system

Abstract

The system according to the embodiment aims to speak to the user at an appropriate timing. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and an interlocutor unit. The collection unit collects behavioral data or environmental data of a user. The analysis unit analyzes the data collected by the collection unit. The interlocutor unit interlocuts the user at an appropriate time based on the results of the analysis by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, conversational AI is triggered by a question from the user, which means that the user has to ask a question every time.

[0005] The system according to the embodiment aims to speak to the user at an appropriate timing. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an interlocutor. The collection unit collects behavioral data or environmental data of a user. The analysis unit analyzes the data collected by the collection unit. The interlocutor interlocutors interlocutors interlocutor the user at an appropriate timing based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can speak to the user at an appropriate timing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The active assistant function according to an embodiment of the present invention is an enhanced generative AI system that understands the situation and can speak to the user at the optimal timing. This system collects user behavioral and environmental data and, based on the analysis results, speaks to the user at the optimal timing, providing a more human-like AI experience. For example, the active assistant function collects the user's schedule, location information, surrounding audio data, biometric data, and other information, and analyzes them with AI. This analysis allows the AI ​​to understand the user's situation. Next, the AI ​​determines the optimal timing to speak to the user based on the collected data. For example, it may not speak to the user when the user is busy or concentrating, but may speak to the user when the user is relaxed or taking a break. In this way, the AI ​​can speak to the user at the optimal time for the user. Furthermore, the AI ​​also appropriately selects the content of the conversation. For example, if the user is working, it may provide work-related information, and if the user is relaxing, it may provide topics related to hobbies and interests. In this way, it can provide useful information to the user. This active assistant function allows the user to feel that the interaction with the AI ​​is more natural. Conventional conversational AI requires the user to ask a question before a dialogue can begin, resulting in a one-sided dialogue. However, with this invention, the AI ​​itself can speak, enabling a more two-way dialogue. For example, when a user wakes up in the morning, the AI ​​can naturally begin a dialogue with the AI ​​by saying, "Good morning. What are your plans for today?" Similarly, when a user is concentrating on work, the AI ​​can encourage the user to relax by saying, "Thank you for your hard work. Would you like to take a short break?" In this way, the active assistant function can provide a more human-like AI experience by understanding the user's situation and speaking at the optimal time. This is expected to promote the widespread adoption of AI and encourage more users to use AI. This allows the active assistant function to provide a more human-like AI experience by understanding the user's situation and speaking at the optimal time.

[0029] The active assistant system according to the embodiment includes a collection unit, an analysis unit, and a conversation unit. The collection unit collects user behavioral data or environmental data. Examples of the behavioral data include, but are not limited to, movement history, used applications, and operation logs. Examples of the environmental data include, but are not limited to, temperature, humidity, illuminance, and noise levels. The collection unit collects data from, for example, the user's smartphone or wearable device. The collection unit can also collect data from smart devices and sensors in the home. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a machine learning algorithm to understand the user's situation. The analysis unit can also analyze the user's behavioral patterns using data mining technology. For example, the analysis unit predicts the user's current situation based on the user's past behavioral data. The conversation unit talks to the user at an appropriate time based on the results of the analysis by the analysis unit. For example, the conversation unit talks to the user when the user is relaxed. The conversation unit can also avoid talking to the user when the user is busy. Furthermore, the conversation unit selects what to talk to the user. For example, if the user is at work, it can provide work-related information. Also, if the user is relaxing, it can provide topics related to the user's hobbies and interests. This allows the active assistant system according to the embodiment to collect user behavioral data and environmental data, and talk to the user at the optimal timing based on the analysis results, thereby providing a more human-like AI experience.

[0030] The collection unit can collect biometric data of the user. Examples of biometric data include, but are not limited to, heart rate, blood pressure, body temperature, and brain waves. The collection unit can collect biometric data from, for example, the user's smartwatch or fitness tracker. The collection unit can also collect biometric data from medical devices or sensors. For example, the collection unit can monitor heart rate in real time and collect data. The collection unit can also periodically measure blood pressure and collect data. The collection unit can also measure body temperature and collect data. By collecting the user's biometric data, more detailed understanding of the situation becomes possible. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input heart rate data acquired from the smartwatch to the generation AI and have the generation AI analyze the data.

[0031] The collection unit can collect the user's past dialogue history. Examples of past dialogue history include, but are not limited to, text chat, voice dialogue, and video calls. The collection unit can collect the dialogue history from, for example, the user's smartphone or computer. The collection unit can also collect the dialogue history from a cloud service or a messaging app. For example, the collection unit can collect text chat history and store it in a database. The collection unit can also collect recorded voice dialogue data and store it for analysis. Furthermore, the collection unit can collect recorded video call data and store it for analysis. By collecting the user's past dialogue history, more personalized dialogue becomes possible. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input text chat history data into a generation AI and have the generation AI analyze the data.

[0032] The analysis unit can analyze the user's situation based on the collected data. The user's situation includes, but is not limited to, current activities, emotional state, and health status. The analysis unit can analyze the data using, for example, a machine learning algorithm to understand the user's situation. The analysis unit can also analyze the user's behavioral patterns using data mining technology. For example, the analysis unit can predict the user's current situation based on the user's past behavioral data. The analysis unit can also analyze the user's emotional state and take appropriate action. Furthermore, the analysis unit can analyze the user's health status and provide health management advice. By analyzing the user's situation based on the collected data, it is possible to speak to the user at a more appropriate time. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0033] The conversation unit can select what to talk to the user based on the analysis results. The conversation content can include, but is not limited to, reminders, advice, and information. For example, if the user is working, the conversation unit can provide work-related information. Also, if the user is relaxing, the conversation unit can provide topics related to hobbies and interests. Furthermore, the conversation unit can provide health management advice based on the user's health condition. For example, the conversation unit can provide reminders based on the user's schedule. Also, the conversation unit can provide related information based on the user's interests. Thus, by selecting what to talk to based on the analysis results, useful information can be provided to the user. Some or all of the above-described processing in the conversation unit can be performed using, for example, AI, or without AI. For example, the conversation unit can input the analysis results to a generation AI and have the generation AI select what to talk to.

[0034] The system includes a privacy protection unit that protects the privacy of collected data. The privacy protection unit protects the privacy of the collected data. Examples of privacy protection include, but are not limited to, data encryption, access control, and anonymization technology. For example, the privacy protection unit encrypts and stores collected data. The privacy protection unit can also control access to the data so that only authorized users can access the data. The privacy protection unit can also anonymize the data to prevent individuals from being identified. For example, the privacy protection unit encrypts and stores collected biometric data. The privacy protection unit can also anonymize and store collected location information. This protects the privacy of the collected data, thereby ensuring the privacy of the user. Some or all of the above-described processing in the privacy protection unit may be performed using, or without, AI. For example, the privacy protection unit may input collected data to a generation AI and have the generation AI encrypt and anonymize the data.

