System

Abstract

To provide a system according to an embodiment that complements a memory of a user.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; and a complementary unit. The collection unit is configured to collect data on a user. The analysis unit is configured to analyze the data collected by the collection unit. The complementary unit is configured to complement a memory of the user on the basis of an analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately collect and analyze data to complement a user's memory, and there is room for improvement.

[0005] The system according to the embodiment aims to complement the memory of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a complementation unit. The collection unit collects data related to a user. The analysis unit analyzes the data collected by the collection unit. The complementation unit complements the user's memory based on the analysis result obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can supplement the memory of the user. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The memory complement system according to the embodiment of the present invention is a system for complementing a user's memory. This memory complement system is an attempt to raise a digital clone that complements the user's memory by collecting data using a wearable device such as a smartwatch or smartphone and analyzing it with AI on the cloud. For example, the memory complement system collects data about the user. For example, the memory complement system collects data using sensors in a smartwatch or smartphone. For example, the memory complement system can collect the user's behavior history, emotional state, location information, and the like. Next, the memory complement system analyzes the collected data with AI on the cloud. For example, the memory complement system analyzes the collected data and learns the user's behavior patterns and memory tendencies. For example, the user's behavior patterns can be grasped by analyzing places that the user frequently visits and actions that he or she frequently performs. Furthermore, the memory complement system complements the user's memory based on the analysis results. For example, the memory complement system provides information when the user asks a question. For example, if the user asks, "What did you eat for dinner yesterday?", the memory complement system can provide information such as "I ate pasta for dinner yesterday" based on the analysis results. In addition, the memory complement system has a protection function for encrypting data and protecting privacy. For example, the memory complement system encrypts collected data to protect the user's privacy. For example, by encrypting data such as the user's behavior history and emotional state, unauthorized access by a third party can be prevented. Furthermore, the memory complement system has a function of predicting the user's behavior pattern. For example, the memory complement system predicts the user's behavior pattern based on the analysis result and predicts the user's next action. For example, if the user has the habit of jogging at 7 o'clock every morning, the memory complement system can predict, for example, "the next jogging will be tomorrow at 7 o'clock." In addition, the memory complement system estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is feeling stressed, the memory complement system can reduce the frequency of data collection to reduce the burden on the user. Furthermore, the memory complement system analyzes the user's past behavior history and selects a data collection method.For example, if a user has frequently visited a particular place in the past, more accurate data can be collected by prioritizing data collection at that place. In this way, the memory complement system is a system for complementing a user's memory, and attempts to raise a digital clone that complements the user's memory by collecting data using wearable devices such as smartwatches and smartphones and analyzing it with AI on the cloud. In this way, the memory complement system can complement the user's memory and support daily life.

[0029] The memory complement system according to the embodiment includes a collection unit, an analysis unit, and a complement unit. The collection unit collects data related to the user. The data related to the user includes, for example, behavioral data, location information, and emotion data, but is not limited to such examples. The collection unit collects data by utilizing, for example, a sensor of a smartwatch or a smartphone. For example, the collection unit collects the user's exercise data using an acceleration sensor of the smartwatch. The collection unit can also collect the user's location information using a location information sensor of the smartphone. Furthermore, the collection unit can collect the user's heartbeat data using a heartbeat sensor of the smartwatch. For example, the collection unit counts the number of steps of the user using the acceleration sensor of the smartwatch to grasp the amount of exercise. The collection unit can also record the user's movement route using the location information sensor of the smartphone to grasp the behavior pattern. Furthermore, the collection unit can monitor the user's heart rate using the heartbeat sensor of the smartwatch to grasp the health condition. The analysis unit analyzes the data collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, machine learning algorithms, and the like, but is not limited to such examples. For example, the analysis unit analyzes the user's behavioral patterns using data mining technology. The analysis unit can also analyze the user's behavioral data using statistical analysis technology. Furthermore, the analysis unit can learn the user's memory tendency using a machine learning algorithm. For example, the analysis unit extracts the user's behavioral patterns using data mining technology and performs frequency analysis. The analysis unit can also analyze the user's behavioral data in time series using statistical analysis technology to grasp trends. Furthermore, the analysis unit can model the user's memory tendency using a machine learning algorithm and make predictions. The complementing unit complements the user's memory based on the analysis results obtained by the analysis unit. The complementing is performed based on criteria such as, for example, the method of presenting information, timing, and type of content, but is not limited to such examples. For example, the complementing unit provides information when the user asks a question. The complementing unit can also provide information at an appropriate timing based on the user's behavioral patterns.Furthermore, the complementing unit can provide information in the form of text, voice, images, etc., to complement the user's memory. For example, when the user asks "What did you eat for dinner yesterday?", the complementing unit provides information such as "I had pasta for dinner yesterday" based on the analysis result. The complementing unit can also provide information at an appropriate time based on the places the user frequently visits and the actions he or she frequently performs. Furthermore, the complementing unit can provide information in the form of text messages, voice messages, images, etc., to complement the user's memory. In this way, the memory complementing system according to the embodiment can complement the user's memory and support daily life.

[0030] The collection unit collects data on the user. The data on the user includes, for example, behavioral data, location information, and emotion data, but is not limited to such examples. The collection unit collects data, for example, by utilizing a sensor of a smartwatch or a smartphone. Specifically, the collection unit collects the user's exercise data using an acceleration sensor of the smartwatch. The acceleration sensor can detect changes in the user's movement and posture and measure the number of steps and the amount of exercise. This makes it possible to grasp the user's daily activity level. The collection unit can also collect the user's location information using a location information sensor of the smartphone. The location information sensor accurately records the user's current location and movement route using technologies such as GPS, Wi-Fi, and Bluetooth (registered trademark). This makes it possible to grasp the user's behavioral patterns and visited places. Furthermore, the collection unit can also collect the user's heart rate data using a heart rate sensor of the smartwatch. The heart rate sensor can monitor the user's heart rate and heart rate variability to evaluate the stress level and health condition. For example, the collection unit counts the user's steps using the acceleration sensor of the smartwatch to grasp the amount of exercise. The collection unit can also use the smartphone's location information sensor to record the user's movement route and understand the user's behavioral pattern. Furthermore, the collection unit can also monitor the user's heart rate using the smartwatch's heart rate sensor to understand the user's health condition. This allows the collection unit to collect various data about the user and understand detailed information about the user's daily life. The collected data is transmitted to a cloud server so that the analysis unit and completion unit can access it. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, machine learning algorithms, and the like, but is not limited to such examples. Specifically, the analysis unit analyzes the user's behavioral patterns using data mining technology. Data mining technology is a method for extracting useful information from a large amount of data and discovering patterns and trends. This makes it possible to grasp the user's behavioral patterns and habits. The analysis unit can also analyze the user's behavioral data using statistical analysis technology. Statistical analysis technology is a method for clarifying the distribution and correlation of data and understanding the characteristics of data. This makes it possible to analyze the user's behavioral data in a time series and grasp trends. Furthermore, the analysis unit can also learn the user's memory tendency using a machine learning algorithm. Machine learning algorithms are a method for learning from data and making predictions and classifications. This makes it possible to model the user's memory tendency and use it to supplement future behavior and memories. For example, the analysis unit uses data mining technology to extract the user's behavioral patterns and perform frequency analysis. The analysis unit can also use statistical analysis techniques to analyze the user's behavioral data over time to understand trends. Furthermore, the analysis unit can use machine learning algorithms to model the user's memory tendencies and make predictions. This allows the analysis unit to analyze the collected data from multiple angles and understand the user's behavior and memory tendencies. The analysis results are provided to the completion unit and serve as the basis for completing the user's memory.

[0032] The complementing unit complements the user's memory based on the analysis result obtained by the analysis unit. The complementing is performed based on criteria such as, for example, the method of presenting information, timing, and type of content, but is not limited to such examples. Specifically, the complementing unit provides information when the user asks a question. For example, when the user asks "What did you eat for dinner yesterday?", information such as "I had pasta for dinner yesterday" is provided based on the analysis result. The complementing unit can also provide information at an appropriate time based on the user's behavior pattern. For example, information can be provided at an appropriate time based on the user's frequently visited places and frequently performed actions. Furthermore, the complementing unit can also provide information in the form of text, voice, image, etc. to complement the user's memory. For example, when the user asks "What did you eat for dinner yesterday?", information such as "I had pasta for dinner yesterday" is provided based on the analysis result. Furthermore, the complementing unit can also provide information at an appropriate time based on the user's frequently visited places and frequently performed actions. Furthermore, the complementing unit can also provide information in the form of text message, voice message, image, etc. to complement the user's memory. For example, when a user asks "What did you eat for dinner last night?", information such as "I had pasta for dinner last night" is provided based on the analysis result. The complementing unit can also provide information at an appropriate time based on the places the user frequently visits and the actions the user frequently performs. Furthermore, the complementing unit can provide information in the form of a text message, a voice message, an image, or the like to complement the user's memory. In this way, the memory complementing system according to the embodiment can complement the user's memory and support daily life.

[0033] The collection unit can collect data by utilizing at least one of the sensors of the smartwatch or the smartphone. The collection unit, for example, uses an acceleration sensor of the smartwatch to collect the user's exercise data. For example, the collection unit counts the number of steps of the user using the acceleration sensor of the smartwatch to grasp the amount of exercise. The collection unit can also collect the user's location information using the location information sensor of the smartphone. For example, the collection unit can record the user's movement route using the location information sensor of the smartphone to grasp the behavior pattern. Furthermore, the collection unit can also collect the user's heart rate data using the heart rate sensor of the smartwatch. For example, the collection unit can monitor the user's heart rate using the heart rate sensor of the smartwatch to grasp the health condition. In this way, detailed data about the user can be collected by utilizing the sensors of the smartwatch or the smartphone. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired from the acceleration sensor of the smartwatch to the generation AI and cause the generation AI to execute analysis of the exercise data.

