System

Abstract

To provide a system capable of efficiently collecting and analyzing user voice data, image data, and location information to provide the same when needed.SOLUTION: The system in an embodiment includes a collection unit, an analysis unit, a storage unit, and a providing unit. The collection unit collects audio data, image data, and location information about the user. The analysis unit analyzes the data collected by the collection unit. The storage unit stores the data analyzed by the analysis unit. The providing unit provides the data stored by the storage unit in response to inquiries from users.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] With conventional technology, it was difficult to efficiently collect and analyze users' voice data, image data, and location information, and provide this information when needed.

[0005] The system according to the embodiment aims to efficiently collect and analyze users' voice data, image data, and location information, and provide them when necessary. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a storage unit, and a provision unit. The collection unit collects voice data, image data, and position information related to a user. The analysis unit analyzes the data collected by the collection unit. The storage unit stores the data analyzed by the analysis unit. The provision unit provides the data stored by the storage unit in response to an inquiry from a user. Effect of the Invention

[0007] The system according to the embodiment can efficiently collect and analyze a user's voice data, image data, and location information, and provide the information when necessary. [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) A memory assistance system according to an embodiment of the present invention is a system for supporting people with Alzheimer's disease or dementia. This memory assistance system collects voice data, photo and video data, location information, and the like from the user's daily life, and analyzes and stores the data. The user can easily look back on past events and memories by making voice inquiries to the system. In addition, the information collection function can be turned off by setting a time or manually, and data security is also ensured. For example, the memory assistance system includes a collection unit that collects voice data, photo and video data, and location information about the user. This collection unit estimates the user's emotions and adjusts the type and priority of data to be collected based on the emotions. For example, when the user is having fun, photos and videos of that moment are collected preferentially. In addition, the user's past behavior history, current activity status, geographical location information, social media activity, and the like are analyzed, and an appropriate data collection method is selected. Next, the collected data is analyzed by the analysis unit. The analysis unit analyzes the collected data in detail and grasps the user's behavior and emotional patterns. For example, if a user repeats a specific behavior in a specific place, the pattern is analyzed and recorded. The analyzed data is stored in the storage unit. The storage unit securely stores the analyzed data and provides it to the providing unit as necessary. The stored data is provided by the providing unit in response to a user's inquiry. The providing unit accepts voice inquiries from the user and provides the stored data in an interactive format. For example, when a user inquires, "What did you eat for dinner last night?", the providing unit responds based on the stored data. In addition, the system has an off function that turns off information collection. Users can turn off the information collection function by setting a time or manually, protecting privacy. In addition, the system has a security function that ensures data security and prevents unauthorized access to collected data. This allows people with Alzheimer's and dementia to easily look back on memories and daily events that they do not want to forget, improving their quality of life. This allows the memory assistance system to support people with Alzheimer's and dementia, and to easily look back on memories and daily events that they do not want to forget.

[0029] The memory assistance system according to the embodiment includes a collection unit, an analysis unit, a storage unit, and a provision unit. The collection unit collects voice data, photo / video data, and location information related to the user. For example, the collection unit collects the voice of the user using a microphone and stores it as voice data. The collection unit can also collect photos and videos taken by the user and store them as image data. Furthermore, the collection unit can collect the user's location information using GPS or Wi-Fi location information. For example, when the user carries a smartphone, the collection unit collects GPS data and records the user's movement history. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes voice data and converts the user's remarks into text data. The analysis unit can also analyze image data and recognize the contents of the photos and videos. Furthermore, the analysis unit can analyze location information and grasp the user's behavior pattern. For example, the analysis unit analyzes that the user frequently visits a specific place and evaluates the importance of the place. The storage unit stores the data analyzed by the analysis unit. The storage unit stores, for example, the analyzed text data and image data in a database. The storage unit can also store the analyzed location information in chronological order. For example, the storage unit stores the user's movement history in chronological order so that it can be referenced later. The providing unit provides the data stored by the storage unit in response to an inquiry from the user. For example, when the user makes a voice inquiry such as "What did you eat for dinner last night?", the providing unit responds based on the stored data. The providing unit can also respond to the user's inquiry in an interactive format. For example, when the user makes an inquiry such as "Show me photos of your trip last week," the providing unit displays the stored photo data. As a result, the memory assistance system according to the embodiment can support people with Alzheimer's disease and dementia by collecting, analyzing, storing, and providing data from the user's daily life.

[0030] The collection unit collects voice data, photo / video data, and location information related to the user. For example, the collection unit collects the voice of the user using a microphone and saves it as voice data. Specifically, the collection unit uses a high-sensitivity microphone built into a smartphone or wearable device to clearly collect the user's remarks. The voice data is recorded clearly by removing background sounds using noise reduction technology. The collection unit can also collect photos and videos taken by the user and save them as image data. For example, photos and videos taken with a smartphone camera or digital camera are automatically uploaded to cloud storage and centrally managed by the collection unit. Furthermore, the collection unit can also collect the user's location information using GPS or Wi-Fi location information. For example, when the user carries a smartphone, the collection unit collects GPS data and records the user's movement history. Wi-Fi location information is effective for identifying indoor locations and provides accurate location information even in places where GPS signals cannot reach. This allows the collection unit to comprehensively collect the user's voice, image, and location information and create a detailed record of the user's daily life. Furthermore, the collection unit collects this data in real time and immediately transmits it to the analysis unit and storage unit, thereby improving the efficiency of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes voice data and converts the user's speech into text data. Specifically, the analysis unit converts the voice data into text with high accuracy using voice recognition technology. The voice recognition engine improves the recognition accuracy by learning the user's pronunciation and speaking characteristics and using an individually optimized model. The analysis unit can also analyze image data and recognize the contents of photos and videos. For example, image recognition technology is used to identify and tag people and objects in photos. Video data can be analyzed frame by frame to extract important scenes and events. Furthermore, the analysis unit can analyze location information and grasp the user's behavioral patterns. For example, the analysis unit analyzes that the user frequently visits a specific place and evaluates the importance of the place. To analyze behavioral patterns, a machine learning algorithm can be used to extract characteristic patterns from the user's movement history and detect abnormal behavior or new behavior. This allows the analysis unit to analyze the collected data from multiple angles and provide detailed information about the user's life. Furthermore, the analysis unit transmits the analysis results to the storage unit so that they can be referenced later.

[0032] The storage unit stores the data analyzed by the analysis unit. The storage unit stores, for example, the analyzed text data or image data in a database. Specifically, the data is stored in a cloud storage or a local server to ensure data redundancy and security. The storage unit optimizes the data storage format and compression method to efficiently manage large amounts of data. The storage unit can also store the analyzed location information in chronological order. For example, the storage unit stores the user's movement history in chronological order so that it can be referenced later. This allows the user to easily check the places and movement routes that he or she has visited in the past. Furthermore, the storage unit periodically backs up the data to prevent data loss or damage. The storage unit manages access rights to the data and takes appropriate security measures to protect the user's privacy. For example, the storage unit encrypts the data and monitors the access log to prevent unauthorized access. This allows the storage unit to safely and efficiently store the analyzed data and enable the user to quickly obtain the information he or she needs.