[0035] The collection unit can analyze the user's behavioral patterns and automatically start data collection when a specific behavior occurs. For example, if the user wakes up at a specific time every morning, the collection unit collects biometric data at that time. The collection unit can also collect location information and surrounding audio data when the user arrives at a specific location. The collection unit can also collect heart rate and calorie consumption when the user starts exercising. For example, if the user wakes up at a specific time every morning, the collection unit collects heart rate and body temperature at that time and accumulates the data. The collection unit can also collect location information and analyze surrounding audio data when the user arrives at a specific location. The collection unit can also monitor heart rate and calorie consumption in real time and collect data when the user starts exercising. This enables efficient data collection by analyzing the user's behavioral patterns and automatically starting data collection when a specific behavior occurs. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's behavioral pattern data into a generation AI and cause the generation AI to start data collection.

[0036] The collection unit can link multiple sensor devices to improve the accuracy of the collected data. For example, the collection unit can link a smartwatch and a smartphone to simultaneously collect heart rate and location information. The collection unit can also link a home smart speaker and a camera to collect audio data and video data. The collection unit can also link an in-car device and a smartphone to collect driving behavior data and location information. For example, the collection unit can link a smartwatch and a smartphone to simultaneously collect heart rate and location information to improve the accuracy of the data. The collection unit can also link a home smart speaker and a camera to collect audio data and video data to improve the accuracy of the data. The collection unit can also link an in-car device and a smartphone to collect driving behavior data and location information to improve the accuracy of the data. In this way, linking multiple sensor devices improves the accuracy of the collected data. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from multiple sensor devices into the generation AI and have the generation AI improve the accuracy of the data.

[0037] The collection unit can prioritize data collection at specific locations based on the user's geographical location information. For example, when the user is at home, the collection unit prioritizes collecting environmental data within the home. Furthermore, when the user is at work, the collection unit can prioritize collecting work-related behavioral data. Furthermore, when the user is out, the collection unit can prioritize collecting location information and surrounding audio data. For example, when the user is at home, the collection unit prioritizes collecting environmental data such as temperature, humidity, and illuminance within the home and storing the data. Furthermore, when the user is at work, the collection unit can prioritize collecting work-related behavioral data and analyze the data. Furthermore, when the user is out, the collection unit prioritizes collecting location information and surrounding audio data to grasp changes in the environment. Thus, data collection at specific locations is prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's geographical location information to a generation AI and cause the generation AI to determine the priority of data collection.

[0038] The collection unit can analyze a user's social media activity and collect related data. For example, if a user posts on social media that they will attend a specific event, the collection unit can collect data related to the event. Also, if a user posts on social media that they are at a specific location, the collection unit can collect data related to the location. Furthermore, if a user expresses a specific emotion on social media, the collection unit can collect data related to the emotion. For example, if a user posts on social media that they will attend a specific event, the collection unit can collect data related to the event and accumulate the data. Also, if a user posts on social media that they are at a specific location, the collection unit can collect data related to the location and analyze the data. Furthermore, if a user expresses a specific emotion on social media, the collection unit can collect data related to the emotion and analyze the data. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed, for example, using AI, or without AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect data.

[0039] During analysis, the analysis unit can compare past data with current data to detect changes in the user's behavioral patterns. For example, the analysis unit can compare the user's past sleep data with current sleep data to detect changes in sleep patterns. The analysis unit can also compare the user's past exercise data with current exercise data to detect changes in exercise habits. Furthermore, the analysis unit can compare the user's past dietary data with current dietary data to detect changes in dietary habits. For example, the analysis unit can compare the user's past sleep data with current sleep data to detect changes in sleep patterns and provide appropriate advice. The analysis unit can also compare the user's past exercise data with current exercise data to detect changes in exercise habits and provide appropriate advice. Furthermore, the analysis unit can compare the user's past dietary data with current dietary data to detect changes in dietary habits and provide appropriate advice. In this way, by comparing past data with current data, changes in the user's behavioral patterns can be detected. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input past data and current data into the generation AI and have the generation AI detect changes in behavioral patterns.

[0040] The analysis unit can integrate information from different data sources during analysis to perform more accurate analysis. For example, the analysis unit can integrate the user's biometric data and behavioral data to analyze the user's health condition. The analysis unit can also integrate the user's location information and surrounding audio data to analyze environmental changes. The analysis unit can also integrate the user's past dialogue history and current behavioral data to analyze behavioral patterns. For example, the analysis unit can integrate the user's biometric data and behavioral data to analyze the user's health condition and provide appropriate advice. The analysis unit can also integrate the user's location information and surrounding audio data to analyze environmental changes and provide appropriate advice. The analysis unit can also integrate the user's past dialogue history and current behavioral data to analyze the user's behavioral patterns and provide appropriate advice. This enables more accurate analysis by integrating information from different data sources. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input information from different data sources into a generation AI and have the generation AI perform data integration and analysis.

[0041] During analysis, the analysis unit can select the optimal analysis method by taking into account the user's device information. For example, if the user is using a smartphone, the analysis unit can select an analysis method optimized for the smartphone. Furthermore, if the user is using a tablet, the analysis unit can also select an analysis method optimized for the tablet. Furthermore, if the user is using a smartwatch, the analysis unit can also select an analysis method optimized for the smartwatch. For example, if the user is using a smartphone, the analysis unit can select an analysis method optimized for the smartphone and analyze the data. Furthermore, if the user is using a tablet, the analysis unit can select an analysis method optimized for the tablet and analyze the data. Furthermore, if the user is using a smartwatch, the analysis unit can also select an analysis method optimized for the smartwatch and analyze the data. This allows the optimal analysis method to be selected by taking into account the user's device information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's device information into the generation AI and cause the generation AI to select the optimal analysis method.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past dialogue history. The analysis unit, for example, extracts frequently used keywords from the user's past dialogue history and reflects them in the analysis. The analysis unit can also detect specific patterns from the user's past dialogue history and reflect them in the analysis. The analysis unit can also detect changes in emotions from the user's past dialogue history and reflect them in the analysis. For example, the analysis unit extracts frequently used keywords from the user's past dialogue history and reflects them in the analysis to improve the accuracy of the analysis. The analysis unit can also detect specific patterns from the user's past dialogue history and reflect them in the analysis to improve the accuracy of the analysis. The analysis unit can also detect changes in emotions from the user's past dialogue history and reflect them in the analysis to improve the accuracy of the analysis. In this way, by referring to the user's past dialogue history, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI. For example, the analysis unit can input the user's past dialogue history into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] The conversation unit can optimize the timing of conversation based on the user's schedule and current activity status. For example, if the user is in a meeting, the conversation unit can talk to the user after the meeting ends. Also, if the user is exercising, the conversation unit can talk to the user after the exercise ends. Furthermore, if the user is taking a break, the conversation unit can talk to the user during the break. For example, if the user is in a meeting, the conversation unit can talk to the user after the meeting ends so as not to disturb the user's concentration. Also, if the user is exercising, the conversation unit can talk to the user after the exercise ends so as not to interrupt the user's exercise. Furthermore, if the user is taking a break, the conversation unit can talk to the user during the break to encourage the user to relax. This allows conversation to be performed at the optimal timing based on the user's schedule and current activity status. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's schedule data into the generation AI and cause the generation AI to optimize the timing of conversation.

