system

Abstract

The system according to this embodiment aims to integrate information in different formats and efficiently provide the necessary information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a restoration unit, and a protection unit. The collection unit collects information such as voice, text, and images. The analysis unit analyzes the information collected by the collection unit. The provision unit provides necessary information based on the information analyzed by the analysis unit. The restoration unit understands the user's work context based on the information provided by the provision unit and restores the interrupted work. The protection unit protects the privacy of the information restored by the restoration unit using edge AI.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to integrate and utilize information in different formats such as voice, text, and images, and it is difficult to provide efficient information.

[0005] The system according to the embodiment aims to integrate information in different formats and efficiently provide necessary information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a restoration unit, and a protection unit. The collection unit collects information such as voice, text, and images. The analysis unit analyzes the information collected by the collection unit. The provision unit provides necessary information based on the information analyzed by the analysis unit. The restoration unit understands the user's work context based on the information provided by the provision unit and restores the interrupted work. The protection unit protects the privacy of the information restored by the restoration unit using edge AI. [Effects of the Invention]

[0007] The system according to this embodiment can integrate information in different formats and efficiently provide the necessary information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The platform according to an embodiment of the present invention is an innovative platform that extends the cognitive capabilities of knowledge workers by utilizing the latest multimodal AI technology. This platform organically connects all forms of information, such as voice, text, and images, and provides an environment in which users can access necessary information through natural dialogue. For example, the platform collects information such as voice, text, and images. In this process, it collects information in all forms, such as voice spoken by the user, text entered, and images taken. For example, if a user asks, "What about the blue graph that was discussed at last week's product strategy meeting?", that voice information is collected. Next, the collected information is analyzed. The AI ​​analyzes the collected information such as voice, text, and images to identify the necessary information. For example, it searches for information related to the "blue graph" that the user asked about and identifies the relevant data. Furthermore, it understands the user's work context and completely restores interrupted work. For example, even if a user temporarily interrupts their work, it restores the previous state when they resume. This allows the user to smoothly resume their work. Finally, privacy protection is provided by edge AI. User data is processed locally and is not leaked externally. This protects the user's privacy. This allows the platform to significantly reduce the time users spend searching for information, creating an environment where they can focus on creative work.

[0029] The platform according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a restoration unit, and a protection unit. The collection unit collects information such as voice, text, and images. The collection unit collects information such as voice spoken by the user, text entered, and images taken. For example, the collection unit can collect voice data with a microphone, text data with keyboard input, and image data with a camera. For example, the collection unit can collect voice data in real time, text data periodically, and image data as needed. For example, the collection unit can collect voice data with a high-precision microphone, text data with high-speed keyboard input, and image data with a high-resolution camera. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze voice data with speech recognition technology, text data with natural language processing technology, and image data with image recognition technology. For example, the analysis unit can analyze voice data with a speech recognition algorithm, text data with a text analysis algorithm, and image data with an image analysis algorithm. The analysis unit can, for example, analyze audio data with a speech recognition engine, analyze text data with a natural language processing engine, and analyze image data with an image recognition engine. The provision unit provides necessary information based on the information analyzed by the analysis unit. The provision unit can, for example, present the analyzed information to the user and provide the information the user needs. The provision unit can, for example, display the analyzed information on the user's device and provide the information the user needs. The provision unit can, for example, integrate the analyzed information into the user's application and provide the information the user needs. The restoration unit understands the user's work context based on the information provided by the provision unit and restores the interrupted work. The restoration unit can, for example, analyze the user's work context and restore the interrupted work. The restoration unit can, for example, record the user's work context and restore the interrupted work. The restoration unit can, for example, learn the user's work context and restore the interrupted work. The protection unit protects the privacy of the information restored by the restoration unit using edge AI.The protection unit can, for example, process user data locally to protect privacy. The protection unit can, for example, encrypt user data to protect privacy. The protection unit can, for example, control access to user data to protect privacy. As a result, the platform according to the embodiment can collect, analyze, provide, restore, and protect the privacy of information such as voice, text, and images.

[0030] The data collection unit collects information such as voice, text, and images. For example, it collects information such as voice spoken by the user, text entered, and images captured by the user. Specifically, voice data is collected using a high-precision microphone, which reduces ambient noise and allows for the acquisition of clear voice data. Text data is collected quickly and accurately from information entered by the user using a keyboard or touchscreen. Image data is captured using a high-resolution camera, allowing for the acquisition of detailed image information. The data collection unit can collect this data in real time; for example, voice data is collected in real time during a conversation, and text data is collected each time the user enters text. Image data is collected as needed when the user uses the camera. Furthermore, the data collection unit can centrally manage this data and adjust the frequency and accuracy of data collection. For example, it can increase the frequency of voice data collection or adjust the resolution of image data depending on specific situations or conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze audio data using speech recognition technology, text data using natural language processing technology, and image data using image recognition technology. Specifically, audio data is analyzed using a speech recognition algorithm, and conversion from speech to text is performed. In this process, the speech recognition engine accurately recognizes subtle differences in pronunciation and accent, generating highly accurate text data. The text data is analyzed using a natural language processing engine, and analysis is performed to understand the context and meaning. This allows for an accurate understanding of the user's intentions and requests. Image data is analyzed using an image recognition engine to identify objects and scenes within the image. For example, it can recognize people and objects in an image and analyze their position and movement. The analysis unit integrates these analysis results to comprehensively understand the user's behavior and situation. Furthermore, the analysis unit can also utilize past data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to not only grasp the situation in real time but also to respond to future predictions and anomaly detection, improving the reliability and safety of the entire system.