[0033] The providing unit provides the data stored by the storage unit in response to an inquiry from a user. For example, when the user makes an inquiry by voice, such as "What did you eat for dinner last night?", the providing unit responds based on the stored data. Specifically, the providing unit converts the user's inquiry into text using voice recognition technology, and searches for and provides related data from the storage unit. The providing unit can also respond to the user's inquiry in an interactive format. For example, when the user makes an inquiry such as "Show me photos of your trip last week," the providing unit displays the stored photo data. The providing unit uses natural language processing technology to understand the content of the user's inquiry and generate an appropriate answer. Furthermore, the providing unit can learn the user's past inquiry history and provide a more accurate answer. For example, the providing unit can prioritize the display of frequently inquired questions by the user or suggest related information. This allows the providing unit to quickly and accurately provide information according to the user's needs and assist the user's memory. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and ease of use of the system. For example, the user evaluates the information provided, and adjusts the system's algorithm based on the evaluation. This allows the providing unit to always provide the most appropriate information to the user, maximizing the effectiveness of the memory assistance system.

[0034] The memory assistance system includes an information collection stop function that stops information collection. The information collection stop function is a function that allows the user to set a time or manually turn off information collection. For example, the user can set a time to stop information collection through a smartphone app. The user can also manually stop information collection. For example, when the user wants to protect privacy, the user can press a button on the app to stop information collection. This allows the user to stop information collection as necessary and protect privacy. Some or all of the above-mentioned processing in the information collection stop function may be performed, for example, using AI, or may be performed without using AI. For example, the information collection stop function allows AI to analyze the user's behavioral patterns and stop information collection at an appropriate timing.

[0035] The memory assistance system includes a security function that ensures the security of data. The security function is a function that prevents unauthorized access to collected data. For example, the security function encrypts and stores collected data. The security function can also perform access control to allow only authorized users to access data. For example, the security function checks user authentication information to allow only authenticated users to access data. This ensures the security of collected data and prevents unauthorized access. Some or all of the above-mentioned processing in the security function may be performed, for example, using AI or may be performed without using AI. For example, the security function can use AI to analyze data access logs and detect signs of unauthorized access.

[0036] The providing unit can receive an inquiry from the user by voice and provide the data stored by the storage unit in a voice dialogue format. For example, when the user makes a voice inquiry such as "What did you eat for dinner last night?", the providing unit responds based on the stored data. The providing unit can also respond to the user's inquiry in a dialogue format. For example, when the user makes an inquiry such as "Show me photos of your trip last week," the providing unit displays the stored photo data. This allows the user to easily look back on past events and memories by making a voice inquiry. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can convert the user's voice into text using voice recognition technology, and the AI ​​can analyze the text to generate an appropriate answer.

[0037] The collection unit can analyze the user's past behavior history and select the optimal data collection method. The collection unit, for example, analyzes the places the user has visited in the past and the behavior pattern and selects the optimal data collection method. For example, in places that the user frequently visits, location information is preferentially collected. In addition, when a user repeats a specific behavior, data related to the behavior can also be preferentially collected. This enables efficient data collection by selecting the optimal data collection method based on the user's past behavior history. 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 inputs the user's behavior history data into the generation AI, and the generation AI can select the optimal data collection method.

[0038] The collection unit can perform filtering based on the user's current activity status when collecting data. For example, when the user is exercising, the collection unit can preferentially collect data related to exercise. Also, when the user is resting, the collection unit can preferentially collect data related to relaxation. This allows more relevant data to be collected by filtering the data to be collected according to the user's current activity status. Some 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 activity data to the generation AI, which can filter the data based on the activity status.

[0039] The collection unit can collect highly relevant data preferentially based on 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 moving, the collection unit can also preferentially collect data related to the moving route. As a result, more useful data can be collected by preferentially collecting highly relevant data based on the geographical location information of the user. 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 inputs the user's location information data to the generation AI, and the generation AI can preferentially collect highly relevant data based on the location information.

[0040] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, the collection unit analyzes the content posted by the user on social media and collects data related to the content. The collection unit can also analyze the activity of accounts the user follows on social media and collect related data. This allows the user's social media activity to be analyzed, thereby efficiently collecting related data. Some 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 social media data to a generation AI, which then analyzes the social media activity and collects related data.

[0041] The analysis unit can improve the accuracy of the analysis based on the interrelationships of the collected data during the analysis. The analysis unit can, for example, analyze the interrelationships between the collected voice data and image data to obtain more accurate analysis results. It can also analyze the interrelationships between the collected location information and behavioral history to grasp the user's behavioral patterns. This improves the accuracy of the analysis by taking into account the interrelationships of the collected data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data to a generation AI, which can analyze the interrelationships of the data.

[0042] The analysis unit can optimize the analysis algorithm based on the user's past behavioral patterns during analysis. The analysis unit, for example, refers to the user's past behavioral patterns and optimizes the analysis algorithm based on the patterns. The analysis unit can also improve the accuracy of the analysis based on the user's past behavioral patterns. As a result, the analysis algorithm is optimized by referring to the user's past behavioral patterns, and the accuracy of the analysis is improved. 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 can input the user's behavioral pattern data to the generation AI, which can optimize the analysis algorithm.

[0043] The analysis unit can perform the analysis while taking into account the temporal changes in the collected data. For example, the analysis unit analyzes the temporal changes in the collected data to grasp the fluctuations in the data. The analysis unit can also provide an analysis result while taking into account the temporal changes in the collected data. This allows for more accurate analysis by taking into account the temporal changes in the collected data. Some 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 collected data to a generation AI, which can analyze the temporal changes in the data.

[0044] The analysis unit can apply different analysis methods to each category of collected data during analysis. For example, the analysis unit applies a voice analysis method to voice data and an image analysis method to image data. Also, the analysis unit can apply a location information analysis method to location information. This improves the accuracy of analysis by applying different analysis methods to each category of collected data. Some 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 inputs collected data to a generation AI, which can apply different analysis methods to each category.

[0045] The storage unit can adjust the level of detail of the storage based on the importance of the data when storing. For example, the storage unit stores important data with a high level of detail and stores less important data with a low level of detail. The storage unit can also adjust the compression rate of the data to efficiently use the storage capacity. This allows the storage capacity to be used efficiently by adjusting the level of detail of the storage based on the importance of the data. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the importance of the data to a generation AI, which can adjust the level of detail of the storage.

[0046] The storage unit can apply different storage algorithms depending on the category of data when storing. For example, the storage unit applies an audio compression algorithm to audio data and an image compression algorithm to image data. Also, the storage unit can apply a location information storage algorithm to location information. This improves storage efficiency by applying different storage algorithms depending on the category of data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the category of data to the generation AI, and the generation AI can apply a storage algorithm according to the category.

[0047] The storage unit can determine the priority of storage based on the time of data submission when storing. For example, the storage unit prioritizes storing the latest data and postpones storing old data. The storage unit can also adjust the data storage period based on the time of submission. In this way, by determining the priority of storage based on the time of data submission, the latest data can be stored with priority. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the time of data submission to the generation AI, and the generation AI can determine the priority of storage.