[0044] The conversation unit can customize the conversation content based on the user's past dialogue history and interests. For example, the conversation unit may speak based on topics the user has shown interest in in the past. The conversation unit may also speak based on frequently discussed themes from the user's past dialogue history. The conversation unit may also speak based on topics containing specific keywords from the user's past dialogue history. For example, the conversation unit may speak based on topics the user has shown interest in in the past to attract the user's interest. The conversation unit may also speak based on frequently discussed themes from the user's past dialogue history to deepen the conversation with the user. The conversation unit may also speak based on topics containing specific keywords from the user's past dialogue history to attract the user's attention. This allows the conversation to be customized based on the user's past dialogue history and interests. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit may input the user's past dialogue history into a generation AI and cause the generation AI to customize the conversation content.

[0045] The conversation unit can localize the conversation content based on the user's geographical location information. For example, if the user is in a specific area, the conversation unit can provide topics related to the area. Furthermore, if the user is traveling, the conversation unit can provide tourist information about the travel destination. Furthermore, if the user is at home, the conversation unit can provide topics related to home activities. For example, if the user is in a specific area, the conversation unit can provide topics related to the area to attract the user's attention. Furthermore, if the user is traveling, the conversation unit can provide tourist information about the travel destination to support the user's travel. Furthermore, if the user is at home, the conversation unit can provide topics related to home activities to enrich the user's life. This allows the conversation to be localized based on the user's geographical location information. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's geographical location information to the generation AI and cause the generation AI to localize the conversation content.

[0046] The conversation unit can personalize the conversation content based on the user's social media activity. For example, the conversation unit can speak based on topics the user has shown interest in on social media. The conversation unit can also provide topics related to events the user has participated in on social media. The conversation unit can also provide topics related to accounts the user follows on social media. For example, the conversation unit can speak based on topics the user has shown interest in on social media to attract the user's interest. The conversation unit can also provide topics related to events the user has participated in on social media to attract the user's interest. The conversation unit can also provide topics related to accounts the user follows on social media to attract the user's interest. This allows the conversation to be personalized based on the user's social media activity. Some or all of the above-described processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's social media activity data into the generation AI and cause the generation AI to personalize the conversation content.

[0047] The privacy protection unit can encrypt the collected data to ensure data security. The privacy protection unit, for example, encrypts and stores the user's biometric data. The privacy protection unit can also encrypt and store the user's location information. The privacy protection unit can also encrypt and store the user's interaction history. For example, the privacy protection unit can encrypt and store the user's biometric data to ensure data security. The privacy protection unit can also encrypt and store the user's location information to ensure data security. The privacy protection unit can also encrypt and store the user's interaction history to ensure data security. In this way, by encrypting the collected data, data security can be ensured. Some or all of the above-described processing in the privacy protection unit may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection unit can input the collected data to a generation AI and have the generation AI encrypt the data.

[0048] The privacy protection unit may provide an interface that allows a user to control data collection and analysis. For example, the privacy protection unit may provide an interface that allows a user to select the type of data to collect. The privacy protection unit may also provide an interface that allows a user to select a data analysis method. The privacy protection unit may also provide an interface that allows a user to set a data storage period. For example, the privacy protection unit may provide an interface that allows a user to select the type of data to collect, allowing the user to collect only the data they need. The privacy protection unit may also provide an interface that allows a user to select a data analysis method, allowing the user to select their desired analysis method. The privacy protection unit may also provide an interface that allows a user to set a data storage period, allowing the user to freely set the data storage period. In this way, by providing an interface that allows a user to control data collection and analysis, the user's privacy can be protected. Some or all of the above-described processing in the privacy protection unit may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection unit may input the user's setting data into the generation AI and cause the generation AI to provide the interface.

[0049] The privacy protection unit may employ data anonymization technology to protect privacy. For example, the privacy protection unit may anonymize and store a user's biometric data. The privacy protection unit may also anonymize and store a user's location information. The privacy protection unit may also anonymize and store a user's interaction history. For example, the privacy protection unit may anonymize and store a user's biometric data to ensure data security. The privacy protection unit may also anonymize and store a user's location information to ensure data security. The privacy protection unit may also anonymize and store a user's interaction history to ensure data security. In this way, by employing data anonymization technology, user privacy can be protected. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input collected data to a generation AI and have the generation AI perform data anonymization.

[0050] The privacy protection unit may allow a user to select the scope of data collection for privacy protection. For example, the privacy protection unit may allow a user to select the type of data to collect. The privacy protection unit may also allow a user to select the frequency of data collection. The privacy protection unit may also allow a user to select the period of data collection. For example, the privacy protection unit may allow a user to select the type of data to collect, allowing the user to collect only the data they need. The privacy protection unit may also allow a user to select the frequency of data collection, allowing the user to collect data at the frequency they desire. The privacy protection unit may also allow a user to select the period of data collection, allowing the user to freely set the period of data collection. This allows the user to select the scope of data collection, thereby protecting the user's privacy. Some or all of the above-described processing in the privacy protection unit may be performed, for example, using AI or without AI. For example, the privacy protection unit may input user setting data into the generation AI and cause the generation AI to select the scope of data collection.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The analysis unit can learn and predict the user's habits based on the user's behavioral data. For example, the analysis unit can learn that the user wakes up at a specific time every morning and provide an appropriate assistant function based on that time. The analysis unit can also learn that the user engages in a specific activity on a specific day of the week and provide information related to that activity. Furthermore, the analysis unit can learn that the user is in a specific location at a specific time of day and provide information related to that location. In this way, by learning and predicting the user's habits, a more personalized assistant function can be provided.

[0053] The analysis unit can analyze the user's health data and monitor the health condition. For example, the analysis unit can analyze the user's heart rate and blood pressure data and issue an alert if an abnormality is detected. The analysis unit can also analyze the user's sleep data and evaluate the quality of sleep. Furthermore, the analysis unit can analyze the user's exercise data and evaluate the user's exercise habits. In this way, by monitoring the user's health condition, it is possible to provide advice for health management.

