracy and improve the reliability and usefulness of the entire system.

[0062] The feedback unit provides real-time feedback on the analysis results obtained by the analysis unit. Specifically, it sends notifications to the user's smartphone displaying the analysis results. Notifications are provided in the form of text messages, graphs, charts, etc., allowing the user to intuitively understand the analysis results. The feedback unit can also provide feedback to the user's smartwatch via vibration or voice. For example, if the user's stress level is high, the smartwatch will vibrate to draw attention and provide advice on how to relax. Furthermore, the feedback unit visually displays changes in the user's preferences in graphs and charts, helping the user deepen their self-understanding. This allows the user to grasp changes in their interests and preferences, and use this information for self-improvement or to discover new interests. The feedback unit can also collect user feedback and continuously improve the accuracy and usefulness of the analysis results. For example, based on the feedback provided by the user, it adjusts the analysis algorithm and the format of the feedback to provide more effective feedback. In this way, the feedback unit can provide users with quick and appropriate feedback, improving the overall usefulness of the system and user satisfaction.

[0063] The data collection unit can collect chat data, search history, and physiological response data. For example, the data collection unit can obtain chat data via an API and collect chat history for a specific period. The data collection unit can also collect search history from browser history data and extract search history containing specific keywords. Furthermore, the data collection unit can obtain physiological response data from a smartwatch via Bluetooth and collect data such as heart rate and skin electrical activity. This allows for a more accurate analysis of changes in user preferences by collecting data from diverse data sources. 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 chat data obtained via an API into an AI and have the AI ​​perform the process of extracting chat history containing specific keywords.

[0064] The analysis unit can analyze changes in user preferences with high accuracy by integrating historical data with real-time physiological responses. For example, the analysis unit can use machine learning algorithms to integrate historical data with real-time physiological responses and predict changes in user interests and preferences. The analysis unit can also use natural language processing technology to analyze chat data and detect changes in user emotions and topics. For example, the analysis unit can integrate historical chat data with real-time heart rate data to analyze changes in the user's stress level. The analysis unit can also integrate historical search history with real-time skin electrical activity data to analyze changes in user interests. This allows for highly accurate analysis of changes in user preferences by integrating historical data with real-time physiological responses. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input historical data and real-time physiological response data into an AI and have the AI ​​perform the process of analyzing changes in user preferences.

[0065] The feedback unit can provide real-time feedback to the user. For example, the feedback unit can send notifications to the user's smartphone and display analysis results. It can also provide feedback to the user's smartwatch via vibration or sound. For example, the feedback unit can visually display changes in the user's preferences using graphs and charts, helping the user deepen their self-understanding. The feedback unit can also notify the user of changes in their interests and concerns in real time, supporting them in taking action in response to those changes. By providing real-time feedback, users can deepen their self-understanding and build more satisfying relationships. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input analysis results into AI and have the AI ​​perform the process of generating appropriate feedback for the user.

[0066] The analysis unit can determine the degree of interest or concern in a particular topic. For example, the analysis unit can analyze chat data using natural language processing technology to determine the degree of user interest or concern in a particular topic. The analysis unit can also analyze search history using machine learning algorithms to predict changes in user interest in a particular topic. For example, if a user frequently searches for a particular keyword, the analysis unit can determine that the user has a high level of interest in that topic. The analysis unit can also determine that a user has a high level of interest in a particular topic if they engage in many chats about that topic. This allows for a more detailed analysis of changes in user preferences by determining the degree of interest or concern in a particular topic. 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 chat data and search history into an AI and have the AI ​​perform the process of determining the degree of interest or concern in a particular topic.