[0048] The storage unit can adjust the order of storage based on the relevance of the data when storing. For example, the storage unit prioritizes storing highly relevant data and postpones storing less relevant data. The storage unit can also adjust the level of detail of storage based on the relevance of the data. This allows highly relevant data to be stored preferentially by adjusting the order of storage based on the relevance of the data. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the relevance of the data to the generation AI, which can adjust the order of storage.

[0049] The providing unit can adjust the level of detail of the data provided based on the importance of the data at the time of providing. For example, the providing unit provides important data in detail and provides less important data simply. The providing unit can also adjust the granularity of the data so that the user can efficiently obtain the information required. As a result, the level of detail of the data provided can be adjusted based on the importance of the data, allowing the user to efficiently obtain the information required. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the importance of the data to the generating AI, and the generating AI can adjust the level of detail of the data provided.

[0050] The providing unit can apply different providing algorithms depending on the category of data at the time of providing. For example, the providing unit applies a voice providing algorithm to voice data and an image providing algorithm to image data. Also, the providing unit can apply a location information providing algorithm to location information. This improves the efficiency of providing by applying different providing algorithms depending on the category of data. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of data to the generating AI, and the generating AI can apply a providing algorithm according to the category.

[0051] The providing unit can determine the priority of provision based on the time of data submission at the time of provision. For example, the providing unit provides the latest data with priority, and older data with priority. The providing unit can also adjust the data provision period based on the time of submission. In this way, the latest data can be provided with priority by determining the priority of provision based on the time of data submission. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of data submission to the generating AI, and the generating AI can determine the priority of provision.

[0052] The providing unit can adjust the order of provision based on the relevance of the data at the time of provision. For example, the providing unit provides highly relevant data preferentially and postpones less relevant data. The providing unit can also adjust the level of detail of the provision based on the relevance of the data. As a result, highly relevant data can be provided preferentially by adjusting the order of provision based on the relevance of the data. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the relevance of the data to the generating AI, and the generating AI can adjust the order of provision.

[0053] The off function can select the optimal timing to turn off information collection in consideration of the user's current activity status. For example, when the user is in a meeting, the off function turns off information collection in consideration of the activity status. Also, when the user is exercising, the off function can continue information collection in consideration of the activity status. This allows information collection to be stopped at an appropriate time by turning off information collection in consideration of the user's current activity status. Some or all of the above-mentioned processing in the off function may be performed, for example, using AI or may be performed without using AI. For example, the off function can input the user's activity status data into the generation AI, and the generation AI can select the timing to turn off information collection based on the activity status.

[0054] The security function can select the optimal security measure in consideration of the user's device information to ensure the security of data. The security function selects an appropriate security measure in consideration of, for example, the type and characteristics of the device used by the user. The security function can also automatically adjust the security settings of the device. This makes it possible to enhance the safety of data by selecting the optimal security measure in consideration of the user's device information. Some or all of the above-mentioned processing in the security function may be performed, for example, using AI or may be performed without using AI. For example, the security function can input the user's device information into a generation AI, which can select the optimal security measure.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The memory assistance system may include a health management unit that collects and analyzes the health data of the user. The health management unit collects and analyzes vital data such as the user's heart rate, blood pressure, and body temperature. The health management unit may also monitor the user's exercise amount and sleep pattern to evaluate the health condition. This allows the user's health condition to be understood, and the user may be contacted or advice may be provided to a medical institution if necessary. For example, if the user's heart rate is abnormally high, the health management unit may issue a warning and prompt the user to contact a medical institution. If the user's sleep pattern is disrupted, the health management unit may also provide advice for improvement. The health management unit may also analyze the user's exercise amount and propose an appropriate exercise plan.

[0057] The memory assistance system may include a hobby analysis unit that collects data based on the user's hobbies and interests. The hobby analysis unit may collect and analyze information on, for example, music, movies, sports, and the like that the user likes. The hobby analysis unit may also provide events and news related to the user's hobbies. This allows more personalized information to be provided based on the user's hobbies and interests. For example, when a new song is released by a music artist that the user likes, the hobby analysis unit may notify the user of the information. The hobby analysis unit may also provide schedules of sporting events that the user is interested in. The hobby analysis unit may also introduce communities and groups related to the user's hobbies.

[0058] The memory assistance system may include a schedule management unit that manages the user's schedule and reminds the user of important appointments. The schedule management unit, for example, collects calendar information of the user and reminds the user of important appointments. The schedule management unit may also provide reminders at appropriate times based on the user's schedule. This allows the user to live a smooth daily life without forgetting important appointments. For example, if the user has a meeting scheduled, the schedule management unit may provide a reminder before the start time of the meeting. If the user has an appointment at a medical institution, the schedule management unit may also provide a reminder the day before the appointment. Furthermore, the schedule management unit may suggest appropriate break times based on the user's schedule.

[0059] The memory assistance system may include a communication assistance unit that assists the user in communicating with family and friends. The communication assistance unit, for example, manages messages and call history with the user's family and friends, and reminds the user of important messages. The communication assistance unit may also suggest an appropriate communication method based on the user's emotions. This allows the user to communicate smoothly with family and friends. For example, if the user forgets to call a family member, the communication assistance unit may remind the user of the call. If the user forgets to reply to a message from a friend, the communication assistance unit may remind the user to reply. Furthermore, the communication assistance unit may also suggest communication with family and friends at an appropriate time based on the user's emotions.

[0060] The memory assistance system may include a learning support unit that supports the user's learning. The learning support unit may, for example, suggest a learning plan based on the content that the user wants to learn. The learning support unit may also monitor the user's learning progress and suggest review or additional learning at an appropriate time. This allows the user to progress with learning efficiently. For example, if the user wants to learn a new language, the learning support unit may suggest a language learning plan. If the user is studying for a qualification exam, the learning support unit may also suggest review before the exam. Furthermore, the learning support unit may provide appropriate learning resources based on the user's learning progress.

[0061] The memory assistance system may include a travel support unit that supports the user's travel plans. The travel support unit, for example, collects information on the user's travel destination and suggests a travel plan. The travel support unit may also manage the user's schedule during the travel and remind the user of important plans. This allows the user to enjoy the travel smoothly. For example, when the user visits a tourist spot at the travel destination, the travel support unit may provide information on the spot. When the user forgets the time of a restaurant reservation made during the travel, the travel support unit may also remind the user to make the reservation. Furthermore, the travel support unit may provide weather information and traffic information for the user's travel destination.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects voice data, photo / video data, and location information related to the user. For example, the voice of the user speaking is collected by a microphone and saved as voice data. In addition, photos and videos taken by the user can be collected and saved as image data. Furthermore, the user's location information can be collected using GPS or Wi-Fi location information. For example, when the user carries a smartphone, GPS data is collected and the user's movement history is recorded. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes voice data and converts the user's speech into text data. It can also analyze image data and recognize the contents of photos and videos. It can also analyze location information and understand the user's behavioral patterns. For example, it can analyze whether a user frequently visits a particular place and evaluate the importance of that place. Step 3: The storage unit stores the data analyzed by the analysis unit. For example, the analyzed text data or image data is stored in a database. The storage unit can also store the analyzed location information in chronological order. For example, the user's movement history can be stored in chronological order for later reference. Step 4: The providing unit provides the data stored by the storage unit in response to an inquiry from the user. For example, if the user makes a voice inquiry such as "What did you have for dinner last night?", the providing unit responds based on the stored data. It is also possible to respond to user inquiries in an interactive format. For example, if the user makes an inquiry such as "Show me photos from last week's trip," the stored photo data is displayed.