[0054] The analysis unit can predict the user's behavioral patterns based on the user's behavioral data and suggest future actions. For example, the analysis unit can learn that the user exercises on a specific day of the week and suggest exercises for that day. The analysis unit can also learn that the user relaxes during a specific time of day and suggest relaxation methods for that time of day. Furthermore, the analysis unit can learn that the user performs a specific activity in a specific location and suggest activities for that location. This makes it possible to provide a more personalized assistant function by predicting the user's behavioral patterns and suggesting future actions.

[0055] The analysis unit can analyze the user's behavioral patterns based on the user's behavioral data and make suggestions for behavioral improvement. For example, the analysis unit can analyze the user's exercise data and make suggestions for improving exercise habits. The analysis unit can also analyze the user's dietary data and make suggestions for improving dietary habits. Furthermore, the analysis unit can analyze the user's sleep data and make suggestions for improving sleep quality. In this way, by analyzing the user's behavioral patterns and making suggestions for behavioral improvement, the user's quality of life can be improved.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects user behavioral data or environmental data. Behavioral data includes, for example, movement history, application usage, and operation logs. Environmental data includes, for example, temperature, humidity, illuminance, and noise level. The collection unit collects data from the user's smartphone, wearable device, and smart devices and sensors in the home. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses machine learning algorithms and data mining techniques to analyze the data and understand the user's situation. For example, it predicts the user's current situation based on the user's past behavioral data. Step 3: The conversation unit speaks to the user at an appropriate time based on the results of the analysis by the analysis unit. For example, it will speak to the user when they are relaxed and not speak to them when they are busy. It also selects what to talk to the user about, providing work-related information when the user is working and topics related to hobbies and interests when the user is relaxing.

[0058] (Example 2) The active assistant function according to an embodiment of the present invention is an enhanced generative AI system that understands the situation and can speak to the user at the optimal timing. This system collects user behavioral and environmental data and, based on the analysis results, speaks to the user at the optimal timing, providing a more human-like AI experience. For example, the active assistant function collects the user's schedule, location information, surrounding audio data, biometric data, and other information, and analyzes them with AI. This analysis allows the AI ​​to understand the user's situation. Next, the AI ​​determines the optimal timing to speak to the user based on the collected data. For example, it may not speak to the user when the user is busy or concentrating, but may speak to the user when the user is relaxed or taking a break. In this way, the AI ​​can speak to the user at the optimal time for the user. Furthermore, the AI ​​also appropriately selects the content of the conversation. For example, if the user is working, it may provide work-related information, and if the user is relaxing, it may provide topics related to hobbies and interests. In this way, it can provide useful information to the user. This active assistant function allows the user to feel that the interaction with the AI ​​is more natural. Conventional conversational AI requires the user to ask a question before a dialogue can begin, resulting in a one-sided dialogue. However, with this invention, the AI ​​itself can speak, enabling a more two-way dialogue. For example, when a user wakes up in the morning, the AI ​​can naturally begin a dialogue with the AI ​​by saying, "Good morning. What are your plans for today?" Similarly, when a user is concentrating on work, the AI ​​can encourage the user to relax by saying, "Thank you for your hard work. Would you like to take a short break?" In this way, the active assistant function can provide a more human-like AI experience by understanding the user's situation and speaking at the optimal time. This is expected to promote the widespread adoption of AI and encourage more users to use AI. This allows the active assistant function to provide a more human-like AI experience by understanding the user's situation and speaking at the optimal time.

[0059] The active assistant system according to the embodiment includes a collection unit, an analysis unit, and a conversation unit. The collection unit collects user behavioral data or environmental data. Examples of the behavioral data include, but are not limited to, movement history, used applications, and operation logs. Examples of the environmental data include, but are not limited to, temperature, humidity, illuminance, and noise levels. The collection unit collects data from, for example, the user's smartphone or wearable device. The collection unit can also collect data from smart devices and sensors in the home. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a machine learning algorithm to understand the user's situation. The analysis unit can also analyze the user's behavioral patterns using data mining technology. For example, the analysis unit predicts the user's current situation based on the user's past behavioral data. The conversation unit talks to the user at an appropriate time based on the results of the analysis by the analysis unit. For example, the conversation unit talks to the user when the user is relaxed. The conversation unit can also avoid talking to the user when the user is busy. Furthermore, the conversation unit selects what to talk to the user. For example, if the user is at work, it can provide work-related information. Also, if the user is relaxing, it can provide topics related to the user's hobbies and interests. This allows the active assistant system according to the embodiment to collect user behavioral data and environmental data, and talk to the user at the optimal timing based on the analysis results, thereby providing a more human-like AI experience.

[0060] The collection unit can collect biometric data of the user. Examples of biometric data include, but are not limited to, heart rate, blood pressure, body temperature, and brain waves. The collection unit can collect biometric data from, for example, the user's smartwatch or fitness tracker. The collection unit can also collect biometric data from medical devices or sensors. For example, the collection unit can monitor heart rate in real time and collect data. The collection unit can also periodically measure blood pressure and collect data. The collection unit can also measure body temperature and collect data. By collecting the user's biometric data, more detailed understanding of the situation becomes possible. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input heart rate data acquired from the smartwatch to the generation AI and have the generation AI analyze the data.

[0061] The collection unit can collect the user's past dialogue history. Examples of past dialogue history include, but are not limited to, text chat, voice dialogue, and video calls. The collection unit can collect the dialogue history from, for example, the user's smartphone or computer. The collection unit can also collect the dialogue history from a cloud service or a messaging app. For example, the collection unit can collect text chat history and store it in a database. The collection unit can also collect recorded voice dialogue data and store it for analysis. Furthermore, the collection unit can collect recorded video call data and store it for analysis. By collecting the user's past dialogue history, more personalized dialogue becomes possible. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input text chat history data into a generation AI and have the generation AI analyze the data.