[0067] The feedback section can visually display changes in user preferences. For example, the feedback section can visually display changes in user preferences using graphs and charts, helping users deepen their self-understanding. The feedback section can also display changes in user interests and preferences in a dashboard format, allowing users to grasp these changes at a glance. For example, the feedback section can display changes in user preferences along a timeline, visually showing changes from the past to the present. The feedback section can also express the degree of user interest and preference using color and size, making it easy to understand visually. This makes it easier for users to deepen their self-understanding by visually displaying changes in user preferences. Some or all of the above processing in the feedback section may be performed using AI or not. For example, the feedback section can input analysis results into AI and have the AI ​​perform the process of generating graphs and charts for visual display.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using facial recognition technology and reduce the frequency of data collection if the user is stressed. Alternatively, the data collection unit can estimate the user's emotions using voice analysis technology and increase the frequency of data collection if the user is relaxed. For example, if the data collection unit is stressed, it can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, it can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is excited, the data collection unit can collect data in real time, enabling immediate feedback. This reduces the user's burden and allows for the collection of detailed data by adjusting the timing of data collection 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user image data acquired using facial recognition technology into a generating AI, which can then perform the estimation of the user's emotions.

[0069] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit may prioritize using data collection methods that the user has frequently used in the past (e.g., specific time slots or devices). The collection unit can also identify the most effective data collection timing from the user's past data collection history and create a collection plan. For example, the collection unit may analyze the user's history of collecting data at specific time slots and perform data collection during those times. The collection unit may also analyze the user's history of using specific devices to collect data and prioritize the use of those devices. Furthermore, the collection unit may analyze the user's past data collection history and propose new collection methods to improve data quality. This allows for the selection of the optimal collection method and improvement of data quality by analyzing the user's past data collection history. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit may input past data collection history into AI and have the AI ​​perform the process of selecting the optimal collection method.

[0070] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is working, the unit can collect only work-related data and filter out other data. The unit can also prioritize collecting data related to a hobby if the user is engrossed in that hobby. For example, if the user is working, the unit can prioritize collecting data containing work-related keywords. Furthermore, if the user is on a break, the unit can collect data related to relaxation and filter out data to help avoid stress. This allows for the collection of highly relevant data by filtering data based on the user's current activities and areas of interest. 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 the user's current activities and areas of interest into an AI and have the AI ​​perform the filtering process.

[0071] 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, the data collection unit can estimate the user's emotions using facial recognition technology and prioritize collecting data that helps reduce stress if the user is stressed. Alternatively, the data collection unit can estimate the user's emotions using voice analysis technology and prioritize collecting data that helps maintain relaxation if the user is relaxed. For example, if the user is stressed, the data collection unit will prioritize collecting data that helps reduce stress. If the user is relaxed, the data collection unit will also prioritize collecting data that helps maintain relaxation. Furthermore, if the user is excited, the data collection unit will also prioritize collecting data that helps sustain excitement. By prioritizing the data to collect based on the user's emotions, the most important data for the user can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user image data acquired using facial recognition technology into a generating AI, which can then perform the estimation of the user's emotions.

[0072] 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, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of data around their home. 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, or not. For example, the data collection unit can input the user's geographical location information into an AI and have the AI ​​perform the process of prioritizing the collection of highly relevant data.

[0073] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user frequently posts on social media about a particular topic, the data collection unit will prioritize collecting data related to that topic. The data collection unit can also collect data related to an event if the user participates in that event. Furthermore, if the user belongs to a particular group, the data collection unit can collect data related to that group. This allows for the collection of relevant data and a more accurate analysis of changes in the user's preferences by analyzing their social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into an AI and have the AI ​​perform the process of collecting relevant data.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology and provide simple, visually easy-to-understand analysis results if the user is stressed. The analysis unit can also estimate the user's emotions using voice analysis technology and provide detailed analysis results if the user is relaxed. For example, if the user is stressed, the analysis unit provides simple, visually easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results and deeper insights. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. 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 or not. For example, the analysis unit can input user image data acquired using facial recognition technology into a generating AI, and have the generating AI perform the estimation of the user's emotions.