[0064] (Example 2) A memory assistance system according to an embodiment of the present invention is a system for supporting people with Alzheimer's disease or dementia. This memory assistance system collects voice data, photo and video data, location information, and the like from the user's daily life, and analyzes and stores the data. The user can easily look back on past events and memories by making voice inquiries to the system. In addition, the information collection function can be turned off by setting a time or manually, and data security is also ensured. For example, the memory assistance system includes a collection unit that collects voice data, photo and video data, and location information about the user. This collection unit estimates the user's emotions and adjusts the type and priority of data to be collected based on the emotions. For example, when the user is having fun, photos and videos of that moment are collected preferentially. In addition, the user's past behavior history, current activity status, geographical location information, social media activity, and the like are analyzed, and an appropriate data collection method is selected. Next, the collected data is analyzed by the analysis unit. The analysis unit analyzes the collected data in detail and grasps the user's behavior and emotional patterns. For example, if a user repeats a specific behavior in a specific place, the pattern is analyzed and recorded. The analyzed data is stored in the storage unit. The storage unit securely stores the analyzed data and provides it to the providing unit as necessary. The stored data is provided by the providing unit in response to a user's inquiry. The providing unit accepts voice inquiries from the user and provides the stored data in an interactive format. For example, when a user inquires, "What did you eat for dinner last night?", the providing unit responds based on the stored data. In addition, the system has an off function that turns off information collection. Users can turn off the information collection function by setting a time or manually, protecting privacy. In addition, the system has a security function that ensures data security and prevents unauthorized access to collected data. This allows people with Alzheimer's and dementia to easily look back on memories and daily events that they do not want to forget, improving their quality of life. This allows the memory assistance system to support people with Alzheimer's and dementia, and to easily look back on memories and daily events that they do not want to forget.

[0065] The memory assistance system according to the embodiment includes a collection unit, an analysis unit, a storage unit, and a provision unit. The collection unit collects voice data, photo / video data, and location information related to the user. For example, the collection unit collects the voice of the user using a microphone and stores it as voice data. The collection unit can also collect photos and videos taken by the user and store them as image data. Furthermore, the collection unit can collect the user's location information using GPS or Wi-Fi location information. For example, when the user carries a smartphone, the collection unit collects GPS data and records the user's movement history. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes voice data and converts the user's remarks into text data. The analysis unit can also analyze image data and recognize the contents of the photos and videos. Furthermore, the analysis unit can analyze location information and grasp the user's behavior pattern. For example, the analysis unit analyzes that the user frequently visits a specific place and evaluates the importance of the place. The storage unit stores the data analyzed by the analysis unit. The storage unit stores, for example, the analyzed text data and image data in a database. The storage unit can also store the analyzed location information in chronological order. For example, the storage unit stores the user's movement history in chronological order so that it can be referenced later. The providing unit provides the data stored by the storage unit in response to an inquiry from the user. For example, when the user makes a voice inquiry such as "What did you eat for dinner last night?", the providing unit responds based on the stored data. The providing unit can also respond to the user's inquiry in an interactive format. For example, when the user makes an inquiry such as "Show me photos of your trip last week," the providing unit displays the stored photo data. As a result, the memory assistance system according to the embodiment can support people with Alzheimer's disease and dementia by collecting, analyzing, storing, and providing data from the user's daily life.

[0066] The collection unit collects voice data, photo / video data, and location information related to the user. For example, the collection unit collects the voice of the user using a microphone and saves it as voice data. Specifically, the collection unit uses a high-sensitivity microphone built into a smartphone or wearable device to clearly collect the user's remarks. The voice data is recorded clearly by removing background sounds using noise reduction technology. The collection unit can also collect photos and videos taken by the user and save them as image data. For example, photos and videos taken with a smartphone camera or digital camera are automatically uploaded to cloud storage and centrally managed by the collection unit. Furthermore, the collection unit can also collect the user's location information using GPS or Wi-Fi location information. For example, when the user carries a smartphone, the collection unit collects GPS data and records the user's movement history. Wi-Fi location information is effective for identifying indoor locations and provides accurate location information even in places where GPS signals cannot reach. This allows the collection unit to comprehensively collect the user's voice, image, and location information and create a detailed record of the user's daily life. Furthermore, the collection unit collects this data in real time and immediately transmits it to the analysis unit and storage unit, thereby improving the efficiency of the entire system.

[0067] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes voice data and converts the user's speech into text data. Specifically, the analysis unit converts the voice data into text with high accuracy using voice recognition technology. The voice recognition engine improves the recognition accuracy by learning the user's pronunciation and speaking characteristics and using an individually optimized model. The analysis unit can also analyze image data and recognize the contents of photos and videos. For example, image recognition technology is used to identify and tag people and objects in photos. Video data can be analyzed frame by frame to extract important scenes and events. Furthermore, the analysis unit can analyze location information and grasp the user's behavioral patterns. For example, the analysis unit analyzes that the user frequently visits a specific place and evaluates the importance of the place. To analyze behavioral patterns, a machine learning algorithm can be used to extract characteristic patterns from the user's movement history and detect abnormal behavior or new behavior. This allows the analysis unit to analyze the collected data from multiple angles and provide detailed information about the user's life. Furthermore, the analysis unit transmits the analysis results to the storage unit so that they can be referenced later.

[0068] The storage unit stores the data analyzed by the analysis unit. The storage unit stores, for example, the analyzed text data or image data in a database. Specifically, the data is stored in a cloud storage or a local server to ensure data redundancy and security. The storage unit optimizes the data storage format and compression method to efficiently manage large amounts of data. The storage unit can also store the analyzed location information in chronological order. For example, the storage unit stores the user's movement history in chronological order so that it can be referenced later. This allows the user to easily check the places and movement routes that he or she has visited in the past. Furthermore, the storage unit periodically backs up the data to prevent data loss or damage. The storage unit manages access rights to the data and takes appropriate security measures to protect the user's privacy. For example, the storage unit encrypts the data and monitors the access log to prevent unauthorized access. This allows the storage unit to safely and efficiently store the analyzed data and enable the user to quickly obtain the information he or she needs.

[0069] The providing unit provides the data stored by the storage unit in response to an inquiry from a user. For example, when the user makes an inquiry by voice, such as "What did you eat for dinner last night?", the providing unit responds based on the stored data. Specifically, the providing unit converts the user's inquiry into text using voice recognition technology, and searches for and provides related data from the storage unit. The providing unit can also respond to the user's inquiry in an interactive format. For example, when the user makes an inquiry such as "Show me photos of your trip last week," the providing unit displays the stored photo data. The providing unit uses natural language processing technology to understand the content of the user's inquiry and generate an appropriate answer. Furthermore, the providing unit can learn the user's past inquiry history and provide a more accurate answer. For example, the providing unit can prioritize the display of frequently inquired questions by the user or suggest related information. This allows the providing unit to quickly and accurately provide information according to the user's needs and assist the user's memory. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and ease of use of the system. For example, the user evaluates the information provided, and adjusts the system's algorithm based on the evaluation. This allows the providing unit to always provide the most appropriate information to the user, maximizing the effectiveness of the memory assistance system.