[0034] The analysis unit can learn behavioral patterns and memory tendencies by using AI to analyze data on the cloud. The analysis unit can, for example, analyze data on the cloud using AI to learn behavioral patterns and memory tendencies. For example, the analysis unit can analyze the user's behavioral patterns using data mining technology. The analysis unit can also analyze the user's behavioral data using statistical analysis technology. Furthermore, the analysis unit can learn the user's memory tendencies using machine learning algorithms. For example, the analysis unit can extract the user's behavioral patterns using data mining technology and perform frequency analysis. The analysis unit can also analyze the user's behavioral data in chronological order using statistical analysis technology to grasp trends. Furthermore, the analysis unit can model the user's memory tendencies using machine learning algorithms and make predictions. As a result, the behavioral patterns and memory tendencies can be efficiently learned by using AI to analyze data on the cloud. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data collected on the cloud into the generation AI and have the generation AI perform an analysis of behavioral patterns.

[0035] The complementing unit can provide information when the user asks a question. The complementing unit provides information when the user asks a question, for example. For example, when the user asks "What did you eat for dinner yesterday?", the complementing unit provides information such as "I ate pasta for dinner yesterday" based on the analysis result. The complementing unit can also provide information at an appropriate time based on the user's behavioral pattern. For example, the complementing unit can provide information at an appropriate time based on the places the user frequently visits and the actions the user frequently performs. Furthermore, the complementing unit can provide information in the form of a text message, a voice message, an image, or the like to complement the user's memory. This makes it possible to complement the memory by providing appropriate information when the user asks a question. A part or all of the above-mentioned processing in the complementing unit may be performed, for example, using AI, or may be performed without using AI. For example, the complementing unit can input the user's question to the generating AI and cause the generating AI to provide appropriate information.

[0036] The memory complement system includes at least one of a protection unit for encrypting data or protecting privacy. The protection unit encrypts data, for example. For example, the protection unit encrypts data using AES (Advanced Encryption Standard). The protection unit can also encrypt data using RSA (Rivest-Shamir-Adleman). Furthermore, the protection unit can also encrypt data using SSL / TLS (Secure Sockets Layer / Transport Layer Security). For example, the protection unit encrypts user behavior data using AES to prevent unauthorized access by a third party. The protection unit can also encrypt user emotion data using RSA to ensure data security. Furthermore, the protection unit can encrypt data communication using SSL / TLS to prevent data eavesdropping. This allows the user's data to be safely protected by encrypting data and protecting privacy. A part or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the data encryption process to the generation AI and leave the execution of encryption to the generation AI.

[0037] The memory complement system includes a prediction unit that predicts a user's behavior pattern. The prediction unit predicts, for example, the user's behavior pattern. For example, the prediction unit predicts the user's behavior pattern using a machine learning model. The prediction unit can also predict the user's behavior pattern using a prediction algorithm. Furthermore, the prediction unit can predict the user's next behavior based on past behavior data. For example, the prediction unit learns the user's behavior pattern using a machine learning model and predicts the user's next behavior. The prediction unit can also analyze the user's behavior pattern using a prediction algorithm and predict the user's next behavior. Furthermore, the prediction unit can model the user's behavior pattern based on past behavior data and predict the user's next behavior. Thereby, the user's behavior pattern can be predicted to predict the user's next behavior. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit inputs the user's behavior data to the generation AI and causes the generation AI to execute the prediction of the behavior pattern.

[0038] The collection unit can analyze the user's past behavior history and trigger data collection based on a specific event or behavior. The collection unit, for example, analyzes the user's past behavior history and triggers data collection based on a specific event or behavior. For example, the collection unit collects data when the user visits a specific place in the past. The collection unit can also trigger data collection when the user acts in a specific time period. Furthermore, the collection unit can start data collection when the user performs a specific activity. For example, the collection unit collects data when the user visits a specific place in the past. The collection unit can also trigger data collection when the user acts in a specific time period. Furthermore, the collection unit can start data collection when the user performs a specific activity. As a result, by triggering data collection based on the past behavior history, more accurate data can be collected. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavior data to the generation AI and cause the generation AI to execute a trigger for data collection.

[0039] The collection unit can dynamically change the type of data to be collected according to the user's current activity status when collecting data. For example, the collection unit dynamically changes the type of data to be collected according to the user's current activity status when collecting data. For example, when the user is exercising, the collection unit prioritizes collecting data such as heart rate and number of steps. In addition, when the user is resting, the collection unit can also collect data related to a relaxed state. In addition, when the user is working, the collection unit can also collect data related to a concentration level and a stress level. For example, when the user is exercising, the collection unit prioritizes collecting data such as heart rate and number of steps. In addition, when the user is resting, the collection unit can also collect data related to a relaxed state. In addition, when the user is working, the collection unit can also collect data related to a concentration level and a stress level. In this way, by changing the type of data to be collected according to the current activity status, appropriate data can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's activity data to the generation AI and cause the generation AI to change the type of data.

[0040] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting data. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. In addition, when the user is traveling, the collection unit can also preferentially collect data related to the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect data related to the home. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. In addition, when the user is traveling, the collection unit can also preferentially collect data related to the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect data related to the home. In this way, by considering the geographical location information, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0041] The collection unit can analyze the social media activity of the user at the time of data collection and collect related data. The collection unit, for example, analyzes the social media activity of the user at the time of data collection and collects related data. For example, when the user posts about a specific topic on social media, the collection unit collects data related to the topic. In addition, when the user participates in a specific event on social media, the collection unit can also collect data related to the event. Furthermore, when the user checks in to a specific place on social media, the collection unit can also collect data related to the place. For example, when the user posts about a specific topic on social media, the collection unit collects data related to the topic. In addition, when the user participates in a specific event on social media, the collection unit can also collect data related to the event. Furthermore, when the user checks in to a specific place on social media, the collection unit can also collect data related to the place. In this way, by analyzing social media activity, related data can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media data of the user to the generation AI and cause the generation AI to execute collection of related data.

[0042] The analysis unit can detect changes in the user's behavior pattern in real time during analysis and reflect them in the analysis results. For example, the analysis unit can detect changes in the user's behavior pattern in real time during analysis and reflect them in the analysis results. For example, when the user's behavior pattern suddenly changes, the analysis unit detects the change in real time and reflects it in the analysis results. In addition, when the user's behavior pattern gradually changes, the analysis unit can also detect the change in real time and reflect it in the analysis results. In addition, when the user's behavior pattern temporarily changes, the analysis unit can detect the change in real time and reflect it in the analysis results. For example, when the user's behavior pattern suddenly changes, the analysis unit detects the change in real time and reflects it in the analysis results. In addition, when the user's behavior pattern gradually changes, the analysis unit can detect the change in real time and reflect it in the analysis results. In addition, when the user's behavior pattern temporarily changes, the analysis unit can detect the change in real time and reflect it in the analysis results. In this way, the latest analysis results can be provided by detecting changes in behavior patterns in real time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's behavioral data to the generation AI and cause the generation AI to detect changes in behavioral patterns.

[0043] The analysis unit can compare the user's past data and current data during the analysis to identify an abnormal pattern. The analysis unit can, for example, compare the user's past data and current data during the analysis to identify an abnormal pattern. For example, the analysis unit can compare the user's past data and current data to identify an abnormal behavior pattern. The analysis unit can also compare the user's past data and current data to identify an abnormal emotion pattern. The analysis unit can also compare the user's past data and current data to identify an abnormal health condition. For example, the analysis unit can compare the user's past data and current data to identify an abnormal behavior pattern. The analysis unit can also compare the user's past data and current data to identify an abnormal emotion pattern. The analysis unit can also compare the user's past data and current data to identify an abnormal health condition. In this way, by comparing the past data and the current data, an abnormal pattern can be identified. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input a user's past and current data into the generation AI and have the generation AI identify anomalous patterns.

[0044] The analysis unit can customize the analysis result by considering the geographical location information of the user at the time of analysis. The analysis unit customizes the analysis result by considering the geographical location information of the user at the time of analysis, for example. For example, when the user is in a specific location, the analysis unit provides an analysis result related to the location. In addition, when the user is traveling, the analysis unit can also provide an analysis result related to the travel destination. Furthermore, when the user is at home, the analysis unit can also provide an analysis result related to the home. For example, when the user is in a specific location, the analysis unit provides an analysis result related to the location. In addition, when the user is traveling, the analysis unit can also provide an analysis result related to the travel destination. Furthermore, when the user is at home, the analysis unit can also provide an analysis result related to the home. In this way, by considering the geographical location information, it is possible to provide an analysis result with high relevance. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute customization of the analysis result.

[0045] The analysis unit can complement the analysis result by referring to the social media activity of the user during the analysis. The analysis unit complements the analysis result by referring to the social media activity of the user during the analysis. For example, when the user posts about a specific topic on social media, the analysis unit provides an analysis result related to the topic. In addition, when the user participates in a specific event on social media, the analysis unit can provide an analysis result related to the event. Furthermore, when the user checks in to a specific place on social media, the analysis unit can provide an analysis result related to the place. For example, when the user posts about a specific topic on social media, the analysis unit provides an analysis result related to the topic. In addition, when the user participates in a specific event on social media, the analysis unit can provide an analysis result related to the event. Furthermore, when the user checks in to a specific place on social media, the analysis unit can provide an analysis result related to the place. In this way, by referring to the social media activity, it is possible to provide a related analysis result. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the social media data of the user to the generation AI and causes the generation AI to execute the complement of the analysis result.