[0062] The analysis unit can analyze the user's situation based on the collected data. The user's situation includes, but is not limited to, current activities, emotional state, and health status. The analysis unit can analyze the data using, for example, a machine learning algorithm to understand the user's situation. The analysis unit can also analyze the user's behavioral patterns using data mining technology. For example, the analysis unit can predict the user's current situation based on the user's past behavioral data. The analysis unit can also analyze the user's emotional state and take appropriate action. Furthermore, the analysis unit can analyze the user's health status and provide health management advice. By analyzing the user's situation based on the collected data, it is possible to speak to the user at a more appropriate time. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0063] The conversation unit can select what to talk to the user based on the analysis results. The conversation content can include, but is not limited to, reminders, advice, and information. For example, if the user is working, the conversation unit can provide work-related information. Also, if the user is relaxing, the conversation unit can provide topics related to hobbies and interests. Furthermore, the conversation unit can provide health management advice based on the user's health condition. For example, the conversation unit can provide reminders based on the user's schedule. Also, the conversation unit can provide related information based on the user's interests. Thus, by selecting what to talk to based on the analysis results, useful information can be provided to the user. Some or all of the above-described processing in the conversation unit can be performed using, for example, AI, or without AI. For example, the conversation unit can input the analysis results to a generation AI and have the generation AI select what to talk to.

[0064] The system includes a privacy protection unit that protects the privacy of collected data. The privacy protection unit protects the privacy of the collected data. Examples of privacy protection include, but are not limited to, data encryption, access control, and anonymization technology. For example, the privacy protection unit encrypts and stores collected data. The privacy protection unit can also control access to the data so that only authorized users can access the data. The privacy protection unit can also anonymize the data to prevent individuals from being identified. For example, the privacy protection unit encrypts and stores collected biometric data. The privacy protection unit can also anonymize and store collected location information. This protects the privacy of the collected data, thereby ensuring the privacy of the user. Some or all of the above-described processing in the privacy protection unit may be performed using, or without, AI. For example, the privacy protection unit may input collected data to a generation AI and have the generation AI encrypt and anonymize the data.

[0065] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collection of biometric data such as heart rate and respiratory rate. Furthermore, when the user is relaxed, the collection unit can also collect surrounding audio data and environmental data. Furthermore, when the user is excited, the collection unit can also collect behavioral data and location information. For example, when the user is stressed, the collection unit monitors heart rate fluctuations in real time and collects data. Furthermore, when the user is relaxed, the collection unit can collect surrounding audio data and grasp changes in the environment. Furthermore, when the user is excited, the collection unit can collect behavioral data and analyze the user's behavioral patterns. This allows for more appropriate data collection by adjusting the type of data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to adjust the type of data collection.

[0066] The collection unit can analyze a user's behavioral patterns and automatically start data collection when a specific behavior occurs. For example, if the user wakes up at a specific time every morning, the collection unit collects biometric data at that time. The collection unit can also collect location information and surrounding audio data when the user arrives at a specific location. The collection unit can also collect heart rate and calorie consumption when the user starts exercising. For example, if the user wakes up at a specific time every morning, the collection unit collects heart rate and body temperature at that time and accumulates the data. The collection unit can also collect location information and analyze surrounding audio data when the user arrives at a specific location. The collection unit can also monitor heart rate and calorie consumption in real time and collect data when the user starts exercising. This enables efficient data collection by analyzing the user's behavioral patterns and automatically starting data collection when a specific behavior occurs. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's behavioral pattern data into a generation AI and cause the generation AI to start data collection.

[0067] The collection unit can link multiple sensor devices to improve the accuracy of the collected data. For example, the collection unit can link a smartwatch and a smartphone to simultaneously collect heart rate and location information. The collection unit can also link a home smart speaker and a camera to collect audio data and video data. The collection unit can also link an in-car device and a smartphone to collect driving behavior data and location information. For example, the collection unit can link a smartwatch and a smartphone to simultaneously collect heart rate and location information to improve the accuracy of the data. The collection unit can also link a home smart speaker and a camera to collect audio data and video data to improve the accuracy of the data. The collection unit can also link an in-car device and a smartphone to collect driving behavior data and location information to improve the accuracy of the data. In this way, linking multiple sensor devices improves the accuracy of the collected data. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from multiple sensor devices into the generation AI and have the generation AI improve the accuracy of the data.

[0068] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is stressed, the collection unit prioritizes collecting heart rate and respiratory rate. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting surrounding audio data. Furthermore, if the user is excited, the collection unit can also prioritize collecting behavioral data. For example, if the user is stressed, the collection unit monitors heart rate fluctuations in real time and prioritizes collecting data. Furthermore, if the user is relaxed, the collection unit can prioritize collecting surrounding audio data to understand changes in the environment. Furthermore, if the user is excited, the collection unit can prioritize collecting behavioral data and analyze the user's behavioral patterns. Thus, by prioritizing the data to be collected based on the user's emotions, more important data can be prioritized for collection. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0069] The collection unit can prioritize data collection at specific locations based on the user's geographical location information. For example, when the user is at home, the collection unit prioritizes collecting environmental data within the home. Furthermore, when the user is at work, the collection unit can prioritize collecting work-related behavioral data. Furthermore, when the user is out, the collection unit can prioritize collecting location information and surrounding audio data. For example, when the user is at home, the collection unit prioritizes collecting environmental data such as temperature, humidity, and illuminance within the home and storing the data. Furthermore, when the user is at work, the collection unit can prioritize collecting work-related behavioral data and analyze the data. Furthermore, when the user is out, the collection unit prioritizes collecting location information and surrounding audio data to grasp changes in the environment. Thus, data collection at specific locations is prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority of data collection.

[0070] The collection unit can analyze a user's social media activity and collect related data. For example, if a user posts on social media that they will attend a specific event, the collection unit can collect data related to the event. Also, if a user posts on social media that they are at a specific location, the collection unit can collect data related to the location. Furthermore, if a user expresses a specific emotion on social media, the collection unit can collect data related to the emotion. For example, if a user posts on social media that they will attend a specific event, the collection unit can collect data related to the event and accumulate the data. Also, if a user posts on social media that they are at a specific location, the collection unit can collect data related to the location and analyze the data. Furthermore, if a user expresses a specific emotion on social media, the collection unit can collect data related to the emotion and analyze the data. In this way, related data can be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed, for example, using AI, or without AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect data.

[0071] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing data related to stress reduction. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing data for maintaining a relaxed state. Furthermore, if the user is excited, the analysis unit can prioritize analyzing data for managing an excited state. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing data related to stress reduction and provide appropriate advice. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing data for maintaining a relaxed state and provide appropriate advice. Furthermore, if the user is excited, the analysis unit can prioritize analyzing data for managing an excited state and provide appropriate advice. This enables more appropriate analysis by adjusting the analysis algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0072] During analysis, the analysis unit can compare past data with current data to detect changes in the user's behavioral patterns. For example, the analysis unit can compare the user's past sleep data with current sleep data to detect changes in sleep patterns. The analysis unit can also compare the user's past exercise data with current exercise data to detect changes in exercise habits. Furthermore, the analysis unit can compare the user's past dietary data with current dietary data to detect changes in dietary habits. For example, the analysis unit can compare the user's past sleep data with current sleep data to detect changes in sleep patterns and provide appropriate advice. The analysis unit can also compare the user's past exercise data with current exercise data to detect changes in exercise habits and provide appropriate advice. Furthermore, the analysis unit can compare the user's past dietary data with current dietary data to detect changes in dietary habits and provide appropriate advice. In this way, by comparing past data with current data, changes in the user's behavioral patterns can be detected. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input past data and current data into the generation AI and have the generation AI detect changes in behavioral patterns.