[0032] The service provider provides necessary information based on the information analyzed by the analysis unit. For example, the service provider can present the analyzed information to the user and provide the information the user needs. Specifically, the analyzed information is displayed on the user's device and provided in a format that the user can intuitively understand. For example, text generated by speech recognition is displayed on the user's smartphone or computer screen for the user to review. Text data analyzed by natural language processing is presented in an appropriate format to provide information that meets the user's needs. Image data analyzed by image recognition is integrated into the user's application to provide the information the user needs. The service provider updates this information in real time, ensuring that users always have access to the latest information. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information provided. For example, by providing feedback on the information provided by the user, the service provider can review the method and content of information provision based on that feedback, and provide more useful information to the user. This allows the service provider to provide users with quick and accurate information and improve user convenience.

[0033] The restoration unit understands the user's work context based on the information provided by the service provider and restores the interrupted work. Specifically, the restoration unit analyzes the user's work context and records the state of the work the user interrupted. For example, if a user interrupts document creation, the restoration unit records the content and editing state of the document at that point, allowing the user to resume work from the same state when they resume. The restoration unit can perform more appropriate restorations by learning the user's work context and understanding the user's work patterns and preferences. For example, if a user tends to perform a specific task at a specific time, the restoration unit learns that pattern and restores the work at the optimal time when the user resumes. Furthermore, the restoration unit can improve the accuracy of restorations based on user feedback. For example, by providing feedback on the restored work, the restoration unit can improve its restoration algorithm based on that feedback and perform more accurate restorations. This allows the restoration unit to improve the user's work efficiency and reduce the stress caused by interruptions.

[0034] The protection unit uses edge AI to protect the privacy of information restored by the restoration unit. Specifically, the protection unit processes user data locally and protects privacy without transmitting it externally. For example, voice and text data are analyzed within the user's device and processed without being sent to an external server. This protects the user's privacy. Furthermore, the protection unit encrypts user data to protect it from unauthorized access. For example, collected data is encrypted using a strong encryption algorithm to ensure data security. The protection unit implements access control and strictly manages access rights to user data. For example, only the user themselves or those with specific permissions can access user data, and others cannot. This allows the protection unit to securely protect user data and ensure privacy. Furthermore, the protection unit can continuously improve its privacy protection methods based on user feedback. For example, if a user expresses privacy concerns, the protection unit can review its privacy protection methods based on that feedback and provide stronger protection. This allows the protection unit to securely protect user data and gain user trust.

[0035] The data collection unit can collect information such as voice spoken by the user, text entered, and images taken. For example, the data collection unit can collect voice spoken by the user with a microphone, text entered with a keyboard, and images taken with a camera. For example, the data collection unit can collect voice spoken by the user in real time, text entered periodically, and images taken as needed. For example, the data collection unit can collect voice spoken by the user with a high-precision microphone, text entered with a high-speed keyboard, and images taken with a high-resolution camera. By collecting information such as voice spoken by the user, text entered, and images taken, the necessary information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect voice spoken by the user with a microphone, input the collected voice data into a generating AI, and have the generating AI perform the conversion from voice data to text data.

[0036] The analysis unit can analyze collected information such as audio, text, and images and identify the necessary information. For example, the analysis unit can analyze collected audio data using speech recognition technology, collected text data using natural language processing technology, and collected image data using image recognition technology. For example, the analysis unit can analyze collected audio data using a speech recognition algorithm, collected text data using a text analysis algorithm, and collected image data using an image analysis algorithm. For example, the analysis unit can analyze collected audio data using a speech recognition engine, collected text data using a natural language processing engine, and collected image data using an image recognition engine. By analyzing collected information such as audio, text, and images and identifying the necessary information, the analysis unit can provide the user with the information they need. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected audio data into a generating AI and have the generating AI perform the analysis of the audio data.

[0037] The service provider can provide the necessary information based on the analyzed information. For example, the service provider can present the analyzed information to the user and provide the information the user needs. For example, the service provider can display the analyzed information on the user's device and provide the information the user needs. For example, the service provider can integrate the analyzed information into the user's application and provide the information the user needs. This allows the service provider to quickly provide the user with the necessary information based on the analyzed information. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analyzed information into a generating AI and have the generating AI perform the information provision.

[0038] The restoration unit can understand the user's work context and completely restore the interrupted work. For example, the restoration unit can analyze the user's work context and restore the interrupted work. For example, the restoration unit can record the user's work context and restore the interrupted work. For example, the restoration unit can learn the user's work context and restore the interrupted work. This allows the user to smoothly resume work by understanding the user's work context and completely restoring the interrupted work. Some or all of the above processing in the restoration unit may be performed using AI, for example, or without AI. For example, the restoration unit can input the user's work context into a generating AI and have the generating AI perform the work restoration.