[0046] The completion unit can provide optimal information by referring to the user's past question history at the time of completion. For example, the completion unit can provide optimal information by referring to the user's past question history at the time of completion. For example, the completion unit provides related information based on the content of the user's past questions. The completion unit can also provide frequently asked content preferentially from the user's past question history. Furthermore, the completion unit can analyze the user's past question history and provide the most appropriate information. For example, the completion unit provides related information based on the content of the user's past questions. The completion unit can also provide frequently asked content preferentially from the user's past question history. Furthermore, the completion unit can analyze the user's past question history and provide the most appropriate information. As a result, optimal information can be provided by referring to the past question history. A part or all of the above-mentioned processing in the completion unit may be performed using, for example, AI, or may be performed without using AI. For example, the completion unit can input the user's question history data to the generation AI and cause the generation AI to provide optimal information.

[0047] The completion unit can adjust the level of detail of the information to be provided according to the user's current situation at the time of completion. The completion unit adjusts the level of detail of the information to be provided according to the user's current situation at the time of completion, for example. For example, the completion unit provides concise information when the user is in a hurry. The completion unit can also provide detailed information when the user is relaxed. Furthermore, the completion unit can provide visually stimulating information when the user is excited. For example, the completion unit provides concise information when the user is in a hurry. The completion unit can also provide detailed information when the user is relaxed. Furthermore, the completion unit can provide visually stimulating information when the user is excited. This allows the level of detail of the information to be adjusted according to the current situation, thereby providing appropriate information. Part or all of the above-mentioned processing in the completion unit may be performed, for example, using AI, or may be performed without using AI. For example, the completion unit can input the user's current situation data to the generation AI and cause the generation AI to adjust the level of detail of the information.

[0048] The completion unit can provide highly relevant information in consideration of the geographical location information of the user at the time of completion. For example, the completion unit provides highly relevant information in consideration of the geographical location information of the user at the time of completion. For example, when the user is in a specific location, the completion unit provides information related to the location. In addition, when the user is traveling, the completion unit can also provide information related to the travel destination. Furthermore, when the user is at home, the completion unit can also provide information related to the home. For example, when the user is in a specific location, the completion unit provides information related to the location. In addition, when the user is traveling, the completion unit can also provide information related to the travel destination. Furthermore, when the user is at home, the completion unit can also provide information related to the home. In this way, by taking the geographical location information into consideration, highly relevant information can be provided. Part or all of the above-mentioned processing in the completion unit may be performed, for example, using AI, or may be performed without using AI. For example, the completion unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute the provision of highly relevant information.

[0049] The completion unit can analyze the social media activity of the user at the time of completion and provide related information. The completion unit can analyze the social media activity of the user at the time of completion, for example, and provide related information. For example, when the user posts about a specific topic on social media, the completion unit can provide information related to the topic. In addition, when the user participates in a specific event on social media, the completion unit can provide information related to the event. Furthermore, when the user checks in to a specific place on social media, the completion unit can provide information related to the place. For example, when the user posts about a specific topic on social media, the completion unit can provide information related to the topic. In addition, when the user participates in a specific event on social media, the completion unit can provide information related to the event. Furthermore, when the user checks in to a specific place on social media, the completion unit can provide information related to the place. In this way, by analyzing social media activity, related information can be efficiently provided. Part or all of the above-mentioned processing in the completion unit may be performed, for example, using AI, or may be performed without using AI. For example, the completion unit can input the social media data of the user to the generation AI and cause the generation AI to provide related information.

[0050] The protection unit can select an optimal protection method by referring to the user's past data access history when protecting data. For example, the protection unit selects an optimal protection method by referring to the user's past data access history when protecting data. For example, the protection unit preferentially protects data that the user frequently accessed in the past. The protection unit can also adjust the protection level for specific data from the user's past data access history. Furthermore, the protection unit can analyze the user's past data access history and select an optimal protection method. For example, the protection unit preferentially protects data that the user frequently accessed in the past. The protection unit can also adjust the protection level for specific data from the user's past data access history. Furthermore, the protection unit can analyze the user's past data access history and select an optimal protection method. As a result, optimal data protection can be provided by referring to the past data access history. A part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can input the user's data access history to the generation AI and cause the generation AI to select the optimal protection method.

[0051] The protection unit can select the optimal protection method in consideration of the geographical location information of the user when protecting data. For example, the protection unit selects the optimal protection method in consideration of the geographical location information of the user when protecting data. For example, when the user is in a specific location, the protection unit prioritizes protecting data related to the location. In addition, when the user is traveling, the protection unit can also prioritize protecting data related to the travel destination. Furthermore, when the user is at home, the protection unit can also prioritize protecting data related to the home. For example, when the user is in a specific location, the protection unit prioritizes protecting data related to the location. In addition, when the user is traveling, the protection unit can also prioritize protecting data related to the travel destination. Furthermore, when the user is at home, the protection unit can also prioritize protecting data related to the home. In this way, optimal data protection can be provided by considering the geographical location information. Part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can input the geographical location data of the user to the generation AI and cause the generation AI to select the optimal protection method.

[0052] The prediction unit can predict the next behavior by referring to the user's past behavior pattern at the time of prediction. The prediction unit, for example, predicts the next behavior by referring to the user's past behavior pattern at the time of prediction. For example, if the user has been jogging every morning in the past, the prediction unit predicts the timing of the next jogging. In addition, the prediction unit can also predict the behavior of the user if the user has visited a specific place on a specific day of the week in the past. Furthermore, the prediction unit can analyze the user's past behavior pattern and predict the next behavior. For example, if the user has been jogging every morning in the past, the prediction unit predicts the timing of the next jogging. In addition, the prediction unit can predict the behavior of the user if the user has visited a specific place on a specific day of the week in the past. Furthermore, the prediction unit can analyze the user's past behavior pattern and predict the next behavior. As a result, the next behavior can be accurately predicted by referring to the past behavior pattern. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's past behavior data to the generation AI and cause the generation AI to execute a prediction of the next behavior.

[0053] The prediction unit can customize the prediction result based on the current situation of the user at the time of prediction. The prediction unit customizes the prediction result based on the current situation of the user, for example, at the time of prediction. For example, the prediction unit predicts the next break time when the user is currently at work. Also, the prediction unit can predict the next visit to a tourist spot when the user is currently traveling. Furthermore, the prediction unit can predict the timing of the next outing when the user is currently at home. For example, the prediction unit predicts the next break time when the user is currently at work. Also, the prediction unit can predict the next visit to a tourist spot when the user is currently traveling. Furthermore, the prediction unit can predict the timing of the next outing when the user is currently at home. This allows the prediction result to be customized based on the current situation, thereby providing an appropriate prediction result. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's current situation data to the generation AI and cause the generation AI to customize the prediction result.

[0054] The prediction unit can customize the prediction result by considering the geographical location information of the user at the time of prediction. The prediction unit customizes the prediction result by considering the geographical location information of the user at the time of prediction, for example. For example, when the user is in a specific location, the prediction unit provides a prediction result related to the location. In addition, when the user is traveling, the prediction unit can also provide a prediction result related to the travel destination. In addition, when the user is at home, the prediction unit can also provide a prediction result related to the home. For example, when the user is in a specific location, the prediction unit provides a prediction result related to the location. In addition, when the user is traveling, the prediction unit can also provide a prediction result related to the travel destination. In addition, when the user is at home, the prediction unit can also provide a prediction result related to the home. In this way, by considering the geographical location information, a highly relevant prediction result can be provided. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute customization of the prediction result.

[0055] The prediction unit can complement the prediction result by referring to the social media activity of the user at the time of prediction. The prediction unit complements the prediction result by referring to the social media activity of the user at the time of prediction, for example. For example, when the user posts about a specific topic on social media, the prediction unit provides a prediction result related to the topic. In addition, when the user participates in a specific event on social media, the prediction unit can also provide a prediction result related to the event. Furthermore, when the user checks in to a specific place on social media, the prediction unit can also provide a prediction result related to the place. For example, when the user posts about a specific topic on social media, the prediction unit provides a prediction result related to the topic. In addition, when the user participates in a specific event on social media, the prediction unit can also provide a prediction result related to the event. Furthermore, when the user checks in to a specific place on social media, the prediction unit can also provide a prediction result related to the place. In this way, by referring to the social media activity, it is possible to provide a related prediction result. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's social media data to the generation AI and cause the generation AI to execute the complement of the prediction result.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The memory complement system may further include a health management unit that monitors the user's health condition. The health management unit can grasp the user's health condition by collecting the user's health data and analyzing it with the analysis unit. For example, the health management unit monitors the user's heart rate using a heart rate sensor of a smart watch and detects abnormal heart rates. The health management unit can also evaluate the user's sleep quality by collecting the user's sleep data and analyzing it with the analysis unit. Furthermore, the health management unit can evaluate the user's nutritional balance by collecting the user's dietary data and analyzing it with the analysis unit. In this way, the memory complement system can monitor the user's health condition and support health management.

[0058] The memory complement system may further include a hobby analysis unit that analyzes the hobbies and interests of the user. The hobby analysis unit may analyze the user's behavioral data and social media posts to identify the user's hobbies and interests. For example, the hobby analysis unit may analyze the places the user frequently visits and the events the user participates in to identify the user's hobbies. The hobby analysis unit may also analyze the content of the user's social media posts to identify the user's interests. Furthermore, the hobby analysis unit may analyze the user's purchasing history to identify the user's interests. This allows the memory complement system to understand the user's hobbies and interests and provide related information.

