system

Abstract

The system according to this embodiment aims to collect and analyze the user's life log and provide it visually. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a protection unit, and an acquisition unit. The collection unit collects life logs. The analysis unit analyzes the life logs collected by the collection unit. The provision unit provides the results of the analysis performed by the analysis unit visually. The protection unit protects the privacy of the data collected by the collection unit. The acquisition unit allows the user to obtain specific information.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the user's life log has not been sufficiently collected, analyzed, and visually provided, leaving room for improvement.

[0005] The system according to the embodiment aims to collect, analyze, and visually provide the user's life log.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a protection unit, and an acquisition unit. The collection unit collects life logs. The analysis unit analyzes the life logs collected by the collection unit. The provision unit provides the results of the analysis performed by the analysis unit visually. The protection unit protects the privacy of the data collected by the collection unit. The acquisition unit allows the user to obtain specific information. [Effects of the Invention]

[0007] The system according to this embodiment can collect and analyze a user's life log and provide it visually. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM​​​The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The memory recall AI glasses system according to an embodiment of the present invention is a glasses-type device that collects the user's life log (behavior, conversation, location information, etc.) in real time, and an AI agent visually provides past scenes and conversation content. This memory recall AI glasses system allows users to review past scenes, information about the person they are talking to, and past conversation content through the glasses when they want to recall something or are conversing with someone. For example, the memory recall AI glasses system addresses issues such as memory decline in the elderly, missed meeting details in business, the need for educators to help students understand, and the need for medical professionals to quickly review patients' medical histories. Based on the life log collected by the AI ​​agent, the system visually provides the user with the necessary information. Specifically, it enables automatic recording and summarization of conversation content, schedule management, and immediate review of past conversation history. It also includes privacy protection features. Methods of utilizing generative AI include LLM (Large-Scale Language Modeling), NLP (Natural Language Processing), multimodal data analysis technology, privacy protection technology, and AI agent technology. The global market size is expected to reach several billion dollars when combining the markets for seniors suffering from memory decline, business professionals, education, and healthcare. With the advancement of an aging society, the problem of memory decline is becoming increasingly serious, and the problem of information overload is becoming apparent in business, education, and healthcare. The need for efficient management and utilization of this information is growing, making now the time to enter the market. Ultimately, the goal is to create a society where no one is troubled by memory problems or the complexities of information management, enriching and improving lives through the power of technology, and supporting increased productivity and quality of life by making information more accessible to people of all generations. To achieve this, the memory recall AI glasses system will collect, analyze, provide, protect, and retrieve users' life logs, allowing them to visually access the information they need.

[0029] The memory recall AI glasses system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a protection unit, and an acquisition unit. The collection unit collects the user's life log. The life log includes, but is not limited to, action logs, conversation logs, and location information logs. For example, the collection unit detects the user's actions with sensors and records them as action logs. The collection unit can also record the user's conversations with a microphone and record them as conversation logs. Furthermore, the collection unit can acquire the user's location information with GPS and record it as location information logs. For example, the collection unit tracks the user's movement path in real time and saves it as location information logs. The analysis unit analyzes the life log collected by the collection unit. For example, the analysis unit analyzes the collected action logs to identify the user's behavior patterns. The analysis unit can also analyze the collected conversation logs and summarize the conversation content. Furthermore, the analysis unit can analyze the collected location information logs and identify the user's movement patterns. For example, the analysis unit analyzes user behavior patterns using a clustering algorithm and extracts specific behavior patterns. The provision unit visually provides the results analyzed by the analysis unit. The provision unit displays the analysis results as graphs or charts, for example. The provision unit can also display the analysis results as a dashboard. Furthermore, the provision unit can notify the user of the analysis results. For example, the provision unit displays user behavior patterns as graphs, providing them in a visually easy-to-understand format. The protection unit protects the privacy of data collected by the collection unit. The protection unit encrypts the collected data, for example. The protection unit can also control access to the collected data. Furthermore, the protection unit can anonymize the collected data. For example, the protection unit encrypts the collected data using the AES encryption algorithm to ensure data security. The acquisition unit retrieves specific information from the user. For example, the acquisition unit searches for past conversation content from the user. The acquisition unit can also allow the user to check their past behavior history. Furthermore, the acquisition unit can allow the user to check their past location information. For example, the acquisition unit searches for conversation content from a specific date and time and displays it immediately.As a result, the memory recall AI glasses system according to this embodiment can collect, analyze, provide, protect, and acquire the user's life log, allowing the user to visually confirm the information they need.

[0030] The data collection unit collects the user's life log. This life log includes, but is not limited to, activity logs, conversation logs, and location logs. For example, the unit detects the user's actions using sensors and records them as activity logs. Specifically, the AI ​​glasses worn by the user have built-in accelerometers and gyroscopes, which detect the user's movements in real time. This allows for detailed recording of whether the user is walking, sitting, or performing a specific action. The data collection unit can also record the user's conversations using a microphone and record them as conversation logs. The AI ​​glasses are equipped with a high-sensitivity microphone that can clearly record the user's speech while eliminating ambient noise. Furthermore, the data collection unit can acquire the user's location information via GPS and record it as a location log. The AI ​​glasses incorporate a high-precision GPS module that tracks the user's movement path in real time and saves it as a location log. For example, the data collection unit records in detail the places the user has visited and the routes they have traveled, allowing for later reference. This allows the data collection unit to collect user life logs from multiple perspectives and provide detailed data.

[0031] The analysis unit analyzes the life logs collected by the collection unit. For example, the analysis unit analyzes the collected behavioral logs to identify the user's behavioral patterns. Specifically, the AI ​​installed in the AI ​​glasses analyzes the collected behavioral logs using machine learning algorithms and clusters the user's behavioral patterns. This makes it possible to identify the actions the user performs on a daily basis and the activities they perform at specific times of day. The analysis unit can also analyze the collected conversation logs and summarize the content of conversations. Using natural language processing technology, it extracts important points and keywords from conversations and summarizes information useful to the user. Furthermore, the analysis unit can analyze the collected location information logs and identify the user's movement patterns. For example, the analysis unit analyzes places the user frequently visits and travel routes to understand the user's behavioral tendencies. This allows the analysis unit to analyze the user's life logs in detail and provide useful information about the user's behavior, conversations, and movements.

[0032] The service provider visually presents the results analyzed by the analysis unit. For example, the service provider displays the analysis results as graphs or charts. The AI ​​glasses display visually shows the user's behavior patterns and travel routes, allowing the user to understand them intuitively. The service provider can also display the analysis results as a dashboard. Through the AI ​​glasses interface, the user can access a dashboard where they can see the analysis results at a glance and view detailed information. Furthermore, the service provider can notify the user of the analysis results. For example, the service provider can display the user's behavior patterns as graphs, providing them in a visually easy-to-understand format. This helps the service provider to quickly and accurately grasp the information the user needs.

[0033] The protection unit protects the privacy of data collected by the collection unit. For example, the protection unit encrypts the collected data. Specifically, the collected data is encrypted using the AES encryption algorithm to ensure data security. The protection unit can also control access to the collected data. Users can finely configure who can access which data through the settings of the AI ​​glasses. Furthermore, the protection unit can anonymize the collected data. For example, it removes personally identifiable information from the collected data to ensure data anonymity. In this way, the protection unit can maintain the usefulness of the data while protecting user privacy.

[0034] The acquisition unit retrieves specific information from the user. For example, the acquisition unit searches for past conversation content. Through the AI ​​glasses interface, the user can enter specific keywords or dates and times to search past conversation logs. The acquisition unit also allows the user to review their past activity history. Users can easily review their past activity history through the calendar and timeline displayed on the AI ​​glasses' screen. Furthermore, the acquisition unit also allows the user to review their past location information. For example, the acquisition unit searches for the user's location information for a specific date and time and displays it instantly. In this way, the acquisition unit helps the user quickly and accurately obtain the information they need.

[0035] The recording unit can automatically record and summarize conversation content. For example, the recording unit can automatically record conversation content using speech recognition technology. The recording unit can also summarize the recorded conversation content using a summarization algorithm. Furthermore, the recording unit can save the conversation content as text data. For example, the recording unit can perform speech recognition on the conversation content in real time and save it as text data. This enables automatic recording and summarization of conversation content. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input audio data into a generative AI, which can then convert the audio data into text data and generate a summary.

[0036] The management department can manage schedules. For example, the management department can manage users' schedules using a calendar function. The management department can also notify users of appointments using a reminder function. Furthermore, the management department can modify and add to schedules. For example, the management department can register users' appointments in the calendar and notify them with reminders. This enables schedule management. Some or all of the above processes performed by the management department may be performed using, for example, a generation AI, or not using a generation AI. For example, the management department can input user schedule data into a generation AI, which can then optimize the schedule and set reminders.

[0037] The verification unit can instantly verify past conversation history. For example, the verification unit can search past conversation history using a search function. The verification unit can also extract specific conversation content using a filtering function. Furthermore, the verification unit can display the conversation history in chronological order. For example, the verification unit can search past conversation history by having the user enter specific keywords and display it immediately. This enables instant verification of past conversation history. Some or all of the above processing in the verification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the verification unit can input conversation data into a generative AI, which can analyze the conversation content and provide search results based on specific keywords.