[0039] The protection unit can process user data locally using edge AI to protect privacy. The protection unit can, for example, process user data locally to protect privacy. The protection unit can, for example, encrypt user data to protect privacy. The protection unit can, for example, access control to user data to protect privacy. In this way, user privacy is protected by processing user data locally using edge AI to protect privacy. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input user data into a generating AI and have the generating AI perform data encryption.

[0040] The data collection unit can analyze the user's past information collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting information in formats (audio, text, images) that the user has frequently collected in the past. For example, the data collection unit can analyze the time periods in which the user has collected information in the past and perform collection at the same time periods. For example, the data collection unit can analyze the content of information that the user has collected in the past and prioritize collecting relevant information. In this way, the optimal collection method can be selected by analyzing the user's past information collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past information collection history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the data collection unit can prioritize collecting information related to the user's current projects. For example, the data collection unit can filter and collect information related to areas of interest to the user. For example, the data collection unit can collect information related to topics the user has shown interest in in the past. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of information related to the user's current location. For example, the data collection unit can collect information related to places the user has visited in the past. For example, the data collection unit can collect information related to places the user plans to visit in the future. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the data collection.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can collect information based on the content of posts from accounts the user follows. For example, the data collection unit can collect information related to the activities of groups and communities the user participates in. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on information of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the information, important information can be analyzed in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to text information. For example, the analysis unit can apply an image recognition algorithm to image information. For example, the analysis unit can apply a speech recognition algorithm to audio information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the submission date of the information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may postpone the analysis of information that has been submitted for a longer period. For example, the analysis unit may analyze information that has been submitted for a moderate period with an appropriate priority. In this way, by determining the priority of analysis based on the submission date of the information, the analysis unit can prioritize the analysis of the most recent information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the information submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of information with high relevance. For example, the analysis unit may postpone the analysis of information with low relevance. For example, the analysis unit may analyze information with moderate relevance in an appropriate order. In this way, by adjusting the order of analysis based on the relevance of the information, highly relevant information can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide detailed information for highly important information. For example, the provider can provide concise information for less important information. For example, the provider can provide information with an appropriate level of detail for moderately important information. In this way, by adjusting the level of detail provided based on the importance of the information, important information can be provided in detail. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0049] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the provider can apply a natural language generation algorithm to text information. For example, the provider can apply an image display algorithm to image information. For example, the provider can apply a speech synthesis algorithm to audio information. By applying different information provision algorithms depending on the information category, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input information category data into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0050] The information provider can determine the priority of information provision based on the submission date. For example, the provider may prioritize providing the most recent information. For example, the provider may postpone providing older information. For example, the provider may provide information with a moderate priority based on the submission date. In this way, by determining the priority of information provision based on the submission date, the most recent information can be provided first. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input information submission date data into a generating AI and have the generating AI perform the determination of the provision priority.

[0051] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider can prioritize the delivery of highly relevant information. For example, the provider can postpone the delivery of less relevant information. For example, the provider can deliver moderately relevant information in an appropriate order. In this way, by adjusting the order of delivery based on the relevance of the information, highly relevant information can be prioritized. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input information relevance data into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0052] The restoration unit can analyze the user's past work history and select the optimal restoration method during restoration. For example, the restoration unit can select the optimal restoration method based on the user's past work history. For example, the restoration unit can prioritize suggesting restoration methods previously used by the user. For example, the restoration unit can suggest relevant restoration methods from the user's past work history. In this way, the optimal restoration method can be selected by analyzing the user's past work history. Some or all of the above processing in the restoration unit may be performed using AI, for example, or without AI. For example, the restoration unit can input the user's past work history data into a generating AI and have the generating AI select the optimal restoration method.

[0053] The restoration unit can customize the restoration method based on the user's current work status during restoration. For example, the restoration unit prioritizes restoring information related to the work the user is currently performing. For example, the restoration unit can customize and restore the necessary information according to the user's current work status. For example, the restoration unit can propose the optimal restoration method based on the user's current work status. This allows for the provision of a more appropriate restoration method by customizing the restoration method based on the user's current work status. Some or all of the above-described processes in the restoration unit may be performed using AI, for example, or without AI. For example, the restoration unit can input the user's current work status data into a generating AI and have the generating AI perform the customization of the restoration method.

[0054] The restoration unit can select the optimal restoration method by considering the user's geographical location information during restoration. For example, the restoration unit may prioritize restoring information related to the user's current location. For example, the restoration unit may restore information related to places the user has visited in the past. For example, the restoration unit may restore information related to places the user plans to visit in the future. This allows the restoration unit to select the optimal restoration method by considering the user's geographical location information. Some or all of the above processing in the restoration unit may be performed using AI, for example, or without AI. For example, the restoration unit may input the user's geographical location information data into a generating AI and have the generating AI select the optimal restoration method.

[0055] The restoration unit can analyze the user's social media activity during restoration and propose restoration methods. For example, the restoration unit can restore information related to topics the user has shown interest in on social media. For example, the restoration unit can restore information based on the content of posts from accounts the user follows. For example, the restoration unit can restore information related to the activities of groups and communities the user participates in. In this way, relevant information can be restored by analyzing the user's social media activity. Some or all of the above processing in the restoration unit may be performed using AI, for example, or without AI. For example, the restoration unit can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for restoration methods.