[0059] The memory complement system may further include a learning management unit that manages the user's learning progress. The learning management unit can grasp the user's learning progress by collecting the user's learning data and analyzing it with the analysis unit. For example, the learning management unit may record the content and time the user has studied and manage the learning progress. The learning management unit may also analyze the user's learning data and evaluate the effectiveness of the learning. Furthermore, the learning management unit may suggest the next content to be studied based on the user's learning data. In this way, the memory complement system may manage the user's learning progress and support effective learning.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The collection unit collects data about the user. The data about the user includes behavioral data, location information, emotion data, etc. The collection unit collects data by utilizing sensors in the smartwatch and smartphone. For example, the acceleration sensor in the smartwatch is used to collect the user's motion data, and the location information sensor in the smartphone is used to collect the user's location information. The heart rate sensor in the smartwatch can also be used to collect the user's heart rate data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed by methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to analyze the user's behavioral patterns, and statistical analysis techniques are used to analyze the user's behavioral data. Furthermore, machine learning algorithms can be used to learn the user's memory tendencies. Step 3: The completion unit completes the user's memory based on the analysis results obtained by the analysis unit. Complementation is performed based on criteria such as the method of presenting information, timing, and type of content. For example, information is provided when the user asks a question, and information is provided at an appropriate time based on the user's behavioral patterns. Furthermore, information can also be provided in the form of text, audio, images, etc.

[0062] (Example 2) The memory complement system according to the embodiment of the present invention is a system for complementing a user's memory. This memory complement system is an attempt to raise a digital clone that complements the user's memory by collecting data using a wearable device such as a smartwatch or smartphone and analyzing it with AI on the cloud. For example, the memory complement system collects data about the user. For example, the memory complement system collects data using sensors in a smartwatch or smartphone. For example, the memory complement system can collect the user's behavior history, emotional state, location information, and the like. Next, the memory complement system analyzes the collected data with AI on the cloud. For example, the memory complement system analyzes the collected data and learns the user's behavior patterns and memory tendencies. For example, the user's behavior patterns can be grasped by analyzing places that the user frequently visits and actions that he or she frequently performs. Furthermore, the memory complement system complements the user's memory based on the analysis results. For example, the memory complement system provides information when the user asks a question. For example, if the user asks, "What did you eat for dinner yesterday?", the memory complement system can provide information such as "I ate pasta for dinner yesterday" based on the analysis results. In addition, the memory complement system has a protection function for encrypting data and protecting privacy. For example, the memory complement system encrypts collected data to protect the user's privacy. For example, by encrypting data such as the user's behavior history and emotional state, unauthorized access by a third party can be prevented. Furthermore, the memory complement system has a function of predicting the user's behavior pattern. For example, the memory complement system predicts the user's behavior pattern based on the analysis result and predicts the user's next action. For example, if the user has the habit of jogging at 7 o'clock every morning, the memory complement system can predict, for example, "the next jogging will be tomorrow at 7 o'clock." In addition, the memory complement system estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is feeling stressed, the memory complement system can reduce the frequency of data collection to reduce the burden on the user. Furthermore, the memory complement system analyzes the user's past behavior history and selects a data collection method.For example, if a user has frequently visited a particular place in the past, more accurate data can be collected by prioritizing data collection at that place. In this way, the memory complement system is a system for complementing a user's memory, and attempts to raise a digital clone that complements the user's memory by collecting data using wearable devices such as smartwatches and smartphones and analyzing it with AI on the cloud. In this way, the memory complement system can complement the user's memory and support daily life.

[0063] The memory complement system according to the embodiment includes a collection unit, an analysis unit, and a complement unit. The collection unit collects data related to the user. The data related to the user includes, for example, behavioral data, location information, and emotion data, but is not limited to such examples. The collection unit collects data by utilizing, for example, a sensor of a smartwatch or a smartphone. For example, the collection unit collects the user's exercise data using an acceleration sensor of the smartwatch. The collection unit can also collect the user's location information using a location information sensor of the smartphone. Furthermore, the collection unit can collect the user's heartbeat data using a heartbeat sensor of the smartwatch. For example, the collection unit counts the number of steps of the user using the acceleration sensor of the smartwatch to grasp the amount of exercise. The collection unit can also record the user's movement route using the location information sensor of the smartphone to grasp the behavior pattern. Furthermore, the collection unit can monitor the user's heart rate using the heartbeat sensor of the smartwatch to grasp the health condition. The analysis unit analyzes the data collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, machine learning algorithms, and the like, but is not limited to such examples. For example, the analysis unit analyzes the user's behavioral patterns using data mining technology. The analysis unit can also analyze the user's behavioral data using statistical analysis technology. Furthermore, the analysis unit can learn the user's memory tendency using a machine learning algorithm. For example, the analysis unit extracts the user's behavioral patterns using data mining technology and performs frequency analysis. The analysis unit can also analyze the user's behavioral data in time series using statistical analysis technology to grasp trends. Furthermore, the analysis unit can model the user's memory tendency using a machine learning algorithm and make predictions. The complementing unit complements the user's memory based on the analysis results obtained by the analysis unit. The complementing is performed based on criteria such as, for example, the method of presenting information, timing, and type of content, but is not limited to such examples. For example, the complementing unit provides information when the user asks a question. The complementing unit can also provide information at an appropriate timing based on the user's behavioral patterns.Furthermore, the complementing unit can provide information in the form of text, voice, images, etc., to complement the user's memory. For example, when the user asks "What did you eat for dinner yesterday?", the complementing unit provides information such as "I had pasta for dinner yesterday" based on the analysis result. The complementing unit can also provide information at an appropriate time based on the places the user frequently visits and the actions he or she frequently performs. Furthermore, the complementing unit can provide information in the form of text messages, voice messages, images, etc., to complement the user's memory. In this way, the memory complementing system according to the embodiment can complement the user's memory and support daily life.

[0064] The collection unit collects data on the user. The data on the user includes, for example, behavioral data, location information, and emotion data, but is not limited to such examples. The collection unit collects data, for example, by utilizing a sensor of a smartwatch or a smartphone. Specifically, the acceleration sensor of the smartwatch is used to collect the user's exercise data. The acceleration sensor can detect the user's movements and changes in posture and measure the number of steps and the amount of exercise. This makes it possible to grasp the user's daily activity level. The collection unit can also collect the user's location information using the location information sensor of the smartphone. The location information sensor accurately records the user's current location and movement route using technologies such as GPS, Wi-Fi, and Bluetooth. This makes it possible to grasp the user's behavioral patterns and visited places. Furthermore, the collection unit can also collect the user's heart rate data using the heart rate sensor of the smartwatch. The heart rate sensor can monitor the user's heart rate and heart rate variability to evaluate the stress level and health condition. For example, the collection unit counts the user's steps using the acceleration sensor of the smartwatch to grasp the amount of exercise. The collection unit can also use the smartphone's location information sensor to record the user's movement route and understand the user's behavioral pattern. Furthermore, the collection unit can also monitor the user's heart rate using the smartwatch's heart rate sensor to understand the user's health condition. This allows the collection unit to collect various data about the user and understand detailed information about the user's daily life. The collected data is transmitted to a cloud server so that the analysis unit and completion unit can access it. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0065] The analysis unit analyzes the data collected by the collection unit. The analysis is performed by, for example, data mining, statistical analysis, machine learning algorithms, and the like, but is not limited to such examples. Specifically, the analysis unit analyzes the user's behavioral patterns using data mining technology. Data mining technology is a method for extracting useful information from a large amount of data and discovering patterns and trends. This makes it possible to grasp the user's behavioral patterns and habits. The analysis unit can also analyze the user's behavioral data using statistical analysis technology. Statistical analysis technology is a method for clarifying the distribution and correlation of data and understanding the characteristics of data. This makes it possible to analyze the user's behavioral data in a time series and grasp trends. Furthermore, the analysis unit can also learn the user's memory tendency using a machine learning algorithm. Machine learning algorithms are a method for learning from data and making predictions and classifications. This makes it possible to model the user's memory tendency and use it to supplement future behavior and memories. For example, the analysis unit uses data mining technology to extract the user's behavioral patterns and perform frequency analysis. The analysis unit can also use statistical analysis techniques to analyze the user's behavioral data over time to understand trends. Furthermore, the analysis unit can use machine learning algorithms to model the user's memory tendencies and make predictions. This allows the analysis unit to analyze the collected data from multiple angles and understand the user's behavior and memory tendencies. The analysis results are provided to the completion unit and serve as the basis for completing the user's memory.

[0066] The complementing unit complements the user's memory based on the analysis result obtained by the analysis unit. The complementing is performed based on criteria such as, for example, the method of presenting information, timing, and type of content, but is not limited to such examples. Specifically, the complementing unit provides information when the user asks a question. For example, when the user asks "What did you eat for dinner yesterday?", information such as "I had pasta for dinner yesterday" is provided based on the analysis result. The complementing unit can also provide information at an appropriate time based on the user's behavior pattern. For example, information can be provided at an appropriate time based on the user's frequently visited places and frequently performed actions. Furthermore, the complementing unit can also provide information in the form of text, voice, image, etc. to complement the user's memory. For example, when the user asks "What did you eat for dinner yesterday?", information such as "I had pasta for dinner yesterday" is provided based on the analysis result. Furthermore, the complementing unit can also provide information at an appropriate time based on the user's frequently visited places and frequently performed actions. Furthermore, the complementing unit can also provide information in the form of text message, voice message, image, etc. to complement the user's memory. For example, when a user asks "What did you eat for dinner last night?", information such as "I had pasta for dinner last night" is provided based on the analysis result. The complementing unit can also provide information at an appropriate time based on the places the user frequently visits and the actions the user frequently performs. Furthermore, the complementing unit can provide information in the form of a text message, a voice message, an image, or the like to complement the user's memory. In this way, the memory complementing system according to the embodiment can complement the user's memory and support daily life.