[0038] The data collection unit can instantly collect user activity, conversations, and location information—a life log. For example, the unit can instantly record user activity using real-time data collection technology. It can also instantly record user conversations using streaming technology. Furthermore, it can instantly record user location information using GPS technology. For example, the unit can track the user's movement path in real time and save it as a location log. This makes it possible to collect the user's life log in real time. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the real-time collected data into a generative AI, which can then analyze and save the data.

[0039] The analysis unit can analyze collected life logs and identify past scenes and conversation content. For example, the analysis unit can analyze collected image data using image recognition technology to identify past scenes. The analysis unit can also analyze collected audio data using speech analysis technology to identify past conversation content. Furthermore, the analysis unit can analyze collected text data using text analysis technology to identify past events. For example, the analysis unit can analyze collected image data using a deep learning algorithm to extract specific scenes. This makes it possible to analyze collected life logs and identify past scenes and conversation content. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input collected data into a generative AI, which can analyze the data and identify past scenes and conversation content.

[0040] The data collection unit can analyze the user's past behavior patterns and select the optimal data collection timing. For example, the data collection unit can analyze the user's past behavior patterns using behavior pattern analysis technology. The data collection unit can also identify the user's activity times using time-of-day analysis technology. Furthermore, the data collection unit can select the optimal data collection timing based on the user's past behavior patterns. For example, if the data collection unit found that the user was actively engaged in activities during a specific time period in the past, it can concentrate data collection during that time period. If the data collection unit found that the user was very active in a specific location in the past, it can also strengthen data collection at that location. The data collection unit can also predict the optimal timing for data collection from the user's past behavior patterns and collect data efficiently. In this way, data is collected efficiently by analyzing the user's past behavior patterns and selecting the optimal data collection timing. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past behavior data into a generative AI, which can analyze the behavior patterns and select the optimal data collection timing.

[0041] The data collection unit can filter the collected lifelog data based on the user's current activity status. For example, the data collection unit can evaluate the importance of the collected data using importance scoring technology. The data collection unit can also analyze the user's activity status using context analysis technology. Furthermore, the data collection unit can filter the collected data based on the user's current activity status. For example, if the user is in a meeting, the data collection unit can prioritize collecting conversation content and filter other data. If the user is exercising, the data collection unit can prioritize collecting location information and activity data and filter conversation content. If the user is resting, the data collection unit can collect all data equally and record a detailed lifelog. This prioritizes the collection of important information by filtering based on the user's current activity status. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's activity data into a generative AI, which can analyze the activity status and filter the collected data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting life logs. For example, the data collection unit can analyze the user's geographical location information using GPS data analysis technology. The data collection unit can also extract highly relevant data using location information filtering technology. Furthermore, the data collection unit can prioritize the collection of data based on the user's geographical location information. For example, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. If the user is on the move, the data collection unit can also prioritize the collection of data related to the travel route. If the user is at home, the data collection unit can also prioritize the collection of data related to activities at home. This allows for efficient data collection by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's location information data into a generative AI, which can analyze the location information and prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data when collecting life logs. For example, the data collection unit can analyze a user's social media activity using post content analysis technology. The data collection unit can also evaluate a user's social media activity using engagement analysis technology. Furthermore, the data collection unit can collect relevant data based on the user's social media activity. For example, if a user posts about a specific topic on social media, the data collection unit can collect data related to that topic. If a user interacts with a specific person on social media, the data collection unit can also collect data related to that person. If a user participates in a specific event on social media, the data collection unit can also collect data related to that event. In this way, data is efficiently collected by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input the user's social media data into a generative AI, which can analyze the activity and collect relevant data.

[0044] The analysis unit can analyze current data by referring to past life log data during analysis. The analysis unit can refer to past life log data using, for example, database search technology. The analysis unit can also analyze past data using time series analysis technology. Furthermore, the analysis unit can analyze current data based on past life log data. For example, the analysis unit can analyze trends in current data based on past life log data. The analysis unit can also detect anomalies in current data by referring to past life log data. The analysis unit can also make predictions about current data based on past life log data. In this way, by analyzing current data by referring to past life log data, trends and anomalies in the data can be grasped. Some or all of the above processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input past life log data into a generation AI, and the generation AI can analyze the data to grasp trends and anomalies in the current data.

[0045] The analysis unit can apply different analysis algorithms to each category of lifelog data during analysis. For example, the analysis unit can analyze behavioral data using a clustering algorithm. It can also analyze conversational data using a natural language processing algorithm. Furthermore, it can analyze location data using a geographic information analysis algorithm. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral data to analyze detailed behavioral patterns. It can also apply a natural language processing algorithm to conversational data to analyze conversational content. It can also apply a geographic information analysis algorithm to location data to analyze movement patterns. This allows for detailed analysis by applying different analysis algorithms to each category of lifelog data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input lifelog data into a generative AI, which can then analyze the data by applying different analysis algorithms to each category.

[0046] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can evaluate the importance of the information using importance evaluation technology. The provider can also evaluate the urgency of the information. Furthermore, the provider can adjust the level of detail provided based on the importance of the information. For example, the provider can provide highly important information in detail to make it easier for the user to understand. The provider can also provide less important information concisely to reduce the burden on the user. The provider can also dynamically adjust the level of detail provided based on the importance of the information. This allows the provider to provide information that is appropriate for the user by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input information importance data into a generative AI, which can evaluate the importance and adjust the level of detail provided.

[0047] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the information provider can provide information using a recommendation algorithm. The information provider can also provide information using personalized filtering technology. Furthermore, the information provider can apply different information provision algorithms depending on the information category. For example, the information provider can apply a behavioral analysis algorithm to behavioral data to provide detailed behavioral patterns. The information provider can also apply a natural language processing algorithm to conversational data to provide conversational content. The information provider can also apply a geographic information analysis algorithm to location data to provide movement patterns. In this way, detailed information is provided by applying different information provision algorithms depending on the information category. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input information data into a generative AI, and the generative AI can apply different information provision algorithms for each category to provide the information.

[0048] The protection unit can select the optimal protection method by referring to past data protection history when protecting privacy. The protection unit can refer to past data protection history, for example, using log data analysis technology. The protection unit can also search past data protection history using history database search technology. Furthermore, the protection unit can select the optimal protection method based on past data protection history. For example, the protection unit can select the optimal protection method based on privacy protection methods used in the past. The protection unit can also select a protection method that suits the user's preferences by referring to past data protection history. The protection unit can also select the most suitable protection method for the current situation based on past data protection history. This makes it possible to provide privacy protection that suits the user's preferences by selecting the optimal protection method by referring to past data protection history. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the protection unit can input past data protection history into a generative AI, which can analyze the history and select the optimal protection method.

[0049] The protection unit can apply different protection methods to each data category when protecting privacy. For example, the protection unit can protect data using data encryption methods. It can also protect data using access control methods. Furthermore, the protection unit can apply different protection methods to each data category. For example, the protection unit can apply behavioral analysis algorithms to behavioral data to protect detailed behavioral patterns. The protection unit can also apply natural language processing algorithms to conversational data to protect conversation content. The protection unit can also apply geographic information analysis algorithms to location data to protect movement patterns. This enables detailed privacy protection by applying different protection methods to each data category. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the protection unit can input data into a generative AI, which can then protect the data by applying different protection methods to each category.

[0050] The protection unit can determine the priority of protection based on when the data was collected when protecting privacy. For example, the protection unit can protect data using priority protection techniques for the most recent data. It can also protect data using archiving techniques for older data. Furthermore, the protection unit can determine the priority of protection based on when the data was collected. For example, the protection unit can prioritize the protection of recently collected data to protect the most up-to-date information. The protection unit can also prioritize the protection of data collected during a specific period to protect information from that period. The protection unit can also prioritize the protection of historical data to protect long-term information. In this way, by determining the priority of protection based on when the data was collected, the most up-to-date information is protected first. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the protection unit can input data collection timing data into a generative AI, which can analyze the collection timing and determine the priority of protection.

[0051] The protection unit can adjust the order of protection based on data relevance when protecting privacy. The protection unit can evaluate data relevance using, for example, data correlation analysis techniques. It can also evaluate data relevance using context analysis techniques. Furthermore, the protection unit can adjust the order of protection based on data relevance. For example, the protection unit can prioritize the protection of highly relevant data to protect important information. The protection unit can also efficiently protect less relevant data by delaying its protection. The protection unit can also dynamically adjust the order of protection based on data relevance. This allows for the priority protection of important information by adjusting the order of protection based on data relevance. Some or all of the above processing in the protection unit may be performed using, for example, generative AI, or without generative AI. For example, the protection unit can input data relevance data into a generative AI, which can analyze the relevance and adjust the order of protection.

[0052] The acquisition unit can select the optimal acquisition method by referring to past acquisition history at the time of acquisition. The acquisition unit can refer to past acquisition history using, for example, log data analysis technology. The acquisition unit can also search past acquisition history using history database search technology. Furthermore, the acquisition unit can select the optimal acquisition method based on past acquisition history. For example, the acquisition unit can select the optimal acquisition method based on acquisition methods used in the past. The acquisition unit can also select an acquisition method that suits the user's preferences by referring to past acquisition history. The acquisition unit can also select the acquisition method that is best suited to the current situation based on past acquisition history. This makes it possible to acquire information that suits the user's preferences by selecting the optimal acquisition method by referring to past acquisition history. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input past acquisition history into a generation AI, which can analyze the history and select the optimal acquisition method.