[0056] The protection unit can analyze the user's past data usage history to select the optimal protection method when protecting privacy. For example, the protection unit can select the optimal privacy protection method based on the user's past data usage history. For example, the protection unit can prioritize suggesting privacy protection methods previously used by the user. For example, the protection unit can suggest relevant privacy protection methods based on the user's past data usage history. This allows the protection unit to select the optimal privacy protection method by analyzing the user's past data usage history. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's past data usage history data into a generating AI and have the generating AI select the optimal privacy protection method.

[0057] The protection unit can customize the means of protection based on the user's current data usage when protecting privacy. For example, the protection unit can prioritize providing privacy protection methods related to the data the user is currently using. For example, the protection unit can customize the necessary privacy protection means according to the user's current data usage. For example, the protection unit can suggest the optimal privacy protection means based on the user's current data usage. This allows for more appropriate privacy protection by customizing the means of protection based on the user's current data usage. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's current data usage data into a generating AI and have the generating AI perform the customization of the protection means.

[0058] The protection unit can select the optimal protection method when protecting privacy, taking into account the user's geographical location information. For example, the protection unit can prioritize the privacy protection of data related to the user's current location. For example, the protection unit can protect the privacy of data related to places the user has visited in the past. For example, the protection unit can protect the privacy of data related to places the user plans to visit in the future. This allows the protection unit to select the optimal privacy protection method by taking into account the user's geographical location information. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal privacy protection method.

[0059] The protection unit can analyze a user's social media activity and propose protection measures when protecting privacy. For example, the protection unit can protect the privacy of data related to topics the user has shown interest in on social media. For example, the protection unit can protect the privacy of data based on the content of posts from accounts the user follows. For example, the protection unit can protect the privacy of data related to the activities of groups and communities the user participates in. In this way, by analyzing the user's social media activity, it is possible to provide privacy protection for relevant data. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's social media activity data into a generating AI and have the generating AI propose protection measures.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can analyze a user's past search history and select the optimal analysis method. For example, the analysis unit can prioritize analyzing keywords that the user has frequently searched for in the past. For example, the analysis unit can analyze the time periods in which the user has searched for information in the past and perform analysis at the same time periods. For example, the analysis unit can analyze the content of information that the user has searched for in the past and prioritize analyzing related information. In this way, by analyzing the user's past search history, the optimal analysis method can be selected.

[0062] The recovery unit can analyze the user's past work history and select the optimal recovery method. For example, the recovery unit can select the optimal recovery method based on the user's past work history. For example, the recovery unit can prioritize suggesting recovery methods that the user has used in the past. For example, the recovery unit can suggest relevant recovery methods based on the user's past work history. In this way, the optimal recovery method can be selected by analyzing the user's past work history.

[0063] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of information related to the user's current location. For example, the data collection unit can collect information related to places the user has visited in the past. For example, the data collection unit can collect information related to places the user plans to visit in the future. In this way, by considering the user's geographical location, highly relevant information can be prioritized for collection.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on information of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the information, important information can be analyzed in detail.

[0065] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the provider can apply a natural language generation algorithm to text information. For example, the provider can apply an image display algorithm to image information. For example, the provider can apply a speech synthesis algorithm to audio information. By applying different information provision algorithms depending on the information category, more appropriate information can be provided.

[0066] The protection unit can analyze a user's social media activity and propose protection measures when protecting privacy. For example, the protection unit can protect the privacy of data related to topics the user has shown interest in on social media. For example, the protection unit can protect the privacy of data based on the content of posts from accounts the user follows. For example, the protection unit can protect the privacy of data related to the activities of groups and communities the user participates in. In this way, by analyzing the user's social media activity, it is possible to provide privacy protection for relevant data.

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

[0068] Step 1: The collection unit collects information such as voice, text, and images. For example, it collects the voice spoken by the user using a microphone, the text entered using a keyboard, and the images taken using a camera. The collection unit can collect voice data in real time, collect text data periodically, and collect image data as needed. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes audio data using speech recognition technology, text data using natural language processing technology, and image data using image recognition technology. Step 3: The provisioning unit provides the necessary information based on the information analyzed by the analysis unit. For example, it presents the analyzed information to the user and provides the information the user needs. Step 4: The restoration unit understands the user's work context based on the information provided by the provisioning unit and restores the interrupted work. For example, it analyzes the user's work context and restores the interrupted work. Step 5: The protection unit uses edge AI to protect the privacy of the information restored by the restoration unit. For example, it processes user data locally to protect privacy.

[0069] (Example of form 2) The platform according to an embodiment of the present invention is an innovative platform that extends the cognitive capabilities of knowledge workers by utilizing the latest multimodal AI technology. This platform organically connects all forms of information, such as voice, text, and images, and provides an environment in which users can access necessary information through natural dialogue. For example, the platform collects information such as voice, text, and images. In this process, it collects information in all forms, such as voice spoken by the user, text entered, and images taken. For example, if a user asks, "What about the blue graph that was discussed at last week's product strategy meeting?", that voice information is collected. Next, the collected information is analyzed. The AI ​​analyzes the collected information such as voice, text, and images to identify the necessary information. For example, it searches for information related to the "blue graph" that the user asked about and identifies the relevant data. Furthermore, it understands the user's work context and completely restores interrupted work. For example, even if a user temporarily interrupts their work, it restores the previous state when they resume. This allows the user to smoothly resume their work. Finally, privacy protection is provided by edge AI. User data is processed locally and is not leaked externally. This protects the user's privacy. This allows the platform to significantly reduce the time users spend searching for information, creating an environment where they can focus on creative work.