[0067] The collection unit can collect data by utilizing at least one of the sensors of the smartwatch or the smartphone. The collection unit, for example, uses an acceleration sensor of the smartwatch to collect the user's exercise data. For example, the collection unit counts the number of steps of the user using the acceleration sensor of the smartwatch to grasp the amount of exercise. The collection unit can also collect the user's location information using the location information sensor of the smartphone. For example, the collection unit can record the user's movement route using the location information sensor of the smartphone to grasp the behavior pattern. Furthermore, the collection unit can also collect the user's heart rate data using the heart rate sensor of the smartwatch. For example, the collection unit can monitor the user's heart rate using the heart rate sensor of the smartwatch to grasp the health condition. In this way, detailed data about the user can be collected by utilizing the sensors of the smartwatch or the smartphone. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired from the acceleration sensor of the smartwatch to the generation AI and cause the generation AI to execute analysis of the exercise data.

[0068] The analysis unit can learn behavioral patterns and memory tendencies by using AI to analyze data on the cloud. The analysis unit can, for example, analyze data on the cloud using AI to learn behavioral patterns and memory tendencies. For example, the analysis unit can analyze the user's behavioral patterns using data mining technology. The analysis unit can also analyze the user's behavioral data using statistical analysis technology. Furthermore, the analysis unit can learn the user's memory tendencies using machine learning algorithms. For example, the analysis unit can extract the user's behavioral patterns using data mining technology and perform frequency analysis. The analysis unit can also analyze the user's behavioral data in chronological order using statistical analysis technology to grasp trends. Furthermore, the analysis unit can model the user's memory tendencies using machine learning algorithms and make predictions. As a result, the behavioral patterns and memory tendencies can be efficiently learned by using AI to analyze data on the cloud. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data collected on the cloud into the generation AI and have the generation AI perform an analysis of behavioral patterns.

[0069] The complementing unit can provide information when the user asks a question. The complementing unit provides information when the user asks a question, for example. For example, when the user asks "What did you eat for dinner yesterday?", the complementing unit provides information such as "I ate pasta for dinner yesterday" based on the analysis result. The complementing unit can also provide information at an appropriate time based on the user's behavioral pattern. For example, the complementing unit can provide information at an appropriate time based on the places the user frequently visits and the actions the user frequently performs. Furthermore, the complementing unit can provide information in the form of a text message, a voice message, an image, or the like to complement the user's memory. This makes it possible to complement the memory by providing appropriate information when the user asks a question. A part or all of the above-mentioned processing in the complementing unit may be performed, for example, using AI, or may be performed without using AI. For example, the complementing unit can input the user's question to the generating AI and cause the generating AI to provide appropriate information.

[0070] The memory complement system includes at least one of a protection unit for encrypting data or protecting privacy. The protection unit encrypts data, for example. For example, the protection unit encrypts data using AES (Advanced Encryption Standard). The protection unit can also encrypt data using RSA (Rivest-Shamir-Adleman). Furthermore, the protection unit can also encrypt data using SSL / TLS (Secure Sockets Layer / Transport Layer Security). For example, the protection unit encrypts user behavior data using AES to prevent unauthorized access by a third party. The protection unit can also encrypt user emotion data using RSA to ensure data security. Furthermore, the protection unit can encrypt data communication using SSL / TLS to prevent data eavesdropping. This allows the user's data to be safely protected by encrypting data and protecting privacy. A part or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the data encryption process to the generation AI and leave the execution of encryption to the generation AI.

[0071] The memory complement system includes a prediction unit that predicts a user's behavior pattern. The prediction unit predicts, for example, the user's behavior pattern. For example, the prediction unit predicts the user's behavior pattern using a machine learning model. The prediction unit can also predict the user's behavior pattern using a prediction algorithm. Furthermore, the prediction unit can predict the user's next behavior based on past behavior data. For example, the prediction unit learns the user's behavior pattern using a machine learning model and predicts the user's next behavior. The prediction unit can also analyze the user's behavior pattern using a prediction algorithm and predict the user's next behavior. Furthermore, the prediction unit can model the user's behavior pattern based on past behavior data and predict the user's next behavior. Thereby, the user's behavior pattern can be predicted to predict the user's next behavior. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit inputs the user's behavior data to the generation AI and causes the generation AI to execute the prediction of the behavior pattern.

[0072] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of data collection based on the estimated emotion. For example, when the user is stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. Also, when the user is relaxed, the collection unit can increase the frequency of data collection and collect detailed data. Furthermore, when the user is excited, the collection unit can adjust the frequency of data collection and collect data at an appropriate timing. For example, when the user is stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. Also, when the user is relaxed, the collection unit can increase the frequency of data collection and collect detailed data. Furthermore, when the user is excited, the collection unit can adjust the frequency of data collection and collect data at an appropriate timing. This allows the burden on the user to be reduced by adjusting the timing of data collection according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0073] The collection unit can analyze the user's past behavior history and trigger data collection based on a specific event or behavior. The collection unit, for example, analyzes the user's past behavior history and triggers data collection based on a specific event or behavior. For example, the collection unit collects data when the user visits a specific place in the past. The collection unit can also trigger data collection when the user acts in a specific time period. Furthermore, the collection unit can start data collection when the user performs a specific activity. For example, the collection unit collects data when the user visits a specific place in the past. The collection unit can also trigger data collection when the user acts in a specific time period. Furthermore, the collection unit can start data collection when the user performs a specific activity. As a result, by triggering data collection based on the past behavior history, more accurate data can be collected. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavior data to the generation AI and cause the generation AI to execute a trigger for data collection.

[0074] The collection unit can dynamically change the type of data to be collected according to the user's current activity status when collecting data. For example, the collection unit dynamically changes the type of data to be collected according to the user's current activity status when collecting data. For example, when the user is exercising, the collection unit prioritizes collecting data such as heart rate and number of steps. In addition, when the user is resting, the collection unit can also collect data related to a relaxed state. In addition, when the user is working, the collection unit can also collect data related to a concentration level and a stress level. For example, when the user is exercising, the collection unit prioritizes collecting data such as heart rate and number of steps. In addition, when the user is resting, the collection unit can also collect data related to a relaxed state. In addition, when the user is working, the collection unit can also collect data related to a concentration level and a stress level. In this way, by changing the type of data to be collected according to the current activity status, appropriate data can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's activity data to the generation AI and cause the generation AI to change the type of data.

[0075] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated emotion. For example, when the user is stressed, the collection unit preferentially collects stress-related data. Also, when the user is relaxed, the collection unit can also preferentially collect data related to the relaxed state. Furthermore, when the user is excited, the collection unit can also preferentially collect data related to the excited state. For example, when the user is stressed, the collection unit preferentially collects stress-related data. Also, when the user is relaxed, the collection unit can also preferentially collect data related to the relaxed state. Furthermore, when the user is excited, the collection unit can also preferentially collect data related to the excited state. In this way, important data can be preferentially collected by determining the priority of data based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and have the generation AI determine the priority order of the data.

[0076] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting data. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. In addition, when the user is traveling, the collection unit can also preferentially collect data related to the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect data related to the home. For example, when the user is in a specific location, the collection unit preferentially collects data related to the location. In addition, when the user is traveling, the collection unit can also preferentially collect data related to the travel destination. Furthermore, when the user is at home, the collection unit can also preferentially collect data related to the home. In this way, by considering the geographical location information, highly relevant data can be preferentially collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0077] The collection unit can analyze the social media activity of the user at the time of data collection and collect related data. The collection unit, for example, analyzes the social media activity of the user at the time of data collection and collects related data. For example, when the user posts about a specific topic on social media, the collection unit collects data related to the topic. In addition, when the user participates in a specific event on social media, the collection unit can also collect data related to the event. Furthermore, when the user checks in to a specific place on social media, the collection unit can also collect data related to the place. For example, when the user posts about a specific topic on social media, the collection unit collects data related to the topic. In addition, when the user participates in a specific event on social media, the collection unit can also collect data related to the event. Furthermore, when the user checks in to a specific place on social media, the collection unit can also collect data related to the place. In this way, by analyzing social media activity, related data can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media data of the user to the generation AI and cause the generation AI to execute collection of related data.

[0078] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis algorithm based on the estimated emotion. For example, when the user is stressed, the analysis unit analyzes with emphasis on stress-related data. In addition, when the user is relaxed, the analysis unit can also analyze with emphasis on data related to the relaxed state. Furthermore, when the user is excited, the analysis unit can also analyze with emphasis on data related to the excited state. For example, when the user is stressed, the analysis unit analyzes with emphasis on stress-related data. In addition, when the user is relaxed, the analysis unit can also analyze with emphasis on data related to the relaxed state. Furthermore, when the user is excited, the analysis unit can also analyze with emphasis on data related to the excited state. Thereby, by adjusting the analysis algorithm based on the user's emotion, a more appropriate analysis result can be obtained. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0079] The analysis unit can detect changes in the user's behavior pattern in real time during analysis and reflect them in the analysis results. For example, the analysis unit can detect changes in the user's behavior pattern in real time during analysis and reflect them in the analysis results. For example, when the user's behavior pattern suddenly changes, the analysis unit detects the change in real time and reflects it in the analysis results. In addition, when the user's behavior pattern gradually changes, the analysis unit can also detect the change in real time and reflect it in the analysis results. In addition, when the user's behavior pattern temporarily changes, the analysis unit can detect the change in real time and reflect it in the analysis results. For example, when the user's behavior pattern suddenly changes, the analysis unit detects the change in real time and reflects it in the analysis results. In addition, when the user's behavior pattern gradually changes, the analysis unit can detect the change in real time and reflect it in the analysis results. In addition, when the user's behavior pattern temporarily changes, the analysis unit can detect the change in real time and reflect it in the analysis results. In this way, the latest analysis results can be provided by detecting changes in behavior patterns in real time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's behavioral data to the generation AI and cause the generation AI to detect changes in behavioral patterns.