[0053] The acquisition unit can apply different acquisition methods to each category of information during acquisition. For example, the acquisition unit can acquire information using data mining techniques. It can also acquire information using information retrieval techniques. Furthermore, the acquisition unit can apply different acquisition methods to each category of information. For example, the acquisition unit can apply behavioral analysis algorithms to behavioral data to acquire detailed behavioral patterns. The acquisition unit can also apply natural language processing algorithms to conversational data to acquire conversational content. The acquisition unit can also apply geographic information analysis algorithms to location data to acquire movement patterns. This makes it possible to acquire detailed information by applying different acquisition methods to each category of information. Some or all of the above processing in the acquisition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the acquisition unit can input information data into a generative AI, and the generative AI can acquire information by applying different acquisition methods to each category.

[0054] The acquisition unit can determine the acquisition priority based on the information collection timing at the time of acquisition. The acquisition unit can acquire information using, for example, a technique for prioritizing the acquisition of the latest information. The acquisition unit can also acquire information using an archiving technique for older information. Furthermore, the acquisition unit can also determine the acquisition priority based on the information collection timing. For example, the acquisition unit can prioritize the acquisition of recently collected information and provide the latest information to the user. The acquisition unit can also prioritize the acquisition of information collected during a specific period and provide the user with information from that period. The acquisition unit can also prioritize the acquisition of historical information and provide the user with long-term information. In this way, by determining the acquisition priority based on the information collection timing, the latest information is acquired preferentially. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without a generating AI. For example, the acquisition unit can input information collection timing data into a generating AI, which can analyze the collection timing and determine the acquisition priority.

[0055] The data acquisition unit can adjust the acquisition order based on the relevance of the information during acquisition. The data acquisition unit can evaluate the relevance of information using, for example, information correlation analysis techniques. The data acquisition unit can also evaluate the relevance of information using context analysis techniques. Furthermore, the data acquisition unit can adjust the acquisition order based on the relevance of the information. For example, the data acquisition unit can prioritize the acquisition of highly relevant information and provide important information to the user. The data acquisition unit can also efficiently acquire information by delaying the acquisition of less relevant information. The data acquisition unit can also dynamically adjust the acquisition order based on the relevance of the information. This allows for the priority acquisition of important information by adjusting the acquisition order based on the relevance of the information. Some or all of the above processing in the data acquisition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data acquisition unit can input information relevance data into a generative AI, which can analyze the relevance and adjust the acquisition order.

[0056] The recording unit can select the optimal recording method by referring to past recording history when recording. The recording unit can refer to past recording history using, for example, log data analysis technology. The recording unit can also search past recording history using history database search technology. Furthermore, the recording unit can select the optimal recording method based on past recording history. For example, the recording unit can select the optimal recording method based on recording methods used in the past. The recording unit can also select a recording method that suits the user's preferences by referring to past recording history. The recording unit can also select the optimal recording method for the current situation based on past recording history. This makes it possible to record in a way that suits the user's preferences by selecting the optimal recording method by referring to past recording history. Some or all of the above processing in the recording unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recording unit can input past recording history into a generation AI, which can analyze the history and select the optimal recording method.

[0057] The recording unit can determine the priority of recording based on the timing of information collection. For example, the recording unit can record information using a technique that prioritizes the most recent information. The recording unit can also record information using an archiving technique for older information. Furthermore, the recording unit can determine the priority of recording based on the timing of information collection. For example, the recording unit can prioritize recording recently collected information and save the latest information. The recording unit can also prioritize recording information collected during a specific period and save the information for that period. The recording unit can also prioritize recording historical information and save long-term information. In this way, by determining the priority of recording based on the timing of information collection, the latest information is recorded first. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input information collection timing data into a generative AI, which can analyze the collection timing and determine the priority of recording.

[0058] The management department can select the optimal management method by referring to past schedule history when managing schedules. For example, the management department can refer to past schedule history using log data analysis technology. The management department can also search past schedule history using history database search technology. Furthermore, the management department can select the optimal management method based on past schedule history. For example, the management department can select the optimal management method based on schedule management methods used in the past. The management department can also select a management method that suits the user's preferences by referring to past schedule history. The management department can also select the management method that is best suited to the current situation based on past schedule history. This makes it possible to manage schedules that suit the user's preferences by selecting the optimal management method by referring to past schedule history. Some or all of the above processes in the management department may be performed using, for example, a generation AI, or without a generation AI. For example, the management department can input past schedule history into a generation AI, which can analyze the history and select the optimal management method.

[0059] The management department can determine management priorities based on the timing of information collection when managing schedules. For example, the management department can manage information using techniques for prioritizing the latest information. It can also manage information using archiving techniques for older information. Furthermore, the management department can determine management priorities based on the timing of information collection. For example, the management department can prioritize the management of recently collected information and provide the latest schedule. The management department can also prioritize the management of information collected during a specific period and provide the schedule for that period. The management department can also prioritize the management of historical information and provide long-term schedules. In this way, by determining management priorities based on the timing of information collection, the latest schedule is prioritized. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input information collection timing data into a generative AI, which can analyze the collection timing and determine management priorities.

[0060] The verification unit can select the optimal verification method by referring to past verification history during verification. The verification unit can refer to past verification history using, for example, log data analysis technology. The verification unit can also search past verification history using history database search technology. Furthermore, the verification unit can select the optimal verification method based on past verification history. For example, the verification unit can select the optimal verification method based on verification methods used in the past. The verification unit can also select a verification method that suits the user's preferences by referring to past verification history. The verification unit can also select the verification method that is best suited to the current situation based on past verification history. This makes it possible to perform verification that suits the user's preferences by selecting the optimal verification method by referring to past verification history. Some or all of the above processing in the verification unit may be performed using, for example, a generation AI, or without a generation AI. For example, the verification unit can input past verification history into a generation AI, which can analyze the history and select the optimal verification method.

[0061] The verification unit can determine the priority of verification based on the information collection period during verification. For example, the verification unit can verify information using a technique that prioritizes the verification of the latest information. The verification unit can also verify information using an archiving technique for older information. Furthermore, the verification unit can also determine the priority of verification based on the information collection period. For example, the verification unit can prioritize the verification of recently collected information and provide the latest information. The verification unit can also prioritize the verification of information collected during a specific period and provide information for that period. The verification unit can also prioritize the verification of past information and provide long-term information. In this way, by determining the priority of verification based on the information collection period, the latest information is prioritized. Some or all of the above processing in the verification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the verification unit can input information collection period data into a generative AI, which can analyze the collection period and determine the priority of verification.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The memory recall AI glasses system can also be equipped with a health management unit that monitors the user's health status. This unit can measure vital signs such as heart rate, blood pressure, and body temperature using sensors and monitor them in real time. Furthermore, the health management unit can issue alerts based on the user's health status. For example, if the heart rate is abnormally high, it can display an alert prompting the user to rest. In addition, the health management unit can analyze the user's health data to understand health trends. This enables real-time monitoring of the user's health status and the provision of appropriate advice.

[0064] The memory recall AI glasses system can also be equipped with a meal management unit that manages the user's meal records. For example, the meal management unit can take photos of the meals the user has eaten and analyze the meal content using image recognition technology. It can also record the user's meal history and evaluate nutritional balance. Furthermore, the meal management unit can provide dietary advice based on the user's health condition. For example, if the user is nutritionally deficient, it can suggest a nutritionally balanced meal. This makes it possible to manage the user's meal records and support a healthy diet.

[0065] The memory recall AI glasses system can also be equipped with an exercise management unit that manages the user's exercise records. For example, the exercise management unit can detect the user's exercise using sensors and record it as an exercise log. It can also analyze the user's exercise history and identify exercise patterns. Furthermore, the exercise management unit can provide exercise advice based on the user's health status. For example, if the user is not getting enough exercise, it can suggest an appropriate exercise plan. This makes it possible to manage the user's exercise records and support a healthy lifestyle.

[0066] The memory recall AI glasses system can also be equipped with a sleep management unit that monitors the user's sleep state. For example, the sleep management unit can detect the user's movements during sleep using sensors and record them as a sleep log. It can also analyze the user's sleep history and identify sleep patterns. Furthermore, the sleep management unit can provide sleep advice based on the user's health condition. For instance, if the user is sleep-deprived, it can suggest an appropriate sleep plan. This allows for monitoring the user's sleep state and supporting healthy sleep.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects the user's life log. The life log includes activity logs, conversation logs, location logs, etc. The data collection unit detects the user's actions with sensors and records them as activity logs. It can also record the user's conversations with a microphone and record them as conversation logs. Furthermore, it can acquire the user's location information using GPS and record it as a location log. For example, the data collection unit tracks the user's movement route in real time and saves it as a location log. Step 2: The analysis unit analyzes the life logs collected by the collection unit. The analysis unit analyzes the collected behavioral logs to identify the user's behavioral patterns. It can also analyze the collected conversation logs and summarize the conversation content. Furthermore, it can analyze the collected location information logs to identify the user's movement patterns. For example, the analysis unit analyzes the user's behavioral patterns using a clustering algorithm and extracts specific behavioral patterns. Step 3: The service provider visually presents the results analyzed by the analysis unit. The service provider displays the analysis results as graphs and charts. It can also display the analysis results as a dashboard. Furthermore, it can notify the user of the analysis results. For example, the service provider displays the user's behavior patterns as graphs, providing them in a visually easy-to-understand format. Step 4: The protection unit protects the privacy of the data collected by the collection unit. The protection unit encrypts the collected data. It can also control access to the collected data. Furthermore, it can anonymize the collected data. For example, the protection unit encrypts the collected data using the AES encryption algorithm to ensure data security. Step 5: The retrieval unit retrieves specific information from the user. The retrieval unit searches for past conversation content from the user. The user can also review their past activity history. Furthermore, the user can review their past location information. For example, the retrieval unit searches for conversation content from a specific date and time and displays it immediately.