[0073] The analysis unit can integrate information from different data sources during analysis to perform more accurate analysis. For example, the analysis unit can integrate the user's biometric data and behavioral data to analyze the user's health condition. The analysis unit can also integrate the user's location information and surrounding audio data to analyze environmental changes. The analysis unit can also integrate the user's past dialogue history and current behavioral data to analyze behavioral patterns. For example, the analysis unit can integrate the user's biometric data and behavioral data to analyze the user's health condition and provide appropriate advice. The analysis unit can also integrate the user's location information and surrounding audio data to analyze environmental changes and provide appropriate advice. The analysis unit can also integrate the user's past dialogue history and current behavioral data to analyze the user's behavioral patterns and provide appropriate advice. This enables more accurate analysis by integrating information from different data sources. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input information from different data sources into a generation AI and have the generation AI perform data integration and analysis.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method, allowing the user to quickly understand the information. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information, allowing the user to deeply understand the information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points, allowing the user to quickly obtain the necessary information. This allows the display method of the analysis results to be adjusted based on the user's emotions, enabling a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0075] During analysis, the analysis unit can select the optimal analysis method by taking into account the user's device information. For example, if the user is using a smartphone, the analysis unit can select an analysis method optimized for the smartphone. Furthermore, if the user is using a tablet, the analysis unit can also select an analysis method optimized for the tablet. Furthermore, if the user is using a smartwatch, the analysis unit can also select an analysis method optimized for the smartwatch. For example, if the user is using a smartphone, the analysis unit can select an analysis method optimized for the smartphone and analyze the data. Furthermore, if the user is using a tablet, the analysis unit can select an analysis method optimized for the tablet and analyze the data. Furthermore, if the user is using a smartwatch, the analysis unit can also select an analysis method optimized for the smartwatch and analyze the data. This allows the optimal analysis method to be selected by taking into account the user's device information. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's device information into the generation AI and cause the generation AI to select the optimal analysis method.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past dialogue history. The analysis unit, for example, extracts frequently used keywords from the user's past dialogue history and reflects them in the analysis. The analysis unit can also detect specific patterns from the user's past dialogue history and reflect them in the analysis. The analysis unit can also detect changes in emotions from the user's past dialogue history and reflect them in the analysis. For example, the analysis unit extracts frequently used keywords from the user's past dialogue history and reflects them in the analysis to improve the accuracy of the analysis. The analysis unit can also detect specific patterns from the user's past dialogue history and reflect them in the analysis to improve the accuracy of the analysis. The analysis unit can also detect changes in emotions from the user's past dialogue history and reflect them in the analysis to improve the accuracy of the analysis. In this way, by referring to the user's past dialogue history, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI. For example, the analysis unit can input the user's past dialogue history into the generation AI and have the generation AI improve the accuracy of the analysis.

[0077] The conversation unit can estimate the user's emotions and adjust the conversation content based on the estimated user's emotions. For example, if the user is feeling stressed, the conversation unit can provide a topic that will help the user relax. Furthermore, if the user is relaxed, the conversation unit can also provide an interesting topic. Furthermore, if the user is excited, the conversation unit can also provide a topic that will calm the user. For example, if the user is feeling stressed, the conversation unit can provide a topic that will help the user relax, thereby reducing the user's stress. Furthermore, if the user is relaxed, the conversation unit can provide an interesting topic that will attract the user's interest. Furthermore, if the user is excited, the conversation unit can provide a topic that will calm the user, thereby reducing the user's excitement. This allows for more appropriate conversation by adjusting the conversation content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or without AI. For example, the speaking unit can input the user's emotional data into the generation AI and have the generation AI adjust the content of what is spoken.

[0078] The conversation unit can optimize the timing of conversation based on the user's schedule and current activity status. For example, if the user is in a meeting, the conversation unit can talk to the user after the meeting ends. Also, if the user is exercising, the conversation unit can talk to the user after the exercise ends. Furthermore, if the user is taking a break, the conversation unit can talk to the user during the break. For example, if the user is in a meeting, the conversation unit can talk to the user after the meeting ends so as not to disturb the user's concentration. Also, if the user is exercising, the conversation unit can talk to the user after the exercise ends so as not to interrupt the user's exercise. Furthermore, if the user is taking a break, the conversation unit can talk to the user during the break to encourage the user to relax. This allows conversation to be performed at the optimal timing based on the user's schedule and current activity status. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's schedule data into the generation AI and cause the generation AI to optimize the timing of conversation.

[0079] The conversation unit can customize the conversation content based on the user's past dialogue history and interests. For example, the conversation unit may speak based on topics the user has shown interest in in the past. The conversation unit may also speak based on frequently discussed themes from the user's past dialogue history. The conversation unit may also speak based on topics containing specific keywords from the user's past dialogue history. For example, the conversation unit may speak based on topics the user has shown interest in in the past to attract the user's interest. The conversation unit may also speak based on frequently discussed themes from the user's past dialogue history to deepen the conversation with the user. The conversation unit may also speak based on topics containing specific keywords from the user's past dialogue history to attract the user's attention. This allows the conversation to be customized based on the user's past dialogue history and interests. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit may input the user's past dialogue history into a generation AI and cause the generation AI to customize the conversation content.

[0080] The speaking unit can estimate the user's emotions and adjust the tone and language used in speaking based on the estimated user's emotions. For example, if the user is nervous, the speaking unit can speak in a calm tone. Furthermore, if the user is relaxed, the speaking unit can speak in a cheerful tone. Furthermore, if the user is in a hurry, the speaking unit can speak in a quick and concise language. For example, if the user is nervous, the speaking unit can speak in a calm tone to relieve the user's tension. Furthermore, if the user is relaxed, the speaking unit can speak in a cheerful tone to lift the user's mood. Furthermore, if the user is in a hurry, the speaking unit can speak in a quick and concise language to save the user's time. This enables more appropriate dialogue by adjusting the tone and language used in speaking based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the speaking unit may be performed using, for example, AI, or without AI. For example, the speaking unit can input the user's emotional data into the generation AI and have the generation AI adjust the tone and language used in speaking.