[0080] The analysis unit can compare the user's past data and current data during the analysis to identify an abnormal pattern. The analysis unit can, for example, compare the user's past data and current data during the analysis to identify an abnormal pattern. For example, the analysis unit can compare the user's past data and current data to identify an abnormal behavior pattern. The analysis unit can also compare the user's past data and current data to identify an abnormal emotion pattern. The analysis unit can also compare the user's past data and current data to identify an abnormal health condition. For example, the analysis unit can compare the user's past data and current data to identify an abnormal behavior pattern. The analysis unit can also compare the user's past data and current data to identify an abnormal emotion pattern. The analysis unit can also compare the user's past data and current data to identify an abnormal health condition. In this way, by comparing the past data and the current data, an abnormal pattern can be identified. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input a user's past and current data into the generation AI and have the generation AI identify anomalous patterns.

[0081] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated emotion. For example, the analysis unit provides a simple and highly visible display method when the user is stressed. Furthermore, the analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a visually stimulating display method when the user is excited. For example, the analysis unit provides a simple and highly visible display method when the user is stressed. Furthermore, the analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a visually stimulating display method when the user is excited. In this way, by adjusting the display method based on the user's emotion, it is possible to provide a highly visible analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0082] The analysis unit can customize the analysis result by considering the geographical location information of the user at the time of analysis. The analysis unit customizes the analysis result by considering the geographical location information of the user at the time of analysis, for example. For example, when the user is in a specific location, the analysis unit provides an analysis result related to the location. In addition, when the user is traveling, the analysis unit can also provide an analysis result related to the travel destination. Furthermore, when the user is at home, the analysis unit can also provide an analysis result related to the home. For example, when the user is in a specific location, the analysis unit provides an analysis result related to the location. In addition, when the user is traveling, the analysis unit can also provide an analysis result related to the travel destination. Furthermore, when the user is at home, the analysis unit can also provide an analysis result related to the home. In this way, by considering the geographical location information, it is possible to provide an analysis result with high relevance. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute customization of the analysis result.

[0083] The analysis unit can complement the analysis result by referring to the social media activity of the user during the analysis. The analysis unit complements the analysis result by referring to the social media activity of the user during the analysis. For example, when the user posts about a specific topic on social media, the analysis unit provides an analysis result related to the topic. In addition, when the user participates in a specific event on social media, the analysis unit can provide an analysis result related to the event. Furthermore, when the user checks in to a specific place on social media, the analysis unit can provide an analysis result related to the place. For example, when the user posts about a specific topic on social media, the analysis unit provides an analysis result related to the topic. In addition, when the user participates in a specific event on social media, the analysis unit can provide an analysis result related to the event. Furthermore, when the user checks in to a specific place on social media, the analysis unit can provide an analysis result related to the place. In this way, by referring to the social media activity, it is possible to provide a related analysis result. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the social media data of the user to the generation AI and causes the generation AI to execute the complement of the analysis result.

[0084] The complement unit can estimate the user's emotion and adjust the expression method of the information to be provided based on the estimated emotion. The complement unit, for example, estimates the user's emotion and adjusts the expression method of the information to be provided based on the estimated emotion. For example, the complement unit provides simple and highly visible information when the user is stressed. Also, the complement unit can provide detailed information when the user is relaxed. Furthermore, the complement unit can provide visually stimulating information when the user is excited. For example, the complement unit provides simple and highly visible information when the user is stressed. Also, the complement unit can provide detailed information when the user is relaxed. Furthermore, the complement unit can provide visually stimulating information when the user is excited. In this way, by adjusting the expression method of the information based on the user's emotion, it is possible to provide information with high visibility. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the complement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the completion unit can input a user's emotional data into the generation AI and have the generation AI adjust the way information is expressed.

[0085] The completion unit can provide optimal information by referring to the user's past question history at the time of completion. For example, the completion unit can provide optimal information by referring to the user's past question history at the time of completion. For example, the completion unit provides related information based on the content of the user's past questions. The completion unit can also provide frequently asked content preferentially from the user's past question history. Furthermore, the completion unit can analyze the user's past question history and provide the most appropriate information. For example, the completion unit provides related information based on the content of the user's past questions. The completion unit can also provide frequently asked content preferentially from the user's past question history. Furthermore, the completion unit can analyze the user's past question history and provide the most appropriate information. As a result, optimal information can be provided by referring to the past question history. A part or all of the above-mentioned processing in the completion unit may be performed using, for example, AI, or may be performed without using AI. For example, the completion unit can input the user's question history data to the generation AI and cause the generation AI to provide optimal information.

[0086] The completion unit can adjust the level of detail of the information to be provided according to the user's current situation at the time of completion. The completion unit adjusts the level of detail of the information to be provided according to the user's current situation at the time of completion, for example. For example, the completion unit provides concise information when the user is in a hurry. The completion unit can also provide detailed information when the user is relaxed. Furthermore, the completion unit can provide visually stimulating information when the user is excited. For example, the completion unit provides concise information when the user is in a hurry. The completion unit can also provide detailed information when the user is relaxed. Furthermore, the completion unit can provide visually stimulating information when the user is excited. This allows the level of detail of the information to be adjusted according to the current situation, thereby providing appropriate information. Part or all of the above-mentioned processing in the completion unit may be performed, for example, using AI, or may be performed without using AI. For example, the completion unit can input the user's current situation data to the generation AI and cause the generation AI to adjust the level of detail of the information.

[0087] The complement unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated emotion. The complement unit, for example, estimates the user's emotion and determines the priority of information to be provided based on the estimated emotion. For example, when the user is stressed, the complement unit provides stress-related information preferentially. Furthermore, when the user is relaxed, the complement unit can also provide information related to the relaxed state preferentially. Furthermore, when the user is excited, the complement unit can also provide information related to the excited state preferentially. For example, when the user is stressed, the complement unit provides stress-related information preferentially. Furthermore, when the user is relaxed, the complement unit can also provide information related to the relaxed state preferentially. Furthermore, when the user is excited, the complement unit can also provide information related to the excited state preferentially. In this way, by determining the priority of information based on the user's emotion, important information can be provided preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the complementing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the complementing unit may input the user's emotion data to the generating AI and cause the generating AI to determine the priority order of information.

[0088] The completion unit can provide highly relevant information in consideration of the geographical location information of the user at the time of completion. For example, the completion unit provides highly relevant information in consideration of the geographical location information of the user at the time of completion. For example, when the user is in a specific location, the completion unit provides information related to the location. In addition, when the user is traveling, the completion unit can also provide information related to the travel destination. Furthermore, when the user is at home, the completion unit can also provide information related to the home. For example, when the user is in a specific location, the completion unit provides information related to the location. In addition, when the user is traveling, the completion unit can also provide information related to the travel destination. Furthermore, when the user is at home, the completion unit can also provide information related to the home. In this way, by taking the geographical location information into consideration, highly relevant information can be provided. Part or all of the above-mentioned processing in the completion unit may be performed, for example, using AI, or may be performed without using AI. For example, the completion unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute the provision of highly relevant information.

[0089] The completion unit can analyze the social media activity of the user at the time of completion and provide related information. The completion unit can analyze the social media activity of the user at the time of completion, for example, and provide related information. For example, when the user posts about a specific topic on social media, the completion unit can provide information related to the topic. In addition, when the user participates in a specific event on social media, the completion unit can provide information related to the event. Furthermore, when the user checks in to a specific place on social media, the completion unit can provide information related to the place. For example, when the user posts about a specific topic on social media, the completion unit can provide information related to the topic. In addition, when the user participates in a specific event on social media, the completion unit can provide information related to the event. Furthermore, when the user checks in to a specific place on social media, the completion unit can provide information related to the place. In this way, by analyzing social media activity, related information can be efficiently provided. Part or all of the above-mentioned processing in the completion unit may be performed, for example, using AI, or may be performed without using AI. For example, the completion unit can input the social media data of the user to the generation AI and cause the generation AI to provide related information.

[0090] The protection unit can estimate the user's emotion and adjust the encryption level of the data based on the estimated emotion. The protection unit, for example, estimates the user's emotion and adjusts the encryption level of the data based on the estimated emotion. For example, when the user is stressed, the protection unit increases the encryption level of the data to provide a sense of security. In addition, when the user is relaxed, the protection unit can also maintain a normal encryption level. In addition, when the user is excited, the protection unit can adjust the encryption level of the data to provide appropriate protection. For example, when the user is stressed, the protection unit increases the encryption level of the data to provide a sense of security. In addition, when the user is relaxed, the protection unit can also maintain a normal encryption level. In addition, when the user is excited, the protection unit can adjust the encryption level of the data to provide appropriate protection. In this way, by adjusting the encryption level based on the user's emotion, appropriate data protection can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processes in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the encryption level.