[0070] The platform according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a restoration unit, and a protection unit. The collection unit collects information such as voice, text, and images. The collection unit collects information such as voice spoken by the user, text entered, and images taken. For example, the collection unit can collect voice data with a microphone, text data with keyboard input, and image data with a camera. For example, the collection unit can collect voice data in real time, text data periodically, and image data as needed. For example, the collection unit can collect voice data with a high-precision microphone, text data with high-speed keyboard input, and image data with a high-resolution camera. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze voice data with speech recognition technology, text data with natural language processing technology, and image data with image recognition technology. For example, the analysis unit can analyze voice data with a speech recognition algorithm, text data with a text analysis algorithm, and image data with an image analysis algorithm. The analysis unit can, for example, analyze audio data with a speech recognition engine, analyze text data with a natural language processing engine, and analyze image data with an image recognition engine. The provision unit provides necessary information based on the information analyzed by the analysis unit. The provision unit can, for example, present the analyzed information to the user and provide the information the user needs. The provision unit can, for example, display the analyzed information on the user's device and provide the information the user needs. The provision unit can, for example, integrate the analyzed information into the user's application and provide the information the user needs. The restoration unit understands the user's work context based on the information provided by the provision unit and restores the interrupted work. The restoration unit can, for example, analyze the user's work context and restore the interrupted work. The restoration unit can, for example, record the user's work context and restore the interrupted work. The restoration unit can, for example, learn the user's work context and restore the interrupted work. The protection unit protects the privacy of the information restored by the restoration unit using edge AI.The protection unit can, for example, process user data locally to protect privacy. The protection unit can, for example, encrypt user data to protect privacy. The protection unit can, for example, control access to user data to protect privacy. As a result, the platform according to the embodiment can collect, analyze, provide, restore, and protect the privacy of information such as voice, text, and images.

[0071] The data collection unit collects information such as voice, text, and images. For example, it collects information such as voice spoken by the user, text entered, and images captured by the user. Specifically, voice data is collected using a high-precision microphone, which reduces ambient noise and allows for the acquisition of clear voice data. Text data is collected quickly and accurately from information entered by the user using a keyboard or touchscreen. Image data is captured using a high-resolution camera, allowing for the acquisition of detailed image information. The data collection unit can collect this data in real time; for example, voice data is collected in real time during a conversation, and text data is collected each time the user enters text. Image data is collected as needed when the user uses the camera. Furthermore, the data collection unit can centrally manage this data and adjust the frequency and accuracy of data collection. For example, it can increase the frequency of voice data collection or adjust the resolution of image data depending on specific situations or conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0072] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze audio data using speech recognition technology, text data using natural language processing technology, and image data using image recognition technology. Specifically, audio data is analyzed using a speech recognition algorithm, and conversion from speech to text is performed. In this process, the speech recognition engine accurately recognizes subtle differences in pronunciation and accent, generating highly accurate text data. The text data is analyzed using a natural language processing engine, and analysis is performed to understand the context and meaning. This allows for an accurate understanding of the user's intentions and requests. Image data is analyzed using an image recognition engine to identify objects and scenes within the image. For example, it can recognize people and objects in an image and analyze their position and movement. The analysis unit integrates these analysis results to comprehensively understand the user's behavior and situation. Furthermore, the analysis unit can also utilize past data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to not only grasp the situation in real time but also to respond to future predictions and anomaly detection, improving the reliability and safety of the entire system.

[0073] The service provider provides necessary information based on the information analyzed by the analysis unit. For example, the service provider can present the analyzed information to the user and provide the information the user needs. Specifically, the analyzed information is displayed on the user's device and provided in a format that the user can intuitively understand. For example, text generated by speech recognition is displayed on the user's smartphone or computer screen for the user to review. Text data analyzed by natural language processing is presented in an appropriate format to provide information that meets the user's needs. Image data analyzed by image recognition is integrated into the user's application to provide the information the user needs. The service provider updates this information in real time, ensuring that users always have access to the latest information. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information provided. For example, by providing feedback on the information provided by the user, the service provider can review the method and content of information provision based on that feedback, and provide more useful information to the user. This allows the service provider to provide users with quick and accurate information and improve user convenience.

[0074] The restoration unit understands the user's work context based on the information provided by the service provider and restores the interrupted work. Specifically, the restoration unit analyzes the user's work context and records the state of the work the user interrupted. For example, if a user interrupts document creation, the restoration unit records the content and editing state of the document at that point, allowing the user to resume work from the same state when they resume. The restoration unit can perform more appropriate restorations by learning the user's work context and understanding the user's work patterns and preferences. For example, if a user tends to perform a specific task at a specific time, the restoration unit learns that pattern and restores the work at the optimal time when the user resumes. Furthermore, the restoration unit can improve the accuracy of restorations based on user feedback. For example, by providing feedback on the restored work, the restoration unit can improve its restoration algorithm based on that feedback and perform more accurate restorations. This allows the restoration unit to improve the user's work efficiency and reduce the stress caused by interruptions.