[0081] The conversation unit can localize the conversation content based on the user's geographical location information. For example, if the user is in a specific area, the conversation unit can provide topics related to the area. Furthermore, if the user is traveling, the conversation unit can provide tourist information about the travel destination. Furthermore, if the user is at home, the conversation unit can provide topics related to home activities. For example, if the user is in a specific area, the conversation unit can provide topics related to the area to attract the user's attention. Furthermore, if the user is traveling, the conversation unit can provide tourist information about the travel destination to support the user's travel. Furthermore, if the user is at home, the conversation unit can provide topics related to home activities to enrich the user's life. This allows the conversation to be localized based on the user's geographical location information. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's geographical location information to the generation AI and cause the generation AI to localize the conversation content.

[0082] The conversation unit can personalize the conversation content based on the user's social media activity. For example, the conversation unit can speak based on topics the user has shown interest in on social media. The conversation unit can also provide topics related to events the user has participated in on social media. The conversation unit can also provide topics related to accounts the user follows on social media. For example, the conversation unit can speak based on topics the user has shown interest in on social media to attract the user's interest. The conversation unit can also provide topics related to events the user has participated in on social media to attract the user's interest. The conversation unit can also provide topics related to accounts the user follows on social media to attract the user's interest. This allows the conversation to be personalized based on the user's social media activity. Some or all of the above-described processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's social media activity data into the generation AI and cause the generation AI to personalize the conversation content.

[0083] The privacy protection unit can estimate the user's emotions and adjust the level of privacy protection based on the estimated user's emotions. For example, the privacy protection unit can limit the range of data collection when the user is stressed. The privacy protection unit can also expand the range of data collection when the user is relaxed. Furthermore, the privacy protection unit can adjust the range of data collection when the user is excited. For example, the privacy protection unit can limit the range of data collection when the user is stressed, thereby protecting the user's privacy. Furthermore, the privacy protection unit can expand the range of data collection and collect more detailed data when the user is relaxed. Furthermore, the privacy protection unit can adjust the range of data collection when the user is excited, thereby protecting the user's privacy. This enables more appropriate privacy protection by adjusting the level of privacy protection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the privacy protection unit can be performed using, for example, AI, or without AI. For example, the privacy protection unit can input the user's emotional data into the generation AI and have the generation AI adjust the level of privacy protection.

[0084] The privacy protection unit can encrypt the collected data to ensure data security. The privacy protection unit, for example, encrypts and stores the user's biometric data. The privacy protection unit can also encrypt and store the user's location information. The privacy protection unit can also encrypt and store the user's interaction history. For example, the privacy protection unit can encrypt and store the user's biometric data to ensure data security. The privacy protection unit can also encrypt and store the user's location information to ensure data security. The privacy protection unit can also encrypt and store the user's interaction history to ensure data security. In this way, by encrypting the collected data, data security can be ensured. Some or all of the above-described processing in the privacy protection unit may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection unit can input the collected data to a generation AI and have the generation AI encrypt the data.

[0085] The privacy protection unit may provide an interface that allows a user to control data collection and analysis. For example, the privacy protection unit may provide an interface that allows a user to select the type of data to collect. The privacy protection unit may also provide an interface that allows a user to select a data analysis method. The privacy protection unit may also provide an interface that allows a user to set a data storage period. For example, the privacy protection unit may provide an interface that allows a user to select the type of data to collect, allowing the user to collect only the data they need. The privacy protection unit may also provide an interface that allows a user to select a data analysis method, allowing the user to select their desired analysis method. The privacy protection unit may also provide an interface that allows a user to set a data storage period, allowing the user to freely set the data storage period. In this way, by providing an interface that allows a user to control data collection and analysis, the user's privacy can be protected. Some or all of the above-described processing in the privacy protection unit may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection unit may input the user's setting data into the generation AI and cause the generation AI to provide the interface.

[0086] The privacy protection unit can estimate a user's emotions and adjust a privacy-protection notification method based on the estimated user emotions. For example, if the user is nervous, the privacy protection unit can provide a concise and easy-to-understand notification method. Furthermore, if the user is relaxed, the privacy protection unit can provide a detailed notification method. Furthermore, if the user is in a hurry, the privacy protection unit can provide a quick and concise notification method. For example, if the user is nervous, the privacy protection unit can provide a concise and easy-to-understand notification method, allowing the user to quickly understand the information. Furthermore, if the user is relaxed, the privacy protection unit can provide a detailed notification method, allowing the user to deeply understand the information. Furthermore, if the user is in a hurry, the privacy protection unit can provide a quick and concise notification method, allowing the user to quickly obtain the necessary information. This allows for more appropriate notifications by adjusting the privacy-protection notification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input user emotion data into the generation AI and have the generation AI adjust the notification method.

[0087] The privacy protection unit may employ data anonymization technology to protect privacy. For example, the privacy protection unit may anonymize and store a user's biometric data. The privacy protection unit may also anonymize and store a user's location information. The privacy protection unit may also anonymize and store a user's interaction history. For example, the privacy protection unit may anonymize and store a user's biometric data to ensure data security. The privacy protection unit may also anonymize and store a user's location information to ensure data security. The privacy protection unit may also anonymize and store a user's interaction history to ensure data security. In this way, by employing data anonymization technology, user privacy can be protected. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input collected data to a generation AI and have the generation AI perform data anonymization.

[0088] The privacy protection unit may allow a user to select the scope of data collection for privacy protection. For example, the privacy protection unit may allow a user to select the type of data to collect. The privacy protection unit may also allow a user to select the frequency of data collection. The privacy protection unit may also allow a user to select the period of data collection. For example, the privacy protection unit may allow a user to select the type of data to collect, allowing the user to collect only the data they need. The privacy protection unit may also allow a user to select the frequency of data collection, allowing the user to collect data at the frequency they desire. The privacy protection unit may also allow a user to select the period of data collection, allowing the user to freely set the period of data collection. This allows the user to select the scope of data collection, thereby protecting the user's privacy. Some or all of the above-described processing in the privacy protection unit may be performed, for example, using AI or without AI. For example, the privacy protection unit may input user setting data into the generation AI and cause the generation AI to select the scope of data collection. === Hard Collateral 1-1 === The above-described active assistant function is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user behavior data and environmental data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and grasps the user's situation. The speaking unit is realized by the control unit 46A of the smart device 14, and speaks to the user at an appropriate time based on the analysis results. === Hard Collateral 1-2 === The above-described active assistant function is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user behavior data and environmental data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and grasps the user's situation. The speaking unit is realized by the control unit 46A of the smart glasses 214, and speaks to the user at an appropriate time based on the analysis results. === Hard Collateral 1-3 === The above-described active assistant function is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects behavioral data and environmental data of the user using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to understand the user's situation. The speaking unit is realized by the control unit 46A of the headset terminal 314, and speaks to the user at appropriate times based on the analysis results. === Hard Collateral 1-4 === The above-described active assistant function is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects behavioral data and environmental data of the user using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and grasps the user's situation. The speaking unit is realized by the control unit 46A of the robot 414, and speaks to the user at an appropriate time based on the analysis results.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The analysis unit can learn and predict the user's habits based on the user's behavioral data. For example, the analysis unit can learn that the user wakes up at a specific time every morning and provide an appropriate assistant function based on that time. The analysis unit can also learn that the user engages in a specific activity on a specific day of the week and provide information related to that activity. Furthermore, the analysis unit can learn that the user is in a specific location at a specific time of day and provide information related to that location. In this way, by learning and predicting the user's habits, a more personalized assistant function can be provided.