[0069] (Example of form 2) The memory recall AI glasses system according to an embodiment of the present invention is a glasses-type device that collects the user's life log (behavior, conversation, location information, etc.) in real time, and an AI agent visually provides past scenes and conversation content. This memory recall AI glasses system allows users to review past scenes, information about the person they are talking to, and past conversation content through the glasses when they want to recall something or are conversing with someone. For example, the memory recall AI glasses system addresses issues such as memory decline in the elderly, missed meeting details in business, the need for educators to help students understand, and the need for medical professionals to quickly review patients' medical histories. Based on the life log collected by the AI ​​agent, the system visually provides the user with the necessary information. Specifically, it enables automatic recording and summarization of conversation content, schedule management, and immediate review of past conversation history. It also includes privacy protection features. Methods of utilizing generative AI include LLM (Large-Scale Language Modeling), NLP (Natural Language Processing), multimodal data analysis technology, privacy protection technology, and AI agent technology. The global market size is expected to reach several billion dollars when combining the markets for seniors suffering from memory decline, business professionals, education, and healthcare. With the advancement of an aging society, the problem of memory decline is becoming increasingly serious, and the problem of information overload is becoming apparent in business, education, and healthcare. The need for efficient management and utilization of this information is growing, making now the time to enter the market. Ultimately, the goal is to create a society where no one is troubled by memory problems or the complexities of information management, enriching and improving lives through the power of technology, and supporting increased productivity and quality of life by making information more accessible to people of all generations. To achieve this, the memory recall AI glasses system will collect, analyze, provide, protect, and retrieve users' life logs, allowing them to visually access the information they need.

[0070] The memory recall AI glasses system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a protection unit, and an acquisition unit. The collection unit collects the user's life log. The life log includes, but is not limited to, action logs, conversation logs, and location information logs. For example, the collection unit detects the user's actions with sensors and records them as action logs. The collection unit can also record the user's conversations with a microphone and record them as conversation logs. Furthermore, the collection unit can acquire the user's location information with GPS and record it as location information logs. For example, the collection unit tracks the user's movement path in real time and saves it as location information logs. The analysis unit analyzes the life log collected by the collection unit. For example, the analysis unit analyzes the collected action logs to identify the user's behavior patterns. The analysis unit can also analyze the collected conversation logs and summarize the conversation content. Furthermore, the analysis unit can analyze the collected location information logs and identify the user's movement patterns. For example, the analysis unit analyzes user behavior patterns using a clustering algorithm and extracts specific behavior patterns. The provision unit visually provides the results analyzed by the analysis unit. The provision unit displays the analysis results as graphs or charts, for example. The provision unit can also display the analysis results as a dashboard. Furthermore, the provision unit can notify the user of the analysis results. For example, the provision unit displays user behavior patterns as graphs, providing them in a visually easy-to-understand format. The protection unit protects the privacy of data collected by the collection unit. The protection unit encrypts the collected data, for example. The protection unit can also control access to the collected data. Furthermore, the protection unit can anonymize the collected data. For example, the protection unit encrypts the collected data using the AES encryption algorithm to ensure data security. The acquisition unit retrieves specific information from the user. For example, the acquisition unit searches for past conversation content from the user. The acquisition unit can also allow the user to check their past behavior history. Furthermore, the acquisition unit can allow the user to check their past location information. For example, the acquisition unit searches for conversation content from a specific date and time and displays it immediately.As a result, the memory recall AI glasses system according to this embodiment can collect, analyze, provide, protect, and acquire the user's life log, allowing the user to visually confirm the information they need.

[0071] The data collection unit collects the user's life log. This life log includes, but is not limited to, activity logs, conversation logs, and location logs. For example, the unit detects the user's actions using sensors and records them as activity logs. Specifically, the AI ​​glasses worn by the user have built-in accelerometers and gyroscopes, which detect the user's movements in real time. This allows for detailed recording of whether the user is walking, sitting, or performing a specific action. The data collection unit can also record the user's conversations using a microphone and record them as conversation logs. The AI ​​glasses are equipped with a high-sensitivity microphone that can clearly record the user's speech while eliminating ambient noise. Furthermore, the data collection unit can acquire the user's location information via GPS and record it as a location log. The AI ​​glasses incorporate a high-precision GPS module that tracks the user's movement path in real time and saves it as a location log. For example, the data collection unit records in detail the places the user has visited and the routes they have traveled, allowing for later reference. This allows the data collection unit to collect user life logs from multiple perspectives and provide detailed data.

[0072] The analysis unit analyzes the life logs collected by the collection unit. For example, the analysis unit analyzes the collected behavioral logs to identify the user's behavioral patterns. Specifically, the AI ​​installed in the AI ​​glasses analyzes the collected behavioral logs using machine learning algorithms and clusters the user's behavioral patterns. This makes it possible to identify the actions the user performs on a daily basis and the activities they perform at specific times of day. The analysis unit can also analyze the collected conversation logs and summarize the content of conversations. Using natural language processing technology, it extracts important points and keywords from conversations and summarizes information useful to the user. Furthermore, the analysis unit can analyze the collected location information logs and identify the user's movement patterns. For example, the analysis unit analyzes places the user frequently visits and travel routes to understand the user's behavioral tendencies. This allows the analysis unit to analyze the user's life logs in detail and provide useful information about the user's behavior, conversations, and movements.

[0073] The service provider visually presents the results analyzed by the analysis unit. For example, the service provider displays the analysis results as graphs or charts. The AI ​​glasses display visually shows the user's behavior patterns and travel routes, allowing the user to understand them intuitively. The service provider can also display the analysis results as a dashboard. Through the AI ​​glasses interface, the user can access a dashboard where they can see the analysis results at a glance and view detailed information. Furthermore, the service provider can notify the user of the analysis results. For example, the service provider can display the user's behavior patterns as graphs, providing them in a visually easy-to-understand format. This helps the service provider to quickly and accurately grasp the information the user needs.

[0074] The protection unit protects the privacy of data collected by the collection unit. For example, the protection unit encrypts the collected data. Specifically, the collected data is encrypted using the AES encryption algorithm to ensure data security. The protection unit can also control access to the collected data. Users can finely configure who can access which data through the settings of the AI ​​glasses. Furthermore, the protection unit can anonymize the collected data. For example, it removes personally identifiable information from the collected data to ensure data anonymity. In this way, the protection unit can maintain the usefulness of the data while protecting user privacy.

[0075] The acquisition unit retrieves specific information from the user. For example, the acquisition unit searches for past conversation content. Through the AI ​​glasses interface, the user can enter specific keywords or dates and times to search past conversation logs. The acquisition unit also allows the user to review their past activity history. Users can easily review their past activity history through the calendar and timeline displayed on the AI ​​glasses' screen. Furthermore, the acquisition unit also allows the user to review their past location information. For example, the acquisition unit searches for the user's location information for a specific date and time and displays it instantly. In this way, the acquisition unit helps the user quickly and accurately obtain the information they need.

[0076] The recording unit can automatically record and summarize conversation content. For example, the recording unit can automatically record conversation content using speech recognition technology. The recording unit can also summarize the recorded conversation content using a summarization algorithm. Furthermore, the recording unit can save the conversation content as text data. For example, the recording unit can perform speech recognition on the conversation content in real time and save it as text data. This enables automatic recording and summarization of conversation content. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input audio data into a generative AI, which can then convert the audio data into text data and generate a summary.

[0077] The management department can manage schedules. For example, the management department can manage users' schedules using a calendar function. The management department can also notify users of appointments using a reminder function. Furthermore, the management department can modify and add to schedules. For example, the management department can register users' appointments in the calendar and notify them with reminders. This enables schedule management. Some or all of the above processes performed by the management department may be performed using, for example, a generation AI, or not using a generation AI. For example, the management department can input user schedule data into a generation AI, which can then optimize the schedule and set reminders.

[0078] The verification unit can instantly verify past conversation history. For example, the verification unit can search past conversation history using a search function. The verification unit can also extract specific conversation content using a filtering function. Furthermore, the verification unit can display the conversation history in chronological order. For example, the verification unit can search past conversation history by having the user enter specific keywords and display it immediately. This enables instant verification of past conversation history. Some or all of the above processing in the verification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the verification unit can input conversation data into a generative AI, which can analyze the conversation content and provide search results based on specific keywords.

[0079] The data collection unit can instantly collect user activity, conversations, and location information—a life log. For example, the unit can instantly record user activity using real-time data collection technology. It can also instantly record user conversations using streaming technology. Furthermore, it can instantly record user location information using GPS technology. For example, the unit can track the user's movement path in real time and save it as a location log. This makes it possible to collect the user's life log in real time. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the real-time collected data into a generative AI, which can then analyze and save the data.