[0070] The memory assistance system includes an information collection stop function that stops information collection. The information collection stop function is a function that allows the user to set a time or manually turn off information collection. For example, the user can set a time to stop information collection through a smartphone app. The user can also manually stop information collection. For example, when the user wants to protect privacy, the user can press a button on the app to stop information collection. This allows the user to stop information collection as necessary and protect privacy. Some or all of the above-mentioned processing in the information collection stop function may be performed, for example, using AI, or may be performed without using AI. For example, the information collection stop function allows AI to analyze the user's behavioral patterns and stop information collection at an appropriate timing.

[0071] The memory assistance system includes a security function that ensures the security of data. The security function is a function that prevents unauthorized access to collected data. For example, the security function encrypts and stores collected data. The security function can also perform access control to allow only authorized users to access data. For example, the security function checks user authentication information to allow only authenticated users to access data. This ensures the security of collected data and prevents unauthorized access. Some or all of the above-mentioned processing in the security function may be performed, for example, using AI or may be performed without using AI. For example, the security function can use AI to analyze data access logs and detect signs of unauthorized access.

[0072] The providing unit can receive an inquiry from the user by voice and provide the data stored by the storage unit in a voice dialogue format. For example, when the user makes a voice inquiry such as "What did you eat for dinner last night?", the providing unit responds based on the stored data. The providing unit can also respond to the user's inquiry in a dialogue format. For example, when the user makes an inquiry such as "Show me photos of your trip last week," the providing unit displays the stored photo data. This allows the user to easily look back on past events and memories by making a voice inquiry. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can convert the user's voice into text using voice recognition technology, and the AI ​​can analyze the text to generate an appropriate answer.

[0073] The collection unit can estimate the user's emotion and adjust the type of data to be collected based on the emotion. For example, when the user is having fun, the collection unit preferentially collects photos and videos of that moment. Also, when the user is sad, the collection unit can preferentially collect voice data for recording the emotion. This allows more appropriate data to be collected by adjusting the type of data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI, which can estimate the user's emotion and adjust the type of data to be collected based on the emotion.

[0074] The collection unit can analyze the user's past behavior history and select the optimal data collection method. The collection unit, for example, analyzes the places the user has visited in the past and the behavior pattern and selects the optimal data collection method. For example, in places that the user frequently visits, location information is preferentially collected. In addition, when a user repeats a specific behavior, data related to the behavior can also be preferentially collected. This enables efficient data collection by selecting the optimal data collection method based on the user's past behavior history. 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 inputs the user's behavior history data into the generation AI, and the generation AI can select the optimal data collection method.

[0075] The collection unit can perform filtering based on the user's current activity status when collecting data. For example, when the user is exercising, the collection unit can preferentially collect data related to exercise. Also, when the user is resting, the collection unit can preferentially collect data related to relaxation. This allows more relevant data to be collected by filtering the data to be collected according to the user's current activity status. Some 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 activity data to the generation AI, which can filter the data based on the activity status.

[0076] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the emotion. For example, when the user is having fun, the collection unit prioritizes collecting photos and videos of that moment. Also, when the user is sad, the collection unit can prioritize collecting audio data to record the emotion. In this way, important data can be collected preferentially by determining the priority of data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI, which can estimate the user's emotion and determine the priority of data to be collected based on the emotion.

[0077] The collection unit can collect highly relevant data preferentially based on 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 moving, the collection unit can also preferentially collect data related to the moving route. As a result, more useful data can be collected by preferentially collecting highly relevant data based on the geographical location information of the user. 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 inputs the user's location information data to the generation AI, and the generation AI can preferentially collect highly relevant data based on the location information.

[0078] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, the collection unit analyzes the content posted by the user on social media and collects data related to the content. The collection unit can also analyze the activity of accounts the user follows on social media and collect related data. This allows the user's social media activity to be analyzed, thereby efficiently collecting related data. Some 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 social media data to a generation AI, which then analyzes the social media activity and collects related data.

[0079] The analysis unit can estimate the user's emotion and adjust the analysis method based on the emotion. For example, when the user is having fun, the analysis unit analyzes the data taking the emotion into consideration. Also, when the user is sad, the analysis unit can analyze the data taking the emotion into consideration. This allows for more accurate analysis by adjusting the analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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, an AI, or may be performed without using an AI. For example, the analysis unit inputs the user's emotion data to the generation AI, and the generation AI adjusts the analysis method based on the emotion.

[0080] The analysis unit can improve the accuracy of the analysis based on the interrelationships of the collected data during the analysis. The analysis unit can, for example, analyze the interrelationships between the collected voice data and image data to obtain more accurate analysis results. It can also analyze the interrelationships between the collected location information and behavioral history to grasp the user's behavioral patterns. This improves the accuracy of the analysis by taking into account the interrelationships of the collected data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data to a generation AI, which can analyze the interrelationships of the data.

[0081] The analysis unit can optimize the analysis algorithm based on the user's past behavioral patterns during analysis. The analysis unit, for example, refers to the user's past behavioral patterns and optimizes the analysis algorithm based on the patterns. The analysis unit can also improve the accuracy of the analysis based on the user's past behavioral patterns. As a result, the analysis algorithm is optimized by referring to the user's past behavioral patterns, and the accuracy of the analysis is improved. 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 can input the user's behavioral pattern data to the generation AI, which can optimize the analysis algorithm.

[0082] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the emotion. For example, when the user is having fun, the analysis unit can display the analysis result taking into account the emotion. Also, when the user is sad, the analysis result can be displayed taking into account the emotion. This allows the display method of the analysis result to be adjusted according to the user's emotion, making it possible to display the analysis result in a way that is easy for the user to see. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can adjust the display method of the analysis result based on the emotion.

[0083] The analysis unit can perform the analysis while taking into account the temporal changes in the collected data. For example, the analysis unit analyzes the temporal changes in the collected data to grasp the fluctuations in the data. The analysis unit can also provide an analysis result while taking into account the temporal changes in the collected data. This allows for more accurate analysis by taking into account the temporal changes in the collected data. Some 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 collected data to a generation AI, which can analyze the temporal changes in the data.

[0084] The analysis unit can apply different analysis methods to each category of collected data during analysis. For example, the analysis unit applies a voice analysis method to voice data and an image analysis method to image data. Also, the analysis unit can apply a location information analysis method to location information. This improves the accuracy of analysis by applying different analysis methods to each category of collected data. Some 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 inputs collected data to a generation AI, which can apply different analysis methods to each category.

[0085] The storage unit can estimate the user's emotion and determine the priority of data to be stored based on the emotion. For example, when the user is having fun, the storage unit can preferentially store data taking into account the emotion. Also, when the user is sad, the storage unit can preferentially store data taking into account the emotion. In this way, important data can be preferentially stored by determining the priority of data to be stored according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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 storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of data to be stored based on the emotion.