[0075] 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 can evaluate the importance of the data and perform a detailed analysis on highly important data to provide deep insights. The analysis unit can also perform a simplified analysis on less important data to provide only the minimum necessary information. For example, the analysis unit can perform a detailed analysis on highly important data to provide deep insights. The analysis unit can also perform a simplified analysis on less important data to provide only the minimum necessary information. Furthermore, the analysis unit can perform an analysis of moderately important data to provide balanced information. In this way, by adjusting the level of detail of the analysis based on the importance of the data, the necessary information can be provided at the appropriate level of detail. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the process of adjusting the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data to perform semantic analysis. Furthermore, the analysis unit can apply a biometric analysis algorithm to physiological response data to evaluate health status. In addition, the analysis unit can apply a Geographic Information System (GIS) algorithm to location data to analyze movement patterns. This allows for the application of different analysis algorithms depending on the data category, providing optimal analysis results for each category. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input text data, physiological response data, and location data into an AI and have the AI ​​perform the process of applying the appropriate analysis algorithm for each category.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology and provide a short, concise analysis result if the user is in a hurry. Alternatively, the analysis unit can estimate the user's emotions using voice analysis technology and provide a detailed analysis result if the user is relaxed. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result, offering deeper insights. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, appropriate analysis results can be provided according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user image data acquired using facial recognition technology into a generating AI, and have the generating AI perform the estimation of the user's emotions.

[0078] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and provide real-time feedback. The analysis unit can also analyze historical data to grasp long-term trends. For example, the analysis unit can prioritize the analysis of the most recent data and provide real-time feedback. The analysis unit can also analyze historical data to grasp long-term trends. Furthermore, the analysis unit can analyze data for a specific period and evaluate specific events or situations during that period. This enables real-time feedback and the understanding of long-term trends by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​perform the process of determining the analysis priority.

[0079] 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 to provide important insights. It can also analyze data with moderate relevance next to provide supplementary information. For example, the analysis unit can prioritize the analysis of highly relevant data to provide important insights. It can also analyze data with moderate relevance next to provide supplementary information. Furthermore, the analysis unit can analyze data with low relevance last to maintain overall balance. This allows for the priority provision of important insights by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the process of adjusting the order of analysis.

[0080] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, the feedback unit can estimate the user's emotions using facial recognition technology and provide simple, visually easy-to-understand feedback if the user is stressed. Alternatively, the feedback unit can estimate the user's emotions using voice analysis technology and provide detailed feedback if the user is relaxed. For example, if the user is stressed, the feedback unit provides simple, visually easy-to-understand feedback. If the user is relaxed, the feedback unit can provide detailed feedback and deeper insights. Furthermore, if the user is excited, the feedback unit can provide visually stimulating feedback. This allows the system to provide optimal feedback to the user by adjusting the feedback method based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input user image data acquired using facial recognition technology into a generating AI, allowing the generating AI to estimate the user's emotions.

[0081] The feedback unit can analyze the user's past feedback history to select the optimal feedback method when providing feedback. For example, the feedback unit may prioritize using feedback methods that the user has preferred in the past. Furthermore, the feedback unit can identify and apply the most effective feedback method from the user's past feedback history. In addition, the feedback unit can analyze the user's past feedback history and propose new feedback methods. This allows the feedback unit to select the optimal feedback method and provide effective feedback by analyzing the user's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input past feedback history into an AI and have the AI ​​perform the process of selecting the optimal feedback method.

[0082] The feedback unit can customize the content of feedback based on the user's current situation. For example, if the user is working, the feedback unit will provide work-related feedback. It can also provide hobby-related feedback if the user is engrossed in a hobby. Furthermore, if the user is on a break, the feedback unit can provide relaxation-related feedback. This allows the feedback unit to provide optimal feedback by customizing the content based on the user's current situation. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's current situation into the AI ​​and have the AI ​​perform the process of customizing the feedback content.