[0080] The analysis unit can analyze collected life logs and identify past scenes and conversation content. For example, the analysis unit can analyze collected image data using image recognition technology to identify past scenes. The analysis unit can also analyze collected audio data using speech analysis technology to identify past conversation content. Furthermore, the analysis unit can analyze collected text data using text analysis technology to identify past events. For example, the analysis unit can analyze collected image data using a deep learning algorithm to extract specific scenes. This makes it possible to analyze collected life logs and identify past scenes and conversation content. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input collected data into a generative AI, which can analyze the data and identify past scenes and conversation content.

[0081] The data collection unit can estimate the user's emotions and adjust the frequency of life log collection based on the estimated emotions. The data collection unit can estimate the user's emotions using, for example, facial recognition technology. The data collection unit can also estimate the user's emotions using voice tone analysis technology. Furthermore, the data collection unit can adjust the frequency of life log collection based on the user's emotions. For example, if the user is stressed, the data collection unit can reduce the user's burden by lowering the collection frequency. If the user is relaxed, the data collection unit can increase the collection frequency to collect more detailed life logs. If the user is excited, the data collection unit can set the collection frequency to a moderate level to avoid missing important information. In this way, the user's burden is reduced by adjusting the frequency of life log collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input user facial expression data into a generating AI, which can then estimate emotions and adjust the collection frequency.

[0082] The data collection unit can analyze the user's past behavior patterns and select the optimal data collection timing. For example, the data collection unit can analyze the user's past behavior patterns using behavior pattern analysis technology. The data collection unit can also identify the user's activity times using time-of-day analysis technology. Furthermore, the data collection unit can select the optimal data collection timing based on the user's past behavior patterns. For example, if the data collection unit found that the user was actively engaged in activities during a specific time period in the past, it can concentrate data collection during that time period. If the data collection unit found that the user was very active in a specific location in the past, it can also strengthen data collection at that location. The data collection unit can also predict the optimal timing for data collection from the user's past behavior patterns and collect data efficiently. In this way, data is collected efficiently by analyzing the user's past behavior patterns and selecting the optimal data collection timing. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past behavior data into a generative AI, which can analyze the behavior patterns and select the optimal data collection timing.

[0083] The data collection unit can filter the collected lifelog data based on the user's current activity status. For example, the data collection unit can evaluate the importance of the collected data using importance scoring technology. The data collection unit can also analyze the user's activity status using context analysis technology. Furthermore, the data collection unit can filter the collected data based on the user's current activity status. For example, if the user is in a meeting, the data collection unit can prioritize collecting conversation content and filter other data. If the user is exercising, the data collection unit can prioritize collecting location information and activity data and filter conversation content. If the user is resting, the data collection unit can collect all data equally and record a detailed lifelog. This prioritizes the collection of important information by filtering based on the user's current activity status. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's activity data into a generative AI, which can analyze the activity status and filter the collected data.

[0084] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. The data collection unit can estimate the user's emotions using, for example, facial recognition technology. The data collection unit can also estimate the user's emotions using voice tone analysis technology. Furthermore, the data collection unit can determine the priority of data to collect based on the user's emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data that is causing stress. If the user is relaxed, the data collection unit can also prioritize collecting data that is contributing to relaxation. If the user is excited, the data collection unit can also prioritize collecting data that is causing excitement. In this way, important data is prioritized by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input user facial expression data into a generating AI, which can then estimate emotions and determine the priority of the data to be collected.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting life logs. For example, the data collection unit can analyze the user's geographical location information using GPS data analysis technology. The data collection unit can also extract highly relevant data using location information filtering technology. Furthermore, the data collection unit can prioritize the collection of data based on the user's geographical location information. For example, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. If the user is on the move, the data collection unit can also prioritize the collection of data related to the travel route. If the user is at home, the data collection unit can also prioritize the collection of data related to activities at home. This allows for efficient data collection by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's location information data into a generative AI, which can analyze the location information and prioritize the collection of highly relevant data.

[0086] The data collection unit can analyze a user's social media activity and collect relevant data when collecting life logs. For example, the data collection unit can analyze a user's social media activity using post content analysis technology. The data collection unit can also evaluate a user's social media activity using engagement analysis technology. Furthermore, the data collection unit can collect relevant data based on the user's social media activity. For example, if a user posts about a specific topic on social media, the data collection unit can collect data related to that topic. If a user interacts with a specific person on social media, the data collection unit can also collect data related to that person. If a user participates in a specific event on social media, the data collection unit can also collect data related to that event. In this way, data is efficiently collected by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input the user's social media data into a generative AI, which can analyze the activity and collect relevant data.

[0087] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. The analysis unit can also estimate the user's emotions using voice tone analysis technology. Furthermore, the analysis unit can adjust the accuracy of the analysis based on the user's emotions. For example, if the user is stressed, the analysis unit can increase the accuracy of the analysis to provide more detailed information. If the user is relaxed, the analysis unit can set the accuracy of the analysis to a moderate level and provide the necessary information. If the user is excited, the analysis unit can lower the accuracy of the analysis to provide concise information. In this way, by adjusting the accuracy of the analysis based on the user's emotions, information appropriate to the user is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user facial expression data into a generating AI, which can then estimate emotions and adjust the accuracy of the analysis.

[0088] The analysis unit can analyze current data by referring to past life log data during analysis. The analysis unit can refer to past life log data using, for example, database search technology. The analysis unit can also analyze past data using time series analysis technology. Furthermore, the analysis unit can analyze current data based on past life log data. For example, the analysis unit can analyze trends in current data based on past life log data. The analysis unit can also detect anomalies in current data by referring to past life log data. The analysis unit can also make predictions about current data based on past life log data. In this way, by analyzing current data by referring to past life log data, trends and anomalies in the data can be grasped. Some or all of the above processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input past life log data into a generation AI, and the generation AI can analyze the data to grasp trends and anomalies in the current data.

[0089] The analysis unit can apply different analysis algorithms to each category of lifelog data during analysis. For example, the analysis unit can analyze behavioral data using a clustering algorithm. It can also analyze conversational data using a natural language processing algorithm. Furthermore, it can analyze location data using a geographic information analysis algorithm. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral data to analyze detailed behavioral patterns. It can also apply a natural language processing algorithm to conversational data to analyze conversational content. It can also apply a geographic information analysis algorithm to location data to analyze movement patterns. This allows for detailed analysis by applying different analysis algorithms to each category of lifelog data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input lifelog data into a generative AI, which can then analyze the data by applying different analysis algorithms to each category.

[0090] The service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, the service provider can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice tone analysis technology. Furthermore, the service provider can adjust the way the information is presented based on the user's emotions. For example, if the user is tense, the service provider can provide simple and easily visible information. If the user is relaxed, the service provider can provide detailed information. If the user is in a hurry, the service provider can provide concise information. In this way, by adjusting the way the information is presented based on the user's emotions, the service provider can provide information that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input user facial expression data into a generating AI, which can then estimate emotions and adjust how the information is presented.

[0091] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can evaluate the importance of the information using importance evaluation technology. The provider can also evaluate the urgency of the information. Furthermore, the provider can adjust the level of detail provided based on the importance of the information. For example, the provider can provide highly important information in detail to make it easier for the user to understand. The provider can also provide less important information concisely to reduce the burden on the user. The provider can also dynamically adjust the level of detail provided based on the importance of the information. This allows the provider to provide information that is appropriate for the user by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input information importance data into a generative AI, which can evaluate the importance and adjust the level of detail provided.

[0092] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the information provider can provide information using a recommendation algorithm. The information provider can also provide information using personalized filtering technology. Furthermore, the information provider can apply different information provision algorithms depending on the information category. For example, the information provider can apply a behavioral analysis algorithm to behavioral data to provide detailed behavioral patterns. The information provider can also apply a natural language processing algorithm to conversational data to provide conversational content. The information provider can also apply a geographic information analysis algorithm to location data to provide movement patterns. In this way, detailed information is provided by applying different information provision algorithms depending on the information category. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input information data into a generative AI, and the generative AI can apply different information provision algorithms for each category to provide the information.

[0093] The protection unit can estimate the user's emotions and adjust the level of privacy protection based on the estimated user emotions. The protection unit can estimate the user's emotions using, for example, facial recognition technology. The protection unit can also estimate the user's emotions using voice tone analysis technology. Furthermore, the protection unit can adjust the level of privacy protection based on the user's emotions. For example, if the user is stressed, the protection unit can increase the level of privacy protection to provide a sense of security. If the user is relaxed, the protection unit can also set the level of privacy protection to a moderate level. If the user is agitated, the protection unit can lower the level of privacy protection to prioritize the provision of information. In this way, by adjusting the level of privacy protection based on the user's emotions, a sense of security is provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the protection unit may be performed using, for example, generative AI, or without generative AI. For example, the protection unit can input the user's facial expression data into a generating AI, which can then estimate emotions and adjust the level of privacy protection.