[0075] The protection unit uses edge AI to protect the privacy of information restored by the restoration unit. Specifically, the protection unit processes user data locally and protects privacy without transmitting it externally. For example, voice and text data are analyzed within the user's device and processed without being sent to an external server. This protects the user's privacy. Furthermore, the protection unit encrypts user data to protect it from unauthorized access. For example, collected data is encrypted using a strong encryption algorithm to ensure data security. The protection unit implements access control and strictly manages access rights to user data. For example, only the user themselves or those with specific permissions can access user data, and others cannot. This allows the protection unit to securely protect user data and ensure privacy. Furthermore, the protection unit can continuously improve its privacy protection methods based on user feedback. For example, if a user expresses privacy concerns, the protection unit can review its privacy protection methods based on that feedback and provide stronger protection. This allows the protection unit to securely protect user data and gain user trust.

[0076] The data collection unit can collect information such as voice spoken by the user, text entered, and images taken. For example, the data collection unit can collect voice spoken by the user with a microphone, text entered with a keyboard, and images taken with a camera. For example, the data collection unit can collect voice spoken by the user in real time, text entered periodically, and images taken as needed. For example, the data collection unit can collect voice spoken by the user with a high-precision microphone, text entered with a high-speed keyboard, and images taken with a high-resolution camera. By collecting information such as voice spoken by the user, text entered, and images taken, the necessary information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect voice spoken by the user with a microphone, input the collected voice data into a generating AI, and have the generating AI perform the conversion from voice data to text data.

[0077] The analysis unit can analyze collected information such as audio, text, and images and identify the necessary information. For example, the analysis unit can analyze collected audio data using speech recognition technology, collected text data using natural language processing technology, and collected image data using image recognition technology. For example, the analysis unit can analyze collected audio data using a speech recognition algorithm, collected text data using a text analysis algorithm, and collected image data using an image analysis algorithm. For example, the analysis unit can analyze collected audio data using a speech recognition engine, collected text data using a natural language processing engine, and collected image data using an image recognition engine. By analyzing collected information such as audio, text, and images and identifying the necessary information, the analysis unit can provide the user with the information they need. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected audio data into a generating AI and have the generating AI perform the analysis of the audio data.

[0078] The service provider can provide the necessary information based on the analyzed information. For example, the service provider can present the analyzed information to the user and provide the information the user needs. For example, the service provider can display the analyzed information on the user's device and provide the information the user needs. For example, the service provider can integrate the analyzed information into the user's application and provide the information the user needs. This allows the service provider to quickly provide the user with the necessary information based on the analyzed information. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analyzed information into a generating AI and have the generating AI perform the information provision.

[0079] The restoration unit can understand the user's work context and completely restore the interrupted work. For example, the restoration unit can analyze the user's work context and restore the interrupted work. For example, the restoration unit can record the user's work context and restore the interrupted work. For example, the restoration unit can learn the user's work context and restore the interrupted work. This allows the user to smoothly resume work by understanding the user's work context and completely restoring the interrupted work. Some or all of the above processing in the restoration unit may be performed using AI, for example, or without AI. For example, the restoration unit can input the user's work context into a generating AI and have the generating AI perform the work restoration.

[0080] The protection unit can process user data locally using edge AI to protect privacy. The protection unit can, for example, process user data locally to protect privacy. The protection unit can, for example, encrypt user data to protect privacy. The protection unit can, for example, access control to user data to protect privacy. In this way, user privacy is protected by processing user data locally using edge AI to protect privacy. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input user data into a generating AI and have the generating AI perform data encryption.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect information when the user is relaxed. For example, if the user is concentrating, the data collection unit can refrain from collecting information and collect it when the user is taking a break. For example, if the user is tired, the data collection unit can temporarily stop collecting information and resume it after the user has recovered. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The data collection unit can analyze the user's past information collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting information in formats (audio, text, images) that the user has frequently collected in the past. For example, the data collection unit can analyze the time periods in which the user has collected information in the past and perform collection at the same time periods. For example, the data collection unit can analyze the content of information that the user has collected in the past and prioritize collecting relevant information. In this way, the optimal collection method can be selected by analyzing the user's past information collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past information collection history into a generating AI and have the generating AI select the optimal collection method.

[0083] The data collection unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the data collection unit can prioritize collecting information related to the user's current projects. For example, the data collection unit can filter and collect information related to areas of interest to the user. For example, the data collection unit can collect information related to topics the user has shown interest in in the past. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0084] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information of high importance. For example, if the user is relaxed, the data collection unit can prioritize collecting information of interest. For example, if the user is in a hurry, the data collection unit can prioritize collecting information that can be collected quickly. In this way, important information can be collected preferentially by determining the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0085] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of information related to the user's current location. For example, the data collection unit can collect information related to places the user has visited in the past. For example, the data collection unit can collect information related to places the user plans to visit in the future. This allows for the priority collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the data collection.