[0083] The feedback unit can estimate the user's emotions and prioritize feedback based on the estimated emotions. For example, the feedback unit can estimate the user's emotions using facial recognition technology and prioritize providing feedback that helps reduce stress if the user is feeling stressed. Alternatively, the feedback unit can estimate the user's emotions using voice analysis technology and prioritize providing feedback that helps maintain relaxation if the user is relaxed. For example, if the user is feeling stressed, the feedback unit will prioritize providing feedback that helps reduce stress. If the user is relaxed, the feedback unit will also prioritize providing feedback that helps maintain relaxation. Furthermore, if the user is excited, the feedback unit will also prioritize providing feedback that helps sustain excitement. By prioritizing feedback based on the user's emotions, the feedback unit can prioritize providing the most important feedback to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user image data acquired using facial recognition technology into a generating AI, allowing the generating AI to estimate the user's emotions.

[0084] The feedback unit can select the optimal feedback method by considering the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit can provide feedback relevant to that region. Furthermore, if the user is traveling, the feedback unit can provide feedback relevant to their travel destination. Additionally, if the user is at home, the feedback unit can provide feedback about their surroundings. This allows for the provision of highly relevant feedback by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's geographical location information into an AI and have the AI ​​perform the process of selecting the optimal feedback method.

[0085] The feedback unit can analyze the user's social media activity and suggest feedback content when providing feedback. For example, if the user frequently posts on social media about a particular topic, the feedback unit can provide feedback related to that topic. Furthermore, if the user participates in a particular event, the feedback unit can provide feedback related to that event. Additionally, if the user belongs to a particular group, the feedback unit can provide feedback related to that group. This allows for more accurate analysis of changes in the user's preferences by providing relevant feedback through analysis of the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's social media activity into an AI and have the AI ​​perform the process of suggesting feedback content.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The preference change simulation system can also collect and analyze the user's music playback history. The collection unit retrieves the user's playback history from music streaming services such as Spotify and Apple Music. The analysis unit uses the collected music playback history to analyze the user's musical preferences and how they change. For example, if a user frequently plays a particular genre of music, it can be determined that they have a high level of interest in that genre. The feedback unit can also suggest new music to the user based on the analysis results. This allows the system to understand changes in the user's musical preferences and provide a more personalized music experience.

[0088] The preference change simulation system can also collect and analyze users' purchase history. The collection unit retrieves user purchase history from online shopping sites such as Amazon and Rakuten. The analysis unit uses the collected purchase history to analyze the user's purchasing trends and how they change. For example, if a user frequently purchases products from a particular brand, it can be determined that the user has a high level of interest in that brand. The feedback unit can also suggest new products to the user based on the analysis results. This allows the system to understand changes in user purchasing trends and provide a more personalized shopping experience.

[0089] The preference change simulation system can also collect and analyze user exercise data. The data collection unit acquires user exercise data from, for example, fitness trackers or smartwatches. The analysis unit uses the collected exercise data to analyze the user's exercise habits and how they change. For example, if a user frequently performs a particular exercise, it can be determined that they have a high level of interest in that exercise. Furthermore, the feedback unit can suggest new exercise programs to the user based on the analysis results. This allows the system to understand changes in the user's exercise habits and provide a more personalized fitness experience.

[0090] The preference change simulation system can also collect and analyze the user's reading history. The collection unit retrieves the user's reading history from e-readers such as Kindle and Kobo. The analysis unit uses the collected reading history to analyze the user's reading preferences and how they change. For example, if a user frequently reads books of a particular genre, it can be determined that they have a high level of interest in that genre. The feedback unit can also suggest new books to the user based on the analysis results. This allows the system to understand changes in the user's reading preferences and provide a more personalized reading experience.

[0091] The preference change simulation system can also collect and analyze users' travel history. The collection unit obtains user travel history from sources such as travel booking websites and airline apps. The analysis unit uses the collected travel history to analyze the user's travel preferences and how they change. For example, if a user frequently visits a particular region, it can be determined that they have a high level of interest in that region. The feedback unit can also suggest new travel destinations to the user based on the analysis results. This allows the system to understand changes in the user's travel preferences and provide a more personalized travel experience.

[0092] The preference change simulation system can estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, the data collection unit uses facial recognition technology to estimate the user's emotions and increases the frequency of feedback when the user is relaxed. It can also use voice analysis technology to estimate the user's emotions and decrease the frequency of feedback when the user is stressed. By adjusting the timing of feedback based on the user's emotions, the system can reduce the user's burden and provide effective feedback.