[0091] The protection unit can select an optimal protection method by referring to the user's past data access history when protecting data. For example, the protection unit selects an optimal protection method by referring to the user's past data access history when protecting data. For example, the protection unit preferentially protects data that the user frequently accessed in the past. The protection unit can also adjust the protection level for specific data from the user's past data access history. Furthermore, the protection unit can analyze the user's past data access history and select an optimal protection method. For example, the protection unit preferentially protects data that the user frequently accessed in the past. The protection unit can also adjust the protection level for specific data from the user's past data access history. Furthermore, the protection unit can analyze the user's past data access history and select an optimal protection method. As a result, optimal data protection can be provided by referring to the past data access history. A part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can input the user's data access history to the generation AI and cause the generation AI to select the optimal protection method.

[0092] The protection unit can estimate the user's emotion and determine the priority of data protection based on the estimated emotion. The protection unit, for example, estimates the user's emotion and determines the priority of data protection based on the estimated emotion. For example, when the user is stressed, the protection unit protects important data with priority. In addition, when the user is relaxed, the protection unit can also perform normal data protection. Furthermore, when the user is excited, the protection unit can adjust the priority of data protection. For example, when the user is stressed, the protection unit protects important data with priority. In addition, when the user is relaxed, the protection unit can also perform normal data protection. Furthermore, when the user is excited, the protection unit can adjust the priority of data protection. As a result, important data can be protected with priority by determining the priority of data protection based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the protection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the protection unit can input a user's emotional data into the generation AI and have the generation AI determine the priority of data protection.

[0093] The protection unit can select the optimal protection method in consideration of the geographical location information of the user when protecting data. For example, the protection unit selects the optimal protection method in consideration of the geographical location information of the user when protecting data. For example, when the user is in a specific location, the protection unit prioritizes protecting data related to the location. In addition, when the user is traveling, the protection unit can also prioritize protecting data related to the travel destination. Furthermore, when the user is at home, the protection unit can also prioritize protecting data related to the home. For example, when the user is in a specific location, the protection unit prioritizes protecting data related to the location. In addition, when the user is traveling, the protection unit can also prioritize protecting data related to the travel destination. Furthermore, when the user is at home, the protection unit can also prioritize protecting data related to the home. In this way, optimal data protection can be provided by considering the geographical location information. Part or all of the above-mentioned processing in the protection unit may be performed, for example, using AI, or may be performed without using AI. For example, the protection unit can input the geographical location data of the user to the generation AI and cause the generation AI to select the optimal protection method.

[0094] The prediction unit can estimate the user's emotion and adjust the prediction algorithm based on the estimated emotion. The prediction unit, for example, estimates the user's emotion and adjusts the prediction algorithm based on the estimated emotion. For example, the prediction unit predicts stress-related behavior when the user is stressed. In addition, the prediction unit can also predict behavior related to the relaxed state when the user is relaxed. Furthermore, the prediction unit can predict behavior related to the excited state when the user is excited. For example, the prediction unit predicts stress-related behavior when the user is stressed. In addition, the prediction unit can also predict behavior related to the relaxed state when the user is relaxed. Furthermore, the prediction unit can also predict behavior related to the excited state when the user is excited. Thereby, by adjusting the prediction algorithm based on the user's emotion, a more appropriate prediction result can be obtained. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input user emotion data to the generation AI and cause the generation AI to adjust the prediction algorithm.

[0095] The prediction unit can predict the next behavior by referring to the user's past behavior pattern at the time of prediction. The prediction unit, for example, predicts the next behavior by referring to the user's past behavior pattern at the time of prediction. For example, if the user has been jogging every morning in the past, the prediction unit predicts the timing of the next jogging. In addition, the prediction unit can also predict the behavior of the user if the user has visited a specific place on a specific day of the week in the past. Furthermore, the prediction unit can analyze the user's past behavior pattern and predict the next behavior. For example, if the user has been jogging every morning in the past, the prediction unit predicts the timing of the next jogging. In addition, the prediction unit can predict the behavior of the user if the user has visited a specific place on a specific day of the week in the past. Furthermore, the prediction unit can analyze the user's past behavior pattern and predict the next behavior. As a result, the next behavior can be accurately predicted by referring to the past behavior pattern. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's past behavior data to the generation AI and cause the generation AI to execute a prediction of the next behavior.

[0096] The prediction unit can customize the prediction result based on the current situation of the user at the time of prediction. The prediction unit customizes the prediction result based on the current situation of the user, for example, at the time of prediction. For example, the prediction unit predicts the next break time when the user is currently at work. Also, the prediction unit can predict the next visit to a tourist spot when the user is currently traveling. Furthermore, the prediction unit can predict the timing of the next outing when the user is currently at home. For example, the prediction unit predicts the next break time when the user is currently at work. Also, the prediction unit can predict the next visit to a tourist spot when the user is currently traveling. Furthermore, the prediction unit can predict the timing of the next outing when the user is currently at home. This allows the prediction result to be customized based on the current situation, thereby providing an appropriate prediction result. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's current situation data to the generation AI and cause the generation AI to customize the prediction result.

[0097] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated emotion. The prediction unit, for example, estimates the user's emotion and adjusts the display method of the prediction result based on the estimated emotion. For example, the prediction unit provides a simple and highly visible display method when the user is stressed. The prediction unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the prediction unit can also provide a visually stimulating display method when the user is excited. For example, the prediction unit provides a simple and highly visible display method when the user is stressed. The prediction unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the prediction unit can also provide a visually stimulating display method when the user is excited. Thereby, by adjusting the display method based on the user's emotion, a highly visible prediction result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0098] The prediction unit can customize the prediction result by considering the geographical location information of the user at the time of prediction. The prediction unit customizes the prediction result by considering the geographical location information of the user at the time of prediction, for example. For example, when the user is in a specific location, the prediction unit provides a prediction result related to the location. In addition, when the user is traveling, the prediction unit can also provide a prediction result related to the travel destination. In addition, when the user is at home, the prediction unit can also provide a prediction result related to the home. For example, when the user is in a specific location, the prediction unit provides a prediction result related to the location. In addition, when the user is traveling, the prediction unit can also provide a prediction result related to the travel destination. In addition, when the user is at home, the prediction unit can also provide a prediction result related to the home. In this way, by considering the geographical location information, a highly relevant prediction result can be provided. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the geographical location data of the user to the generation AI and cause the generation AI to execute customization of the prediction result.

[0099] The prediction unit can complement the prediction result by referring to the social media activity of the user at the time of prediction. The prediction unit complements the prediction result by referring to the social media activity of the user at the time of prediction, for example. For example, when the user posts about a specific topic on social media, the prediction unit provides a prediction result related to the topic. In addition, when the user participates in a specific event on social media, the prediction unit can also provide a prediction result related to the event. Furthermore, when the user checks in to a specific place on social media, the prediction unit can also provide a prediction result related to the place. For example, when the user posts about a specific topic on social media, the prediction unit provides a prediction result related to the topic. In addition, when the user participates in a specific event on social media, the prediction unit can also provide a prediction result related to the event. Furthermore, when the user checks in to a specific place on social media, the prediction unit can also provide a prediction result related to the place. In this way, by referring to the social media activity, it is possible to provide a related prediction result. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the user's social media data to the generation AI and cause the generation AI to execute the complement of the prediction result.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The memory complement system may further include a health management unit that monitors the user's health condition. The health management unit can grasp the user's health condition by collecting the user's health data and analyzing it with the analysis unit. For example, the health management unit monitors the user's heart rate using a heart rate sensor of a smart watch and detects abnormal heart rates. The health management unit can also evaluate the user's sleep quality by collecting the user's sleep data and analyzing it with the analysis unit. Furthermore, the health management unit can evaluate the user's nutritional balance by collecting the user's dietary data and analyzing it with the analysis unit. In this way, the memory complement system can monitor the user's health condition and support health management.

[0102] The memory complement system may further include a hobby analysis unit that analyzes the hobbies and interests of the user. The hobby analysis unit may analyze the user's behavioral data and social media posts to identify the user's hobbies and interests. For example, the hobby analysis unit may analyze the places the user frequently visits and the events the user participates in to identify the user's hobbies. The hobby analysis unit may also analyze the content of the user's social media posts to identify the user's interests. Furthermore, the hobby analysis unit may analyze the user's purchasing history to identify the user's interests. This allows the memory complement system to understand the user's hobbies and interests and provide related information.

[0103] The memory complement system may further include a learning management unit that manages the user's learning progress. The learning management unit can grasp the user's learning progress by collecting the user's learning data and analyzing it with the analysis unit. For example, the learning management unit may record the content and time the user has studied and manage the learning progress. The learning management unit may also analyze the user's learning data and evaluate the effectiveness of the learning. Furthermore, the learning management unit may suggest the next content to be studied based on the user's learning data. In this way, the memory complement system may manage the user's learning progress and support effective learning.

[0104] The memory complement system may further include a stress management unit that estimates the user's emotions and manages the user's stress level based on the estimated emotions. The stress management unit can grasp the user's stress level by collecting the user's emotion data and analyzing it with the analysis unit. For example, the stress management unit analyzes the user's heart rate and behavioral data to evaluate the stress level. The stress management unit can also analyze the user's emotion data and identify the cause of stress. Furthermore, the stress management unit can suggest relaxation methods according to the user's stress level. In this way, the memory complement system can manage the user's stress level and support stress reduction.

[0105] The memory complement system may further include a motivation management unit that estimates the user's emotions and manages the user's motivation based on the estimated emotions. The motivation management unit can grasp the user's motivation by collecting the user's emotion data and analyzing it with the analysis unit. For example, the motivation management unit may analyze the user's behavioral data and emotion data to evaluate fluctuations in motivation. The motivation management unit may also analyze the user's emotion data and suggest a method for improving motivation. Furthermore, the motivation management unit may support goal setting to maintain the user's motivation. In this way, the memory complement system can manage the user's motivation and support goal achievement.