[0094] The protection unit can select the optimal protection method by referring to past data protection history when protecting privacy. The protection unit can refer to past data protection history, for example, using log data analysis technology. The protection unit can also search past data protection history using history database search technology. Furthermore, the protection unit can select the optimal protection method based on past data protection history. For example, the protection unit can select the optimal protection method based on privacy protection methods used in the past. The protection unit can also select a protection method that suits the user's preferences by referring to past data protection history. The protection unit can also select the most suitable protection method for the current situation based on past data protection history. This makes it possible to provide privacy protection that suits the user's preferences by selecting the optimal protection method by referring to past data protection history. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the protection unit can input past data protection history into a generative AI, which can analyze the history and select the optimal protection method.

[0095] The protection unit can apply different protection methods to each data category when protecting privacy. For example, the protection unit can protect data using data encryption methods. It can also protect data using access control methods. Furthermore, the protection unit can apply different protection methods to each data category. For example, the protection unit can apply behavioral analysis algorithms to behavioral data to protect detailed behavioral patterns. The protection unit can also apply natural language processing algorithms to conversational data to protect conversation content. The protection unit can also apply geographic information analysis algorithms to location data to protect movement patterns. This enables detailed privacy protection by applying different protection methods to each data category. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the protection unit can input data into a generative AI, which can then protect the data by applying different protection methods to each category.

[0096] The protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated user emotions. The protection unit can estimate the user's emotions using, for example, facial recognition technology. The protection unit can also estimate the user's emotions using voice tone analysis technology. Furthermore, the protection unit can determine the priority of privacy protection based on the user's emotions. For example, if the user is stressed, the protection unit can increase the priority of privacy protection. If the user is relaxed, the protection unit can set the priority of privacy protection to a medium level. If the user is excited, the protection unit can also decrease the priority of privacy protection. This provides the user with a sense of security by determining the priority of privacy protection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the protection unit may be performed using, for example, generative AI, or without generative AI. For example, the protection unit can input user facial expression data into a generating AI, which can then estimate emotions and determine the priority of privacy protection.

[0097] The protection unit can determine the priority of protection based on when the data was collected when protecting privacy. For example, the protection unit can protect data using priority protection techniques for the most recent data. It can also protect data using archiving techniques for older data. Furthermore, the protection unit can determine the priority of protection based on when the data was collected. For example, the protection unit can prioritize the protection of recently collected data to protect the most up-to-date information. The protection unit can also prioritize the protection of data collected during a specific period to protect information from that period. The protection unit can also prioritize the protection of historical data to protect long-term information. In this way, by determining the priority of protection based on when the data was collected, the most up-to-date information is protected first. Some or all of the above processing in the protection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the protection unit can input data collection timing data into a generative AI, which can analyze the collection timing and determine the priority of protection.

[0098] The protection unit can adjust the order of protection based on data relevance when protecting privacy. The protection unit can evaluate data relevance using, for example, data correlation analysis techniques. It can also evaluate data relevance using context analysis techniques. Furthermore, the protection unit can adjust the order of protection based on data relevance. For example, the protection unit can prioritize the protection of highly relevant data to protect important information. The protection unit can also efficiently protect less relevant data by delaying its protection. The protection unit can also dynamically adjust the order of protection based on data relevance. This allows for the priority protection of important information by adjusting the order of protection based on data relevance. Some or all of the above processing in the protection unit may be performed using, for example, generative AI, or without generative AI. For example, the protection unit can input data relevance data into a generative AI, which can analyze the relevance and adjust the order of protection.

[0099] The acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated user emotions. The acquisition unit can estimate the user's emotions using, for example, facial recognition technology. The acquisition unit can also estimate the user's emotions using voice tone analysis technology. Furthermore, the acquisition unit can determine the priority of information to acquire based on the user's emotions. For example, if the user is stressed, the acquisition unit will prioritize acquiring information that is causing the stress. If the user is relaxed, the acquisition unit can also prioritize acquiring information that is contributing to relaxation. If the user is excited, the acquisition unit can also prioritize acquiring information that is causing excitement. In this way, by determining the priority of information to acquire based on the user's emotions, important information is acquired preferentially. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the acquisition unit can input the user's facial expression data into a generating AI, which can then estimate emotions and determine the priority of the information to be acquired.

[0100] The acquisition unit can select the optimal acquisition method by referring to past acquisition history at the time of acquisition. The acquisition unit can refer to past acquisition history using, for example, log data analysis technology. The acquisition unit can also search past acquisition history using history database search technology. Furthermore, the acquisition unit can select the optimal acquisition method based on past acquisition history. For example, the acquisition unit can select the optimal acquisition method based on acquisition methods used in the past. The acquisition unit can also select an acquisition method that suits the user's preferences by referring to past acquisition history. The acquisition unit can also select the acquisition method that is best suited to the current situation based on past acquisition history. This makes it possible to acquire information that suits the user's preferences by selecting the optimal acquisition method by referring to past acquisition history. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input past acquisition history into a generation AI, which can analyze the history and select the optimal acquisition method.

[0101] The acquisition unit can apply different acquisition methods to each category of information during acquisition. For example, the acquisition unit can acquire information using data mining techniques. It can also acquire information using information retrieval techniques. Furthermore, the acquisition unit can apply different acquisition methods to each category of information. For example, the acquisition unit can apply behavioral analysis algorithms to behavioral data to acquire detailed behavioral patterns. The acquisition unit can also apply natural language processing algorithms to conversational data to acquire conversational content. The acquisition unit can also apply geographic information analysis algorithms to location data to acquire movement patterns. This makes it possible to acquire detailed information by applying different acquisition methods to each category of information. Some or all of the above processing in the acquisition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the acquisition unit can input information data into a generative AI, and the generative AI can acquire information by applying different acquisition methods to each category.

[0102] The acquisition unit can estimate the user's emotions and adjust the display method of the acquired information based on the estimated user emotions. The acquisition unit can estimate the user's emotions using, for example, facial expression recognition technology. The acquisition unit can also estimate the user's emotions using voice tone analysis technology. Furthermore, the acquisition unit can adjust the display method of the acquired information based on the user's emotions. For example, if the user is tense, the acquisition unit can provide a simple and highly visible display method. If the user is relaxed, the acquisition unit can also provide a display method that includes detailed information. If the user is in a hurry, the acquisition unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the acquired information based on the user's emotions, information suitable for the user is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the acquisition unit can input the user's facial expression data into a generating AI, which can then estimate the emotion and adjust how the information is displayed.

[0103] The acquisition unit can determine the acquisition priority based on the information collection timing at the time of acquisition. The acquisition unit can acquire information using, for example, a technique for prioritizing the acquisition of the latest information. The acquisition unit can also acquire information using an archiving technique for older information. Furthermore, the acquisition unit can also determine the acquisition priority based on the information collection timing. For example, the acquisition unit can prioritize the acquisition of recently collected information and provide the latest information to the user. The acquisition unit can also prioritize the acquisition of information collected during a specific period and provide the user with information from that period. The acquisition unit can also prioritize the acquisition of historical information and provide the user with long-term information. In this way, by determining the acquisition priority based on the information collection timing, the latest information is acquired preferentially. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without a generating AI. For example, the acquisition unit can input information collection timing data into a generating AI, which can analyze the collection timing and determine the acquisition priority.

[0104] The data acquisition unit can adjust the acquisition order based on the relevance of the information during acquisition. The data acquisition unit can evaluate the relevance of information using, for example, information correlation analysis techniques. The data acquisition unit can also evaluate the relevance of information using context analysis techniques. Furthermore, the data acquisition unit can adjust the acquisition order based on the relevance of the information. For example, the data acquisition unit can prioritize the acquisition of highly relevant information and provide important information to the user. The data acquisition unit can also efficiently acquire information by delaying the acquisition of less relevant information. The data acquisition unit can also dynamically adjust the acquisition order based on the relevance of the information. This allows for the priority acquisition of important information by adjusting the acquisition order based on the relevance of the information. Some or all of the above processing in the data acquisition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data acquisition unit can input information relevance data into a generative AI, which can analyze the relevance and adjust the acquisition order.

[0105] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, the recording unit can estimate the user's emotions using facial recognition technology. The recording unit can also estimate the user's emotions using voice tone analysis technology. Furthermore, the recording unit can adjust the recording method based on the user's emotions. For example, if the user is stressed, the recording unit can provide a simple recording method to reduce the user's burden. If the user is relaxed, the recording unit can provide a detailed recording method to meet the user's needs. If the user is excited, the recording unit can provide a visually stimulating recording method. This allows for user-friendly recording by adjusting the recording method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, generative AI, or without generative AI. For example, the recording unit can input the user's facial expression data into a generating AI, which can then estimate emotions and adjust the recording method.

[0106] The recording unit can select the optimal recording method by referring to past recording history when recording. The recording unit can refer to past recording history using, for example, log data analysis technology. The recording unit can also search past recording history using history database search technology. Furthermore, the recording unit can select the optimal recording method based on past recording history. For example, the recording unit can select the optimal recording method based on recording methods used in the past. The recording unit can also select a recording method that suits the user's preferences by referring to past recording history. The recording unit can also select the optimal recording method for the current situation based on past recording history. This makes it possible to record in a way that suits the user's preferences by selecting the optimal recording method by referring to past recording history. Some or all of the above processing in the recording unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recording unit can input past recording history into a generation AI, which can analyze the history and select the optimal recording method.