[0086] The storage unit can adjust the level of detail of the storage based on the importance of the data when storing. For example, the storage unit stores important data with a high level of detail and stores less important data with a low level of detail. The storage unit can also adjust the compression rate of the data to efficiently use the storage capacity. This allows the storage capacity to be used efficiently by adjusting the level of detail of the storage based on the importance of the data. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the importance of the data to a generation AI, which can adjust the level of detail of the storage.

[0087] The storage unit can apply different storage algorithms depending on the category of data when storing. For example, the storage unit applies an audio compression algorithm to audio data and an image compression algorithm to image data. Also, the storage unit can apply a location information storage algorithm to location information. This improves storage efficiency by applying different storage algorithms depending on the category of data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the category of data to the generation AI, and the generation AI can apply a storage algorithm according to the category.

[0088] The storage unit can estimate the user's emotion and adjust the access authority of the stored data based on the emotion. For example, when the user is having fun, the storage unit relaxes the access authority of the data taking into account the emotion. Also, when the user is sad, the storage unit can tighten the access authority of the data taking into account the emotion. In this way, the access authority of the stored data can be adjusted according to the user's emotion, thereby enhancing the security of the data. 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 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 storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI, and the generation AI can adjust the access authority of the stored data based on the emotion.

[0089] The storage unit can determine the priority of storage based on the time of data submission when storing. For example, the storage unit prioritizes storing the latest data and postpones storing old data. The storage unit can also adjust the data storage period based on the time of submission. In this way, by determining the priority of storage based on the time of data submission, the latest data can be stored with priority. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the time of data submission to the generation AI, and the generation AI can determine the priority of storage.

[0090] The storage unit can adjust the order of storage based on the relevance of the data when storing. For example, the storage unit prioritizes storing highly relevant data and postpones storing less relevant data. The storage unit can also adjust the level of detail of storage based on the relevance of the data. This allows highly relevant data to be stored preferentially by adjusting the order of storage based on the relevance of the data. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the relevance of the data to the generation AI, which can adjust the order of storage.

[0091] The providing unit can estimate the user's emotion and adjust the way of expressing the data to be provided based on the emotion. For example, when the user is having fun, the providing unit expresses the data in a bright manner taking into account the emotion. Also, when the user is sad, the providing unit can express the data in a calm manner taking into account the emotion. This allows the display to be easy for the user to see by adjusting the way of expressing the data to be provided according to the user's emotion. 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 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and adjust the way of expressing the data provided by the generation AI based on the emotion.

[0092] The providing unit can adjust the level of detail of the data provided based on the importance of the data at the time of providing. For example, the providing unit provides important data in detail and provides less important data simply. The providing unit can also adjust the granularity of the data so that the user can efficiently obtain the information required. As a result, the level of detail of the data provided can be adjusted based on the importance of the data, allowing the user to efficiently obtain the information required. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the importance of the data to the generating AI, and the generating AI can adjust the level of detail of the data provided.

[0093] The providing unit can apply different providing algorithms depending on the category of data at the time of providing. For example, the providing unit applies a voice providing algorithm to voice data and an image providing algorithm to image data. Also, the providing unit can apply a location information providing algorithm to location information. This improves the efficiency of providing by applying different providing algorithms depending on the category of data. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of data to the generating AI, and the generating AI can apply a providing algorithm according to the category.

[0094] The providing unit can estimate the user's emotion and adjust the length of the data to be provided based on the emotion. For example, when the user is having fun, the providing unit can increase the length of the data taking into account the emotion. Also, when the user is sad, the providing unit can also shorten the length of the data taking into account the emotion. This allows optimal information to be provided to the user by adjusting the length of the data to be provided according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and adjust the length of the data to be provided by the generation AI based on the emotion.

[0095] The providing unit can determine the priority of provision based on the time of data submission at the time of provision. For example, the providing unit provides the latest data with priority, and older data with priority. The providing unit can also adjust the data provision period based on the time of submission. In this way, the latest data can be provided with priority by determining the priority of provision based on the time of data submission. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of data submission to the generating AI, and the generating AI can determine the priority of provision.

[0096] The providing unit can adjust the order of provision based on the relevance of the data at the time of provision. For example, the providing unit provides highly relevant data preferentially and postpones less relevant data. The providing unit can also adjust the level of detail of the provision based on the relevance of the data. As a result, highly relevant data can be provided preferentially by adjusting the order of provision based on the relevance of the data. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the relevance of the data to the generating AI, and the generating AI can adjust the order of provision.

[0097] The off function can estimate the user's emotion and adjust the timing to turn off information collection based on the emotion. For example, when the user is feeling stressed, the off function turns off information collection in consideration of the emotion. Also, when the user is relaxed, the off function can continue information collection in consideration of the emotion. This allows appropriate information collection while protecting privacy by adjusting the timing to turn off information collection according to the user's emotion. The emotion estimation is realized using the emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 off function may be performed, for example, using an AI, or may be performed without using an AI. For example, the off function can input the user's emotion data into the generation AI and adjust the timing for the generation AI to turn off information collection based on the emotion.

[0098] The off function can select the optimal timing to turn off information collection in consideration of the user's current activity status. For example, when the user is in a meeting, the off function turns off information collection in consideration of the activity status. Also, when the user is exercising, the off function can continue information collection in consideration of the activity status. This allows information collection to be stopped at an appropriate time by turning off information collection in consideration of the user's current activity status. Some or all of the above-mentioned processing in the off function may be performed, for example, using AI or may be performed without using AI. For example, the off function can input the user's activity status data into the generation AI, and the generation AI can select the timing to turn off information collection based on the activity status.

[0099] The security function can estimate the user's emotions and adjust the security level of the data based on the emotions. For example, when the user feels anxious, the security function can increase the security level of the data taking into account the emotions. Also, when the user feels relieved, the security level of the data can be relaxed taking into account the emotions. This can ensure the safety of the data by adjusting the security level of the data according to the user's emotions. The emotion estimation is realized using the emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 security function may be performed, for example, using an AI, or may be performed without using an AI. For example, the security function can input the user's emotion data to the generation AI, and the generation AI can adjust the security level of the data based on the emotion.

[0100] The security function can select the optimal security measure in consideration of the user's device information to ensure the security of data. The security function selects an appropriate security measure in consideration of, for example, the type and characteristics of the device used by the user. The security function can also automatically adjust the security settings of the device. This makes it possible to enhance the safety of data by selecting the optimal security measure in consideration of the user's device information. Some or all of the above-mentioned processing in the security function may be performed, for example, using AI or may be performed without using AI. For example, the security function can input the user's device information into a generation AI, which can select the optimal security measure.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The memory assistance system may include a health management unit that collects and analyzes the health data of the user. The health management unit collects and analyzes vital data such as the user's heart rate, blood pressure, and body temperature. The health management unit may also monitor the user's exercise amount and sleep pattern to evaluate the health condition. This allows the user's health condition to be understood, and the user may be contacted or advice may be provided to a medical institution if necessary. For example, if the user's heart rate is abnormally high, the health management unit may issue a warning and prompt the user to contact a medical institution. If the user's sleep pattern is disrupted, the health management unit may also provide advice for improvement. The health management unit may also analyze the user's exercise amount and propose an appropriate exercise plan.