[0091] The collection unit can estimate the user's emotions and monitor the user's stress level based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit monitors fluctuations in heart rate and breathing rate in real time and collects data. Furthermore, when the user is relaxed, the collection unit can collect surrounding audio data and environmental data and provide information for maintaining a relaxed state. Furthermore, when the user is excited, the collection unit can collect behavioral data and location information and provide information for managing the excited state. This allows for more appropriate data collection and information provision by monitoring the stress level based on the user's emotions.

[0092] The conversation unit can estimate the user's emotions and provide topics to improve the user's mood based on the estimated user's emotions. For example, if the user is feeling down, the conversation unit can provide encouraging words or positive topics. If the user is feeling relaxed, the conversation unit can also provide fun topics or interesting information. Furthermore, if the user is excited, the conversation unit can also provide topics to calm the user down. In this way, the user's mood can be improved by adjusting the content of conversation based on the user's emotions.

[0093] The analysis unit can analyze the user's health data and monitor the health condition. For example, the analysis unit can analyze the user's heart rate and blood pressure data and issue an alert if an abnormality is detected. The analysis unit can also analyze the user's sleep data and evaluate the quality of sleep. Furthermore, the analysis unit can analyze the user's exercise data and evaluate the user's exercise habits. In this way, by monitoring the user's health condition, it is possible to provide advice for health management.

[0094] The collection unit can estimate the user's emotion and record the user's emotional state based on the estimated user's emotion. For example, if the user is feeling stressed, the collection unit can record the emotional state and save it as data for later analysis. Furthermore, if the user is relaxed, the collection unit can record the emotional state and provide information for maintaining the relaxed state. Furthermore, if the user is excited, the collection unit can record the emotional state and provide information for managing the excited state. By recording the user's emotional state, changes in emotion can be tracked and appropriate responses can be taken.

[0095] The conversation unit can estimate the user's emotions and provide topics to stabilize the user's emotions based on the estimated user's emotions. For example, if the user is feeling anxious, the conversation unit can provide topics that give the user a sense of security. Also, if the user is feeling angry, the conversation unit can provide topics that will calm the user. Furthermore, if the user is feeling sad, the conversation unit can provide words of comfort or topics of encouragement. In this way, the user's emotions can be stabilized by adjusting the content of conversation based on the user's emotions.

[0096] The analysis unit can predict the user's behavioral patterns based on the user's behavioral data and suggest future actions. For example, the analysis unit can learn that the user exercises on a specific day of the week and suggest exercises for that day. The analysis unit can also learn that the user relaxes during a specific time of day and suggest relaxation methods for that time of day. Furthermore, the analysis unit can learn that the user performs a specific activity in a specific location and suggest activities for that location. This makes it possible to provide a more personalized assistant function by predicting the user's behavioral patterns and suggesting future actions.

[0097] The collection unit can estimate the user's emotion and monitor the user's emotional state in real time based on the estimated user's emotion. For example, if the user is feeling stressed, the collection unit can monitor fluctuations in heart rate and respiratory rate in real time and collect data. In addition, if the user is relaxed, the collection unit can collect surrounding audio data and environmental data and provide information for maintaining a relaxed state. Furthermore, if the user is excited, the collection unit can collect behavioral data and location information and provide information for managing the excited state. This allows for more appropriate data collection and information provision by monitoring the user's emotional state in real time.

[0098] The analysis unit can analyze the user's behavioral patterns based on the user's behavioral data and make suggestions for behavioral improvement. For example, the analysis unit can analyze the user's exercise data and make suggestions for improving exercise habits. The analysis unit can also analyze the user's dietary data and make suggestions for improving dietary habits. Furthermore, the analysis unit can analyze the user's sleep data and make suggestions for improving sleep quality. In this way, by analyzing the user's behavioral patterns and making suggestions for behavioral improvement, the user's quality of life can be improved.

[0099] The conversation unit can estimate the user's emotions and provide topics to support the user's emotions based on the estimated user's emotions. For example, if the user is feeling sad, the conversation unit can provide words of comfort or topics of encouragement. If the user is feeling happy, the conversation unit can also provide words of sympathy or topics of congratulations. Furthermore, if the user is feeling anxious, the conversation unit can also provide topics that give a sense of security. In this way, the user's emotions can be supported by adjusting the content of conversation based on the user's emotions.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects user behavioral data or environmental data. Behavioral data includes, for example, movement history, application usage, and operation logs. Environmental data includes, for example, temperature, humidity, illuminance, and noise level. The collection unit collects data from the user's smartphone, wearable device, and smart devices and sensors in the home. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses machine learning algorithms and data mining techniques to analyze the data and understand the user's situation. For example, it predicts the user's current situation based on the user's past behavioral data. Step 3: The conversation unit speaks to the user at an appropriate time based on the results of the analysis by the analysis unit. For example, it will speak to the user when they are relaxed and not speak to them when they are busy. It also selects what to talk to the user about, providing work-related information when the user is working and topics related to hobbies and interests when the user is relaxing.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user behavior data or environmental data; an analysis unit that analyzes the data collected by the collection unit; a speaking unit that speaks to the user at an appropriate timing based on the results of the analysis by the analysis unit. A system characterized by:

2. The collecting unit Collecting user biometric data 2. The system of claim 1.

3. The collecting unit Collecting user interaction history 2. The system of claim 1.

4. The analysis unit Analyze the user's situation based on the collected data 2. The system of claim 1.

5. The speaking unit is Select what to say to the user based on the analysis results 2. The system of claim 1.

6. Equipped with a privacy protection department that protects the privacy of collected data 2. The system of claim 1.

7. The collecting unit Inferring user sentiment and adjusting the type of data collected based on the estimated user sentiment 2. The system of claim 1.

8. The collecting unit Analyze user behavior patterns and automatically start collecting data when certain actions occur 2. The system of claim 1.

9. The collecting unit Linking multiple sensor devices to improve the accuracy of collected data 2. The system of claim 1.

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