[0107] The recording unit can estimate the user's emotions and determine recording priorities based on the estimated emotions. For example, the recording unit may use facial recognition technology to estimate the user's emotions. It can also use voice tone analysis technology to estimate the user's emotions. Furthermore, the recording unit can determine recording priorities based on the user's emotions. For example, if the user is stressed, the recording unit may prioritize recording important information. If the user is relaxed, the recording unit may prioritize recording detailed information. If the user is excited, the recording unit may prioritize recording visually stimulating information. This ensures that important information is prioritized by determining recording priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, generative AI, or without using generative AI. For example, the recording unit can input user facial expression data into a generating AI, which can then estimate emotions and determine the priority of recording.

[0108] The recording unit can determine the priority of recording based on the timing of information collection. For example, the recording unit can record information using a technique that prioritizes the most recent information. The recording unit can also record information using an archiving technique for older information. Furthermore, the recording unit can determine the priority of recording based on the timing of information collection. For example, the recording unit can prioritize recording recently collected information and save the latest information. The recording unit can also prioritize recording information collected during a specific period and save the information for that period. The recording unit can also prioritize recording historical information and save long-term information. In this way, by determining the priority of recording based on the timing of information collection, the latest information is recorded first. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input information collection timing data into a generative AI, which can analyze the collection timing and determine the priority of recording.

[0109] The management unit can estimate the user's emotions and adjust the schedule management method based on the estimated emotions. The management unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice tone analysis technology. Furthermore, the management unit can adjust the schedule management method based on the user's emotions. For example, if the user is stressed, the management unit can provide a simple schedule management method to reduce the user's burden. If the user is relaxed, the management unit can provide a detailed schedule management method to meet the user's needs. If the user is excited, the management unit can provide a visually stimulating schedule management method. In this way, by adjusting the schedule management method based on the user's emotions, schedule management that is suitable for the user becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using, for example, generative AI, or without generative AI. For example, the management department can input user facial expression data into a generating AI, which can then estimate emotions and adjust the schedule management method accordingly.

[0110] The management department can select the optimal management method by referring to past schedule history when managing schedules. For example, the management department can refer to past schedule history using log data analysis technology. The management department can also search past schedule history using history database search technology. Furthermore, the management department can select the optimal management method based on past schedule history. For example, the management department can select the optimal management method based on schedule management methods used in the past. The management department can also select a management method that suits the user's preferences by referring to past schedule history. The management department can also select the management method that is best suited to the current situation based on past schedule history. This makes it possible to manage schedules that suit the user's preferences by selecting the optimal management method by referring to past schedule history. Some or all of the above processes in the management department may be performed using, for example, a generation AI, or without a generation AI. For example, the management department can input past schedule history into a generation AI, which can analyze the history and select the optimal management method.

[0111] The management unit can estimate the user's emotions and determine schedule management priorities based on the estimated emotions. The management unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice tone analysis technology. Furthermore, the management unit can determine schedule management priorities based on the user's emotions. For example, if the user is stressed, the management unit can prioritize important schedules. If the user is relaxed, the management unit can also prioritize detailed schedules. If the user is excited, the management unit can also prioritize visually stimulating schedules. In this way, important schedules are prioritized by determining schedule management priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input user facial expression data into a generating AI, which can then estimate emotions and determine the priority of schedule management.

[0112] The management department can determine management priorities based on the timing of information collection when managing schedules. For example, the management department can manage information using techniques for prioritizing the latest information. It can also manage information using archiving techniques for older information. Furthermore, the management department can determine management priorities based on the timing of information collection. For example, the management department can prioritize the management of recently collected information and provide the latest schedule. The management department can also prioritize the management of information collected during a specific period and provide the schedule for that period. The management department can also prioritize the management of historical information and provide long-term schedules. In this way, by determining management priorities based on the timing of information collection, the latest schedule is prioritized. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input information collection timing data into a generative AI, which can analyze the collection timing and determine management priorities.

[0113] The verification unit can estimate the user's emotions and adjust the verification method based on the estimated user emotions. For example, the verification unit can estimate the user's emotions using facial recognition technology. The verification unit can also estimate the user's emotions using voice tone analysis technology. Furthermore, the verification unit can adjust the verification method based on the user's emotions. For example, if the user is nervous, the verification unit can provide a simple and highly visible verification method. If the user is relaxed, the verification unit can also provide a verification method that includes detailed information. If the user is in a hurry, the verification unit can also provide a concise verification method. This allows for verification that is appropriate for the user by adjusting the verification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the verification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the verification unit can input the user's facial expression data into a generating AI, which can then estimate the emotion and adjust the verification method.

[0114] The verification unit can select the optimal verification method by referring to past verification history during verification. The verification unit can refer to past verification history using, for example, log data analysis technology. The verification unit can also search past verification history using history database search technology. Furthermore, the verification unit can select the optimal verification method based on past verification history. For example, the verification unit can select the optimal verification method based on verification methods used in the past. The verification unit can also select a verification method that suits the user's preferences by referring to past verification history. The verification unit can also select the verification method that is best suited to the current situation based on past verification history. This makes it possible to perform verification that suits the user's preferences by selecting the optimal verification method by referring to past verification history. Some or all of the above processing in the verification unit may be performed using, for example, a generation AI, or without a generation AI. For example, the verification unit can input past verification history into a generation AI, which can analyze the history and select the optimal verification method.

[0115] The verification unit can estimate the user's emotions and determine the priority of verification based on the estimated user emotions. The verification unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice tone analysis technology. Furthermore, the verification unit can determine the priority of verification based on the user's emotions. For example, if the user is tense, the verification unit will prioritize checking important information. If the user is relaxed, the verification unit may also prioritize checking detailed information. If the user is in a hurry, the verification unit may also prioritize checking concise information. In this way, by determining the priority of verification based on the user's emotions, important information is prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the verification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the verification unit can input the user's facial expression data into a generating AI, which can then estimate the emotion and determine the priority of the verification process.

[0116] The verification unit can determine the priority of verification based on the information collection period during verification. For example, the verification unit can verify information using a technique that prioritizes the verification of the latest information. The verification unit can also verify information using an archiving technique for older information. Furthermore, the verification unit can also determine the priority of verification based on the information collection period. For example, the verification unit can prioritize the verification of recently collected information and provide the latest information. The verification unit can also prioritize the verification of information collected during a specific period and provide information for that period. The verification unit can also prioritize the verification of past information and provide long-term information. In this way, by determining the priority of verification based on the information collection period, the latest information is prioritized. Some or all of the above processing in the verification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the verification unit can input information collection period data into a generative AI, which can analyze the collection period and determine the priority of verification.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The memory recall AI glasses system can also be equipped with a health management unit that monitors the user's health status. This unit can measure vital signs such as heart rate, blood pressure, and body temperature using sensors and monitor them in real time. Furthermore, the health management unit can issue alerts based on the user's health status. For example, if the heart rate is abnormally high, it can display an alert prompting the user to rest. In addition, the health management unit can analyze the user's health data to understand health trends. This enables real-time monitoring of the user's health status and the provision of appropriate advice.

[0119] The memory recall AI glasses system can also include a music recommendation unit that estimates the user's emotions and recommends music based on those emotions. The music recommendation unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice tone analysis technology. Furthermore, the music recommendation unit can recommend appropriate music based on the user's emotions. For example, if the user is feeling stressed, it can recommend relaxing music. If the user is relaxed, it can recommend even more relaxing music. This makes it possible to improve the user's mood by recommending appropriate music based on their emotions.

[0120] The memory recall AI glasses system can also be equipped with a meal management unit that manages the user's meal records. For example, the meal management unit can take photos of the meals the user has eaten and analyze the meal content using image recognition technology. It can also record the user's meal history and evaluate nutritional balance. Furthermore, the meal management unit can provide dietary advice based on the user's health condition. For example, if the user is nutritionally deficient, it can suggest a nutritionally balanced meal. This makes it possible to manage the user's meal records and support a healthy diet.

[0121] The memory recall AI glasses system can also be equipped with an exercise management unit that manages the user's exercise records. For example, the exercise management unit can detect the user's exercise using sensors and record it as an exercise log. It can also analyze the user's exercise history and identify exercise patterns. Furthermore, the exercise management unit can provide exercise advice based on the user's health status. For example, if the user is not getting enough exercise, it can suggest an appropriate exercise plan. This makes it possible to manage the user's exercise records and support a healthy lifestyle.

[0122] The memory recall AI glasses system can also be equipped with a sleep management unit that monitors the user's sleep state. For example, the sleep management unit can detect the user's movements during sleep using sensors and record them as a sleep log. It can also analyze the user's sleep history and identify sleep patterns. Furthermore, the sleep management unit can provide sleep advice based on the user's health condition. For instance, if the user is sleep-deprived, it can suggest an appropriate sleep plan. This allows for monitoring the user's sleep state and supporting healthy sleep.

[0123] The memory recall AI glasses system can further include a relaxation suggestion unit that estimates the user's emotions and suggests relaxation methods based on those emotions. The relaxation suggestion unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice tone analysis technology. Furthermore, the relaxation suggestion unit can suggest appropriate relaxation methods based on the user's emotions. For example, if the user is feeling stressed, it can suggest deep breathing or meditation. If the user is relaxed, it can suggest ways to further relax. This makes it possible to reduce the user's stress by suggesting appropriate relaxation methods based on their emotions.