[0103] The memory assistance system may include a hobby analysis unit that collects data based on the user's hobbies and interests. The hobby analysis unit may collect and analyze information on, for example, music, movies, sports, and the like that the user likes. The hobby analysis unit may also provide events and news related to the user's hobbies. This allows more personalized information to be provided based on the user's hobbies and interests. For example, when a new song is released by a music artist that the user likes, the hobby analysis unit may notify the user of the information. The hobby analysis unit may also provide schedules of sporting events that the user is interested in. The hobby analysis unit may also introduce communities and groups related to the user's hobbies.

[0104] The memory assistance system may include a relaxation suggestion unit that estimates the user's emotions and suggests a relaxation method based on the emotions. For example, when the user is feeling stressed, the relaxation suggestion unit provides music or a meditation guide for relaxation. Also, when the user is relaxed, the relaxation suggestion unit may provide advice for maintaining the state. This makes it possible to reduce stress and promote relaxation by suggesting an appropriate relaxation method according to the user's emotions. For example, when the user is feeling stressed, the relaxation suggestion unit may play relaxing music. Also, when the user is relaxed, the relaxation suggestion unit may provide a meditation guide for maintaining the state. Furthermore, the relaxation suggestion unit may suggest aromatherapy or massage for relaxation based on the user's emotions.

[0105] The memory assistance system may include a meal suggestion unit that estimates the user's emotions and suggests meals based on the emotions. For example, when the user is having fun, the meal suggestion unit suggests a fun meal taking into account the user's emotions. Also, when the user is sad, the meal suggestion unit may suggest a meal to lift the user's mood taking into account the user's emotions. This increases the enjoyment of meals by suggesting appropriate meals according to the user's emotions. For example, when the user is having fun, the meal suggestion unit may suggest party dishes and desserts. Also, when the user is sad, the meal suggestion unit may suggest a nutritionally balanced meal to lift the user's mood. Furthermore, the meal suggestion unit may suggest relaxing herbal teas and smoothies based on the user's emotions.

[0106] The memory assistance system may include an exercise suggestion unit that estimates the user's emotions and suggests an exercise plan based on the emotions. For example, when the user is feeling stressed, the exercise suggestion unit suggests an exercise plan for relieving stress. Also, when the user is relaxed, it can suggest a light exercise plan for maintaining that state. This makes it possible to maintain health and relieve stress by suggesting an appropriate exercise plan according to the user's emotions. For example, when the user is feeling stressed, the exercise suggestion unit can suggest a plan for yoga or stretching. Also, when the user is relaxed, it can suggest a plan for light walking or exercise with a relaxing effect. Furthermore, the exercise suggestion unit can suggest outdoor activities with a refreshing effect based on the user's emotions.

[0107] The memory assistance system may include a schedule management unit that manages the user's schedule and reminds the user of important appointments. The schedule management unit, for example, collects calendar information of the user and reminds the user of important appointments. The schedule management unit may also provide reminders at appropriate times based on the user's schedule. This allows the user to live a smooth daily life without forgetting important appointments. For example, if the user has a meeting scheduled, the schedule management unit may provide a reminder before the start time of the meeting. If the user has an appointment at a medical institution, the schedule management unit may also provide a reminder the day before the appointment. Furthermore, the schedule management unit may suggest appropriate break times based on the user's schedule.

[0108] The memory assistance system may include a communication assistance unit that assists the user in communicating with family and friends. The communication assistance unit, for example, manages messages and call history with the user's family and friends, and reminds the user of important messages. The communication assistance unit may also suggest an appropriate communication method based on the user's emotions. This allows the user to communicate smoothly with family and friends. For example, if the user forgets to call a family member, the communication assistance unit may remind the user of the call. If the user forgets to reply to a message from a friend, the communication assistance unit may remind the user to reply. Furthermore, the communication assistance unit may also suggest communication with family and friends at an appropriate time based on the user's emotions.

[0109] The memory assistance system may include a learning support unit that supports the user's learning. The learning support unit may, for example, suggest a learning plan based on the content that the user wants to learn. The learning support unit may also monitor the user's learning progress and suggest review or additional learning at an appropriate time. This allows the user to progress with learning efficiently. For example, if the user wants to learn a new language, the learning support unit may suggest a language learning plan. If the user is studying for a qualification exam, the learning support unit may also suggest review before the exam. Furthermore, the learning support unit may provide appropriate learning resources based on the user's learning progress.

[0110] The memory assistance system may include a travel support unit that supports the user's travel plans. The travel support unit, for example, collects information on the user's travel destination and suggests a travel plan. The travel support unit may also manage the user's schedule during the travel and remind the user of important plans. This allows the user to enjoy the travel smoothly. For example, when the user visits a tourist spot at the travel destination, the travel support unit may provide information on the spot. When the user forgets the time of a restaurant reservation made during the travel, the travel support unit may also remind the user to make the reservation. Furthermore, the travel support unit may provide weather information and traffic information for the user's travel destination.

[0111] The memory assistance system may include a music suggestion unit that estimates the user's emotions and suggests music based on the emotions. For example, when the user is having fun, the music suggestion unit suggests fun music taking into account the emotions. Also, when the user is sad, the music suggestion unit can suggest music to lift the user's mood taking into account the emotions. This makes it possible to improve the user's mood by suggesting appropriate music according to the user's emotions. For example, when the user is having fun, the music suggestion unit can suggest upbeat, fun music. Also, when the user is sad, the music suggestion unit can suggest positive music to lift the user's mood. Furthermore, the music suggestion unit can suggest music that has a relaxing effect or music that enhances concentration based on the user's emotions.

[0112] The process flow of the second embodiment will be briefly described below.