[0106] The memory complement system may further include a social management unit that estimates the user's emotions and manages the user's social activities based on the estimated emotions. The social management unit can grasp the user's social activities by collecting the user's emotional data and analyzing it with the analysis unit. For example, the social management unit can analyze the user's behavioral data and emotional data to evaluate the frequency and quality of social activities. The social management unit can also analyze the user's emotional data and suggest ways to improve social activities. Furthermore, the social management unit can suggest events and activities to promote the user's social activities. In this way, the memory complement system can manage the user's social activities and support the improvement of social skills.

[0107] The memory complement system may further include a relaxation suggestion unit that estimates the user's emotions and suggests a method for the user to relax based on the estimated emotions. The relaxation suggestion unit can suggest a method for the user to relax by collecting the user's emotion data and analyzing it with the analysis unit. For example, the relaxation suggestion unit may analyze the user's heart rate and behavioral data and suggest a method for relaxing. The relaxation suggestion unit may also analyze the user's emotion data and suggest activities that have a high relaxing effect. Furthermore, the relaxation suggestion unit may suggest environmental settings for maintaining the user in a relaxed state. In this way, the memory complement system can suggest a method for the user to relax and enhance the relaxation effect.

[0108] The memory complement system may further include a sleep management unit that estimates the user's emotions and improves the user's quality of sleep based on the estimated emotions. The sleep management unit can improve the user's quality of sleep by collecting the user's emotion data and analyzing it with the analysis unit. For example, the sleep management unit may analyze the user's heart rate and behavior data to evaluate the quality of sleep. The sleep management unit may also analyze the user's emotion data and suggest a method for improving the quality of sleep. Furthermore, the sleep management unit may make suggestions for optimizing the user's sleep environment. In this way, the memory complement system can improve the user's quality of sleep and support a healthy life.

[0109] The memory complement system may further include an exercise management unit that estimates the user's emotions and manages the user's exercise habits based on the estimated emotions. The exercise management unit can manage the user's exercise habits by collecting the user's emotion data and analyzing it with the analysis unit. For example, the exercise management unit can analyze the user's heart rate and behavior data to evaluate the effectiveness of exercise. The exercise management unit can also analyze the user's emotion data and suggest a method to improve the user's motivation to exercise. Furthermore, the exercise management unit can support the user in setting goals to maintain the user's exercise habits. In this way, the memory complement system can manage the user's exercise habits and support a healthy lifestyle.

[0110] The memory complement system may further include a diet management unit that estimates the user's emotions and manages the user's eating habits based on the estimated emotions. The diet management unit can manage the user's eating habits by collecting the user's emotion data and analyzing it with the analysis unit. For example, the diet management unit can analyze the user's diet data and emotion data and evaluate the nutritional balance of meals. The diet management unit can also analyze the user's emotion data and suggest a method to improve the motivation for eating. Furthermore, the diet management unit can support the user in setting goals to maintain the eating habits. In this way, the memory complement system can manage the user's eating habits and support a healthy life.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The collection unit collects data about the user. The data about the user includes behavioral data, location information, emotion data, etc. The collection unit collects data by utilizing sensors in the smartwatch and smartphone. For example, the acceleration sensor in the smartwatch is used to collect the user's motion data, and the location information sensor in the smartphone is used to collect the user's location information. The heart rate sensor in the smartwatch can also be used to collect the user's heart rate data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed by methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to analyze the user's behavioral patterns, and statistical analysis techniques are used to analyze the user's behavioral data. Furthermore, machine learning algorithms can be used to learn the user's memory tendencies. Step 3: The completion unit completes the user's memory based on the analysis results obtained by the analysis unit. Complementation is performed based on criteria such as the method of presenting information, timing, and type of content. For example, information is provided when the user asks a question, and information is provided at an appropriate time based on the user's behavioral patterns. Furthermore, information can also be provided in the form of text, audio, images, etc.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] For example, the collection unit collects behavioral data and emotion data of the user using a sensor of the smart device 14. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and learns the behavioral patterns of the user. For example, the complement unit complements the memory of the user based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the protection unit encrypts data by the specific processing unit 290 of the data processing device 12 to protect the privacy of the user. For example, the prediction unit predicts the behavioral patterns of the user by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0124] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] For example, the collection unit collects behavioral data and emotion data of the user using the sensor of the smart glasses 214. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and learns the behavioral patterns of the user. For example, the complement unit complements the memory of the user based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the protection unit encrypts data by the specific processing unit 290 of the data processing device 12 to protect the privacy of the user. For example, the prediction unit predicts the behavioral patterns of the user by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] For example, the collection unit collects behavioral data and emotion data of the user using a sensor of the headset type terminal 314. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and learns the behavioral patterns of the user. For example, the complement unit complements the memory of the user based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the protection unit encrypts data by the specific processing unit 290 of the data processing device 12 to protect the privacy of the user. For example, the prediction unit predicts the behavioral patterns of the user by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] For example, the collection unit collects behavioral data and emotion data of the user using a sensor of the robot 414. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and learns the behavioral patterns of the user. For example, the complement unit complements the memory of the user based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the protection unit encrypts data by the specific processing unit 290 of the data processing device 12 to protect the privacy of the user. For example, the prediction unit predicts the behavioral patterns of the user by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A collection unit that collects data about a user; an analysis unit that analyzes the data collected by the collection unit; A complementing unit that complements the memory of the user based on the analysis result obtained by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect data using at least one of the sensors on your smartwatch or smartphone. 2. The system of claim 1, (Appendix 3) The analysis unit is AI analyzes data on the cloud and learns behavioral patterns and memory tendencies. 2. The system of claim 1, (Appendix 4) The complementing unit is Providing information when users ask questions 2. The system of claim 1, (Appendix 5) At least one of the following protection units is provided: data encryption or privacy protection 2. The system of claim 1, (Appendix 6) Equipped with a prediction unit that predicts user behavior patterns 2. The system of claim 1, (Appendix 7) The collecting unit includes: Infer user emotions and adjust the timing of data collection based on the inferred emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze users' past behavioral history and trigger data collection based on specific events or actions 2. The system of claim 1, (Appendix 9) The collecting unit includes: Dynamically change the type of data collected during data collection based on the user's current activity 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user emotions and prioritize data collection based on the inferred emotions 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust analysis algorithms based on the inferred emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, changes in user behavior patterns are detected in real time and reflected in the analysis results. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, we compare a user's past data with their current data to identify anomalous patterns. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, take into account the user's geographic location to customize analysis results 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, we complement the analysis results by looking at the user's social media activity. 2. The system of claim 1, (Appendix 19) The complementing unit is Estimate the user's emotions and adjust the way information is presented based on the estimated emotions. 2. The system of claim 1, (Appendix 20) The complementing unit is When completing a question, the system provides the most appropriate information by referring to the user's past question history. 2. The system of claim 1, (Appendix 21) The complementing unit is When completing, adjust the level of detail provided depending on the user's current situation 2. The system of claim 1, (Appendix 22) The complementing unit is Estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. 2. The system of claim 1, (Appendix 23) The complementing unit is When completing a query, the system takes into account the user's geographic location to provide more relevant information. 2. The system of claim 1, (Appendix 24) The complementing unit is When completed, analyze your social media activity and provide you with relevant information 2. The system of claim 1, (Appendix 25) The protective part is Infer user emotions and adjust data encryption levels based on the inferred emotions 2. The system of claim 1, (Appendix 26) The protective part is When protecting data, the system selects the optimal protection method by referring to the user's past data access history. 2. The system of claim 1, (Appendix 27) The protective part is Inferring user sentiment and prioritizing data protection based on the inferred sentiment 2. The system of claim 1, (Appendix 28) The protective part is When protecting data, the optimal protection method is selected taking into account the user's geographic location information 2. The system of claim 1, (Appendix 29) The prediction unit is Infer user emotions and adjust predictive algorithms based on the inferred emotions 2. The system of claim 1, (Appendix 30) The prediction unit is When making predictions, the system refers to the user's past behavior patterns to predict the next behavior. 2. The system of claim 1, (Appendix 31) The prediction unit is At the time of prediction, customize the prediction results based on the user's current situation 2. The system of claim 1, (Appendix 32) The prediction unit is Infer the user's emotions and adjust how predictions are displayed based on the inferred emotions 2. The system of claim 1, (Appendix 33) The prediction unit is At the time of prediction, consider the user's geographic location to customize the prediction results 2. The system of claim 1, (Appendix 34) The prediction unit is When making predictions, the system complements the prediction results by looking at the user's social media activity. 2. The system of claim 1, [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects data about a user; an analysis unit that analyzes the data collected by the collection unit; A complementing unit that complements the memory of the user based on the analysis result obtained by the analysis unit. A system characterized by:

2. The collecting unit includes: Collect data using at least one of the sensors on your smartwatch or smartphone.

2. The system of claim 1 .

3. The analysis unit is AI analyzes data on the cloud and learns behavioral patterns and memory tendencies.

2. The system of claim 1 .

4. The complementing unit is Providing information when the user asks a question 2. The system of claim 1 .

5. At least one of the following protection units is provided: data encryption or privacy protection 2. The system of claim 1 .

6. A prediction unit for predicting the behavior pattern of the user 2. The system of claim 1 .

7. The collecting unit includes: Inferring the user's emotions and adjusting the timing of data collection based on the inferred emotions.

2. The system of claim 1 .

8. The collecting unit includes: Analyzing the user's past behavioral history and triggering data collection based on specific events or actions 2. The system of claim 1 .

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