[0086] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can collect information based on the content of posts from accounts the user follows. For example, the data collection unit can collect information related to the activities of groups and communities the user participates in. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on information of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the information, important information can be analyzed in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to text information. For example, the analysis unit can apply an image recognition algorithm to image information. For example, the analysis unit can apply a speech recognition algorithm to audio information. By applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0091] The analysis unit can determine the priority of analysis based on the submission date of the information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may postpone the analysis of information that has been submitted for a longer period. For example, the analysis unit may analyze information that has been submitted for a moderate period with an appropriate priority. In this way, by determining the priority of analysis based on the submission date of the information, the analysis unit can prioritize the analysis of the most recent information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the information submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of information with high relevance. For example, the analysis unit may postpone the analysis of information with low relevance. For example, the analysis unit may analyze information with moderate relevance in an appropriate order. In this way, by adjusting the order of analysis based on the relevance of the information, highly relevant information can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0093] The service provider can estimate the user's emotions and adjust the presentation of the information based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed information. If the user is in a hurry, the service provider can provide concise information that gets straight to the point. If the user is stressed, the service provider can provide visually easy-to-understand information. By adjusting the presentation of the information based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust the presentation of the information.

[0094] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide detailed information for highly important information. For example, the provider can provide concise information for less important information. For example, the provider can provide information with an appropriate level of detail for moderately important information. In this way, by adjusting the level of detail provided based on the importance of the information, important information can be provided in detail. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0095] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the provider can apply a natural language generation algorithm to text information. For example, the provider can apply an image display algorithm to image information. For example, the provider can apply a speech synthesis algorithm to audio information. By applying different information provision algorithms depending on the information category, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input information category data into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0096] The service provider can estimate the user's emotions and adjust the length of the service based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise information. For example, if the user is relaxed, the service provider can provide detailed information. For example, if the user is stressed, the service provider can provide visually easy-to-understand information. By adjusting the length of the service based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the service.

[0097] The information provider can determine the priority of information provision based on the submission date. For example, the provider may prioritize providing the most recent information. For example, the provider may postpone providing older information. For example, the provider may provide information with a moderate priority based on the submission date. In this way, by determining the priority of information provision based on the submission date, the most recent information can be provided first. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input information submission date data into a generating AI and have the generating AI perform the determination of the provision priority.

[0098] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider can prioritize the delivery of highly relevant information. For example, the provider can postpone the delivery of less relevant information. For example, the provider can deliver moderately relevant information in an appropriate order. In this way, by adjusting the order of delivery based on the relevance of the information, highly relevant information can be prioritized. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input information relevance data into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0099] The reconstruction unit can estimate the user's emotions and adjust the reconstruction method based on the estimated emotions. For example, if the user is relaxed, the reconstruction unit can provide a detailed reconstruction method. For example, if the user is in a hurry, the reconstruction unit can provide a concise reconstruction method that gets straight to the point. For example, if the user is stressed, the reconstruction unit can provide a visually easy-to-understand reconstruction method. By adjusting the reconstruction method based on the user's emotions, a more appropriate reconstruction method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the reconstruction method.

[0100] The restoration unit can analyze the user's past work history and select the optimal restoration method during restoration. For example, the restoration unit can select the optimal restoration method based on the user's past work history. For example, the restoration unit can prioritize suggesting restoration methods previously used by the user. For example, the restoration unit can suggest relevant restoration methods from the user's past work history. In this way, the optimal restoration method can be selected by analyzing the user's past work history. Some or all of the above processing in the restoration unit may be performed using AI, for example, or without AI. For example, the restoration unit can input the user's past work history data into a generating AI and have the generating AI select the optimal restoration method.

[0101] The restoration unit can customize the restoration method based on the user's current work status during restoration. For example, the restoration unit prioritizes restoring information related to the work the user is currently performing. For example, the restoration unit can customize and restore the necessary information according to the user's current work status. For example, the restoration unit can propose the optimal restoration method based on the user's current work status. This allows for the provision of a more appropriate restoration method by customizing the restoration method based on the user's current work status. Some or all of the above-described processes in the restoration unit may be performed using AI, for example, or without AI. For example, the restoration unit can input the user's current work status data into a generating AI and have the generating AI perform the customization of the restoration method.

[0102] The restoration unit can estimate the user's emotions and determine restoration priorities based on the estimated emotions. For example, if the user is stressed, the restoration unit can prioritize restoring information of high importance. For example, if the user is relaxed, the restoration unit can prioritize restoring information of interest. For example, if the user is in a hurry, the restoration unit can prioritize restoring information that can be quickly restored. In this way, important information can be restored preferentially by determining restoration priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the restoration unit may be performed using AI, for example, or not using AI. For example, the restoration unit can input user emotion data into a generative AI and have the generative AI perform the determination of restoration priorities.

[0103] The restoration unit can select the optimal restoration method by considering the user's geographical location information during restoration. For example, the restoration unit may prioritize restoring information related to the user's current location. For example, the restoration unit may restore information related to places the user has visited in the past. For example, the restoration unit may restore information related to places the user plans to visit in the future. This allows the restoration unit to select the optimal restoration method by considering the user's geographical location information. Some or all of the above processing in the restoration unit may be performed using AI, for example, or without AI. For example, the restoration unit may input the user's geographical location information data into a generating AI and have the generating AI select the optimal restoration method.