[0113] Step 1: The collection unit collects voice data, photo / video data, and location information related to the user. For example, the voice of the user speaking is collected by a microphone and saved as voice data. In addition, photos and videos taken by the user can be collected and saved as image data. Furthermore, the user's location information can be collected using GPS or Wi-Fi location information. For example, when the user carries a smartphone, GPS data is collected and the user's movement history is recorded. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes voice data and converts the user's speech into text data. It can also analyze image data and recognize the contents of photos and videos. It can also analyze location information and understand the user's behavioral patterns. For example, it can analyze whether a user frequently visits a particular place and evaluate the importance of that place. Step 3: The storage unit stores the data analyzed by the analysis unit. For example, the analyzed text data or image data is stored in a database. The storage unit can also store the analyzed location information in chronological order. For example, the user's movement history can be stored in chronological order for later reference. Step 4: The providing unit provides the data stored by the storage unit in response to an inquiry from the user. For example, if the user makes a voice inquiry such as "What did you have for dinner last night?", the providing unit responds based on the stored data. It is also possible to respond to user inquiries in an interactive format. For example, if the user makes an inquiry such as "Show me photos from last week's trip," the stored photo data is displayed.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] For example, the collection unit collects the user's voice data and photo / video data using the camera 42 and microphone 38B of the smart device 14, and acquires location information using the GPS function. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the user's behavior and emotional patterns. The storage unit stores the analyzed data in the database 24 of the data processing device 12. The provision unit is realized by the control unit 46A of the smart device 14, and provides the stored data in response to the user's voice inquiry. The information collection stop function is realized through the application of the smart device 14, and the user can stop information collection manually or by setting a time. The security function is realized by the specific processing unit 290 of the data processing device 12, and encrypts the collected data and performs access control. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] For example, the collection unit collects the user's voice data and photo / video data using the camera 42 and microphone 238 of the smart glasses 214, and acquires location information using the GPS function. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the user's behavior and emotional patterns. The storage unit stores the analyzed data in the database 24 of the data processing device 12. The provision unit is realized by the control unit 46A of the smart glasses 214, and provides the stored data in response to the user's voice inquiry. The information collection stop function is realized through the application of the smart glasses 214, and the user can stop information collection manually or by time setting. The security function is realized by the specific processing unit 290 of the data processing device 12, and encrypts the collected data and performs access control. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] For example, the collection unit collects the user's voice data and photo / video data using the camera 42 and microphone 238 of the headset type terminal 314, and acquires location information using the GPS function. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the user's behavior and emotional patterns. The storage unit stores the analyzed data in the database 24 of the data processing device 12. The provision unit is realized by the control unit 46A of the headset type terminal 314, and provides the stored data in response to the user's voice inquiry. The information collection stop function is realized through the application of the headset type terminal 314, and the user can stop information collection manually or by setting a time. The security function is realized by the specific processing unit 290 of the data processing device 12, and encrypts the collected data and performs access control. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] For example, the collection unit collects the user's voice data and photo / video data using the camera 42 and microphone 238 of the robot 414, and acquires location information using the GPS function. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the user's behavior and emotional patterns. The storage unit stores the analyzed data in the database 24 of the data processing device 12. The provision unit is realized by the control unit 46A of the robot 414, and provides the stored data in response to the user's voice inquiry. The information collection stop function is realized through the application of the robot 414, and the user can stop information collection manually or by setting a time. The security function is realized by the specific processing unit 290 of the data processing device 12, and encrypts the collected data and performs access control. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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 in which a processor is used 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.

[0181] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Appendix 1) A collection unit that collects voice data, image data, and location information related to a user; an analysis unit that analyzes the data collected by the collection unit; A storage unit for storing the data analyzed by the analysis unit; A providing unit that provides the data stored by the storage unit in response to an inquiry from a user. A system characterized by: (Appendix 2) Equipped with a function to stop collecting information 2. The system of claim 1, (Appendix 3) Equipped with security features to ensure data security 2. The system of claim 1, (Appendix 4) The providing unit is Accepting a voice inquiry from the user, and providing data stored by the storage unit in a voice dialogue format in response to the inquiry. 2. The system of claim 1, (Appendix 5) The collecting unit includes: Infer user sentiment and adjust the type of data you collect based on that sentiment 2. The system of claim 1, (Appendix 6) The collecting unit includes: Analyze users' past behavioral history and select the most appropriate data collection method 2. The system of claim 1, (Appendix 7) The collecting unit includes: Filter data collection based on the user's current activity 2. The system of claim 1, (Appendix 8) The collecting unit includes: Infer user sentiment and prioritize data collection based on that sentiment 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting data, prioritize collection of relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 11) The analysis unit is Infer user emotions and adjust analysis methods based on those emotions 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, the accuracy of the analysis is improved based on correlations between collected data 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, the analysis algorithm is optimized based on the user's past behavioral patterns. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust how analysis results are displayed based on those emotions 2. The system of claim 1, (Appendix 15) The analysis unit is When analyzing, take into account the time-dependent changes in the collected data. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, different analytical techniques are applied to different categories of collected data. 2. The system of claim 1, (Appendix 17) The storage unit includes: Infer user sentiment and prioritize data to store based on that sentiment 2. The system of claim 1, (Appendix 18) The storage unit includes: When saving, adjust the granularity of the save based on the importance of the data 2. The system of claim 1, (Appendix 19) The storage unit includes: When storing, apply different storage algorithms depending on the category of data 2. The system of claim 1, (Appendix 20) The storage unit includes: Infer user emotions and adjust access permissions to stored data based on those emotions 2. The system of claim 1, (Appendix 21) The storage unit includes: When storing, prioritize storage based on when the data was submitted 2. The system of claim 1, (Appendix 22) The storage unit includes: When saving, adjust the order of saving based on data relevance 2. The system of claim 1, (Appendix 23) The providing unit is Infer the user's emotions and adjust the presentation of the data provided based on those emotions 2. The system of claim 1, (Appendix 24) The providing unit is At the time of delivery, adjust the level of detail provided based on the importance of the data 2. The system of claim 1, (Appendix 25) The providing unit is At the time of serving, different serving algorithms are applied depending on the category of data 2. The system of claim 1, (Appendix 26) The providing unit is Infer user emotion and adjust the length of data provided based on that emotion 2. The system of claim 1, (Appendix 27) The providing unit is At the time of submission, data submission will be prioritized based on when it was submitted. 2. The system of claim 1, (Appendix 28) The providing unit is At the time of serving, adjust the order of serving based on the relevance of the data 2. The system of claim 1, (Appendix 29) The off function is Infer user emotions and adjust the timing to turn off information collection based on those emotions 2. The system of claim 1, (Appendix 30) The off function is When turning off information collection, select the optimal timing based on the user's current activity. 2. The system of claim 1, (Appendix 31) The security function includes: Infer user sentiment and adjust data security levels based on that sentiment 2. The system of claim 1, (Appendix 32) The security function includes: To ensure data security, select the most appropriate security measures based on the user's device information. 2. The system of claim 1, [Explanation of symbols]

[0186] 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 voice data, image data, and location information related to a user; an analysis unit that analyzes the data collected by the collection unit; A storage unit for storing the data analyzed by the analysis unit; a providing unit that provides the data stored by the storage unit in response to an inquiry from the user. A system characterized by:

2. Equipped with a function to stop collecting information 2. The system of claim 1 .

3. Equipped with security features to ensure data security 2. The system of claim 1 .

4. The providing unit is Accepting a voice inquiry from the user, and providing data stored by the storage unit in a voice dialogue format in response to the inquiry.

2. The system of claim 1 .

5. The collecting unit includes: Inferring a sentiment of the user and adjusting the type of data to be collected based on the sentiment.

2. The system of claim 1 .

6. The collecting unit includes: Analyze the user's past behavior history and select the optimal data collection method.

2. The system of claim 1 .

7. The collecting unit includes: When collecting data, filtering is performed based on the user's current activity.

2. The system of claim 1 .

8. The collecting unit includes: Inferring the user's emotions and prioritizing data to be collected based on the emotions.

2. The system of claim 1 .

9. The collecting unit includes: When collecting data, prioritize collection of data that is highly relevant based on the geographic location information of the user.

2. The system of claim 1 .

10. The collecting unit includes: At the time of data collection, analyzing the social media activity of said users and collecting relevant data.

2. The system of claim 1 .

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