[0093] The preference change simulation system can estimate the user's emotions and customize the content of the feedback based on those emotions. For example, the data collection unit uses facial recognition technology to estimate the user's emotions and provides detailed feedback if the user is excited. It can also use voice analysis technology to estimate the user's emotions and provide simple feedback if the user is relaxed. By customizing the content of the feedback based on the user's emotions, the system can provide the most optimal feedback for the user.

[0094] The preference change simulation system can estimate the user's emotions and adjust the format of feedback based on those emotions. For example, the data collection unit uses facial recognition technology to estimate the user's emotions and provides visually clear feedback if the user is feeling stressed. It can also use voice analysis technology to estimate the user's emotions and provide detailed text feedback if the user is relaxed. By adjusting the format of feedback based on the user's emotions, the system can provide the most optimal feedback for the user.

[0095] The preference change simulation system can estimate the user's emotions and prioritize feedback based on those emotions. For example, the data collection unit uses facial recognition technology to estimate the user's emotions and prioritizes providing feedback that helps reduce stress if the user is feeling stressed. It can also use voice analysis technology to estimate the user's emotions and prioritize providing feedback that helps maintain relaxation if the user is relaxed. By prioritizing feedback based on the user's emotions, the system can prioritize providing the feedback that is most important to the user.

[0096] The preference change simulation system can estimate the user's emotions and adjust the feedback method based on those emotions. For example, the data collection unit uses facial recognition technology to estimate the user's emotions and provides simple, visually easy-to-understand feedback if the user is feeling stressed. It can also use voice analysis technology to estimate the user's emotions and provide detailed feedback if the user is relaxed. By adjusting the feedback method based on the user's emotions, the system can provide the most optimal feedback for the user.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects the user's past data. For example, it retrieves chat data via an API and collects search history from browser history data. It can also acquire physiological response data from a smartwatch via Bluetooth. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to integrate historical data with real-time physiological responses to analyze changes in user preferences with high accuracy. It also uses machine learning algorithms to analyze data and predict changes in user interests and concerns. Furthermore, it can use natural language processing technology to analyze chat data and detect changes in user emotions and topics. Step 3: The feedback unit provides real-time feedback on the analysis results obtained by the analysis unit. For example, it sends a notification to the user's smartphone and displays the analysis results. It can also provide feedback via vibration or sound to the user's smartwatch. Furthermore, it visually displays changes in the user's preferences in graphs and charts to help the user deepen their self-understanding.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects chat data, search history, and physiological response data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The feedback unit is implemented by the control unit 46A of the smart device 14 and notifies the user of the analysis results in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects chat data, search history, and physiological response data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user of the analysis results in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects chat data, search history, and physiological response data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of the analysis results in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects chat data, search history, and physiological response data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The feedback unit is implemented by the control unit 46A of the robot 414 and notifies the user of the analysis results in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A data collection unit that collects the user's past data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a feedback unit that provides real-time feedback of the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect chat data, search history, and physiological response data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By integrating historical data with real-time physiological responses, we can analyze changes in user preferences with high accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Provide real-time feedback to users. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Determining the degree of interest or concern in a particular topic. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Visually display changes in user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) 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 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, 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 15) 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 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, the system analyzes the user's past feedback history to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, customize the content of the feedback based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When providing feedback, the optimal feedback method is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest content for the feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 the user's past data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a feedback unit that provides real-time feedback of the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect chat data, search history, and physiological response data. The system according to feature 1.

3. The aforementioned analysis unit, By integrating historical data with real-time physiological responses, we can analyze changes in user preferences with high accuracy. The system according to feature 1.

4. The aforementioned feedback unit is Provide real-time feedback to users. The system according to feature 1.

5. The aforementioned analysis unit, Determining the degree of interest or concern in a particular topic. The system according to feature 1.

6. The aforementioned feedback unit is Visually display changes in user preferences. The system according to feature 1.

7. 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.

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

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