[0124] The memory recall AI glasses system can further include a communication suggestion unit that estimates the user's emotions and proposes communication methods based on those estimated emotions. The communication suggestion unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice tone analysis technology. Furthermore, the communication suggestion unit can propose appropriate communication methods based on the user's emotions. For example, if the user is tense, it can suggest a relaxing communication method. If the user is relaxed, it can suggest a more proactive communication method. This makes it possible to improve the user's communication skills by suggesting appropriate communication methods based on their emotions.

[0125] The memory recall AI glasses system can further include a learning suggestion unit that estimates the user's emotions and proposes learning methods based on those emotions. The learning suggestion unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice tone analysis technology. Furthermore, the learning suggestion unit can propose appropriate learning methods based on the user's emotions. For example, if the user is feeling stressed, it can suggest learning methods that promote relaxation. If the user is relaxed, it can suggest learning methods that enhance concentration. This makes it possible to improve the user's learning efficiency by suggesting appropriate learning methods based on the user's emotions.

[0126] The memory recall AI glasses system can further include an exercise suggestion unit that estimates the user's emotions and proposes exercise methods based on those emotions. The exercise suggestion unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice tone analysis technology. Furthermore, the exercise suggestion unit can propose appropriate exercise methods based on the user's emotions. For example, if the user is feeling stressed, it can suggest relaxing exercise methods. If the user is relaxed, it can suggest even more relaxing exercise methods. This makes it possible to reduce the user's stress by suggesting appropriate exercise methods based on their emotions.

[0127] The memory recall AI glasses system can further include a travel suggestion unit that estimates the user's emotions and proposes a travel plan based on those emotions. The travel suggestion unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice tone analysis technology. Furthermore, the travel suggestion unit can propose an appropriate travel plan based on the user's emotions. For example, if the user is feeling stressed, it can propose a relaxing travel plan. If the user is relaxed, it can propose an even more relaxing travel plan. This makes it possible to reduce the user's stress by proposing an appropriate travel plan based on their emotions.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The data collection unit collects the user's life log. The life log includes activity logs, conversation logs, location logs, etc. The data collection unit detects the user's actions with sensors and records them as activity logs. It can also record the user's conversations with a microphone and record them as conversation logs. Furthermore, it can acquire the user's location information using GPS and record it as a location log. For example, the data collection unit tracks the user's movement route in real time and saves it as a location log. Step 2: The analysis unit analyzes the life logs collected by the collection unit. The analysis unit analyzes the collected behavioral logs to identify the user's behavioral patterns. It can also analyze the collected conversation logs and summarize the conversation content. Furthermore, it can analyze the collected location information logs to identify the user's movement patterns. For example, the analysis unit analyzes the user's behavioral patterns using a clustering algorithm and extracts specific behavioral patterns. Step 3: The service provider visually presents the results analyzed by the analysis unit. The service provider displays the analysis results as graphs and charts. It can also display the analysis results as a dashboard. Furthermore, it can notify the user of the analysis results. For example, the service provider displays the user's behavior patterns as graphs, providing them in a visually easy-to-understand format. Step 4: The protection unit protects the privacy of the data collected by the collection unit. The protection unit encrypts the collected data. It can also control access to the collected data. Furthermore, it can anonymize the collected data. For example, the protection unit encrypts the collected data using the AES encryption algorithm to ensure data security. Step 5: The retrieval unit retrieves specific information from the user. The retrieval unit searches for past conversation content from the user. The user can also review their past activity history. Furthermore, the user can review their past location information. For example, the retrieval unit searches for conversation content from a specific date and time and displays it immediately.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, protection unit, acquisition unit, recording unit, management unit, and confirmation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's life log using the sensors and microphone of the smart device 14. The analysis unit analyzes the collected life log using the identification processing unit 290 of the data processing unit 12. The provision unit visually provides the analysis results using the display 40A of the smart device 14. The protection unit encrypts the data collected by the identification processing unit 290 of the data processing unit 12 to protect privacy. The acquisition unit uses the control unit 46A of the smart device 14 to obtain specific information from the user. The recording unit automatically records and summarizes the conversation content using the identification processing unit 290 of the data processing unit 12. The management unit manages the schedule using the calendar function of the smart device 14. The confirmation unit immediately checks past conversation history using the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, protection unit, acquisition unit, recording unit, management unit, and confirmation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's life log using the sensors and microphone of the smart glasses 214. The analysis unit analyzes the collected life log by the identification processing unit 290 of the data processing unit 12. The provision unit visually provides the analysis results using the display of the smart glasses 214. The protection unit encrypts the data collected by the identification processing unit 290 of the data processing unit 12 to protect privacy. The acquisition unit uses the control unit 46A of the smart glasses 214 to obtain specific information from the user. The recording unit automatically records and summarizes the conversation content by the identification processing unit 290 of the data processing unit 12. The management unit manages the schedule using the calendar function of the smart glasses 214. The confirmation unit immediately checks past conversation history by the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, protection unit, acquisition unit, recording unit, management unit, and confirmation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's life log using the sensors and microphone of the headset terminal 314. The analysis unit analyzes the collected life log using the identification processing unit 290 of the data processing unit 12. The provision unit visually provides the analysis results using the display 343 of the headset terminal 314. The protection unit encrypts the data collected by the identification processing unit 290 of the data processing unit 12 to protect privacy. The acquisition unit uses the control unit 46A of the headset terminal 314 to obtain specific information from the user. The recording unit automatically records and summarizes the conversation content using the identification processing unit 290 of the data processing unit 12. The management unit manages the schedule using the calendar function of the headset terminal 314. The confirmation unit immediately checks past conversation history using the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, protection unit, acquisition unit, recording unit, management unit, and verification unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's life log using the robot 414's sensors and microphone. The analysis unit analyzes the collected life log using the identification processing unit 290 of the data processing unit 12. The provision unit visually provides the analysis results using the robot 414's display. The protection unit encrypts the data collected by the identification processing unit 290 of the data processing unit 12 to protect privacy. The acquisition unit uses the robot 414's control unit 46A to obtain specific information from the user. The recording unit automatically records and summarizes the conversation content using the identification processing unit 290 of the data processing unit 12. The management unit manages the schedule using the robot 414's calendar function. The verification unit immediately checks past conversation history using the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0183] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] 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.

[0193] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) The collection department collects life logs, An analysis unit analyzes the life logs collected by the aforementioned collection unit, A providing unit that visually provides the results of the analysis performed by the aforementioned analysis unit, A protection unit for protecting the privacy of the data collected by the aforementioned collection unit, It includes an acquisition unit that allows the user to obtain specific information. A system characterized by the following features. (Note 2) It is equipped with a recording unit that automatically records and summarizes conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The company has a management department that handles schedule management. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a verification unit that allows for immediate confirmation of past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Instantly collects user behavior, conversations, and location information—a life log. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, By analyzing collected life logs, past scenes and conversations can be identified. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of life log collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past behavior patterns to select the most appropriate timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting life logs, filtering is performed based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting life logs, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting life logs, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, past life log data is referenced to analyze the current data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each category of lifelog data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned protective part is It estimates the user's emotions and adjusts the level of privacy protection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned protective part is When protecting privacy, the most appropriate protection method is selected by referring to past data protection history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned protective part is When protecting privacy, different protection methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned protective part is It estimates user sentiment and determines privacy protection priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned protective part is When protecting privacy, we prioritize protection based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned protective part is When protecting privacy, the order of protection is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The acquisition unit is, When acquiring data, the optimal acquisition method is selected by referring to past acquisition history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The acquisition unit is, When acquiring information, different acquisition methods are applied for each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The acquisition unit is, It estimates the user's emotions and adjusts how the information obtained is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The acquisition unit is, When acquiring data, prioritize acquisition based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The acquisition unit is, When acquiring data, adjust the acquisition order based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned recording unit is The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned recording unit is When recording, the system selects the optimal recording method by referring to past recording history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned recording unit is The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned recording unit is When recording, prioritize the recordings based on when the information was collected. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned management department, It estimates the user's emotions and adjusts the schedule management method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned management department, When managing schedules, refer to past schedule history to select the most suitable management method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned management department, It estimates the user's emotions and determines schedule management priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned management department, When managing a schedule, prioritize tasks based on when the information was collected. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned verification unit is We estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned verification unit is During verification, the system will refer to past verification history to select the most suitable verification method. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of confirmations based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned verification unit is During the verification process, prioritize the verification based on when the information was collected. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects life logs, An analysis unit analyzes the life logs collected by the aforementioned collection unit, A providing unit that visually provides the results of the analysis performed by the aforementioned analysis unit, A protection unit for protecting the privacy of the data collected by the aforementioned collection unit, It includes an acquisition unit that allows the user to obtain specific information. A system characterized by the following features.

2. It is equipped with a recording unit that automatically records and summarizes conversation content. The system according to feature 1.

3. The company has a management department that handles schedule management. The system according to feature 1.

4. It is equipped with a verification unit that allows for immediate confirmation of past conversation history. The system according to feature 1.

5. The aforementioned collection unit is It instantly collects life logs of user behavior, conversations, and location information. The system according to feature 1.

6. The aforementioned analysis unit, By analyzing collected life logs, past scenes and conversations can be identified. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of life log collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past behavior patterns to select the most appropriate timing for data collection. The system according to feature 1.

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