[0104] The restoration unit can analyze the user's social media activity during restoration and propose restoration methods. For example, the restoration unit can restore information related to topics the user has shown interest in on social media. For example, the restoration unit can restore information based on the content of posts from accounts the user follows. For example, the restoration unit can restore information related to the activities of groups and communities the user participates in. In this way, relevant information can be restored by analyzing the user's social media activity. Some or all of the above processing in the restoration unit may be performed using AI, for example, or without AI. For example, the restoration unit can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for restoration methods.

[0105] The protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated user emotions. For example, if the user is stressed, the protection unit can increase the level of privacy protection. For example, if the user is relaxed, the protection unit can adjust the level of privacy protection appropriately. For example, if the user is in a hurry, the protection unit can quickly implement privacy protection. This allows for more appropriate privacy protection by adjusting the privacy protection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the protection unit may be performed using AI or not using AI. For example, the protection unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the privacy protection method.

[0106] The protection unit can analyze the user's past data usage history to select the optimal protection method when protecting privacy. For example, the protection unit can select the optimal privacy protection method based on the user's past data usage history. For example, the protection unit can prioritize suggesting privacy protection methods previously used by the user. For example, the protection unit can suggest relevant privacy protection methods based on the user's past data usage history. This allows the protection unit to select the optimal privacy protection method by analyzing the user's past data usage history. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's past data usage history data into a generating AI and have the generating AI select the optimal privacy protection method.

[0107] The protection unit can customize the means of protection based on the user's current data usage when protecting privacy. For example, the protection unit can prioritize providing privacy protection methods related to the data the user is currently using. For example, the protection unit can customize the necessary privacy protection means according to the user's current data usage. For example, the protection unit can suggest the optimal privacy protection means based on the user's current data usage. This allows for more appropriate privacy protection by customizing the means of protection based on the user's current data usage. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's current data usage data into a generating AI and have the generating AI perform the customization of the protection means.

[0108] The protection unit can estimate the user's emotions and determine privacy protection priorities based on the estimated emotions. For example, if the user is stressed, the protection unit will prioritize the protection of highly important data. For example, if the user is relaxed, the protection unit will prioritize the protection of interesting data. For example, if the user is in a hurry, the protection unit will quickly protect the data. This allows for the priority protection of important data by determining privacy protection priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the protection unit may be performed using AI or not using AI. For example, the protection unit can input user emotion data into a generative AI and have the generative AI perform the determination of privacy protection priorities.

[0109] The protection unit can select the optimal protection method when protecting privacy, taking into account the user's geographical location information. For example, the protection unit can prioritize the privacy protection of data related to the user's current location. For example, the protection unit can protect the privacy of data related to places the user has visited in the past. For example, the protection unit can protect the privacy of data related to places the user plans to visit in the future. This allows the protection unit to select the optimal privacy protection method by taking into account the user's geographical location information. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal privacy protection method.

[0110] The protection unit can analyze a user's social media activity and propose protection measures when protecting privacy. For example, the protection unit can protect the privacy of data related to topics the user has shown interest in on social media. For example, the protection unit can protect the privacy of data based on the content of posts from accounts the user follows. For example, the protection unit can protect the privacy of data related to the activities of groups and communities the user participates in. In this way, by analyzing the user's social media activity, it is possible to provide privacy protection for relevant data. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can input the user's social media activity data into a generating AI and have the generating AI propose protection measures.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The data collection unit can collect the user's biometric information and adjust the timing of data collection based on the collected biometric information. For example, the data collection unit can monitor the user's heart rate and skin electrical activity, refraining from collecting information when the user is stressed and collecting it when the user is relaxed. For example, the data collection unit can analyze the user's brainwaves, refraining from collecting information when the user is concentrating and collecting it during breaks. For example, the data collection unit can analyze the user's breathing patterns, temporarily stopping data collection if fatigue is detected and resuming it after recovery. By adjusting the timing of data collection based on the user's biometric information, data can be collected at a more appropriate time.

[0113] The analysis unit can analyze a user's past search history and select the optimal analysis method. For example, the analysis unit can prioritize analyzing keywords that the user has frequently searched for in the past. For example, the analysis unit can analyze the time periods in which the user has searched for information in the past and perform analysis at the same time periods. For example, the analysis unit can analyze the content of information that the user has searched for in the past and prioritize analyzing related information. In this way, by analyzing the user's past search history, the optimal analysis method can be selected.

[0114] The information provider can estimate the user's emotions and adjust the format of the information provided based on those emotions. For example, if the user is relaxed, the provider can provide detailed text information. If the user is in a hurry, the provider can provide concise, bullet-point information. If the user is stressed, the provider can provide visually easy-to-understand infographic information. By adjusting the format of the information provided based on the user's emotions, more relevant information can be delivered.

[0115] The recovery unit can analyze the user's past work history and select the optimal recovery method. For example, the recovery unit can select the optimal recovery method based on the user's past work history. For example, the recovery unit can prioritize suggesting recovery methods that the user has used in the past. For example, the recovery unit can suggest relevant recovery methods based on the user's past work history. In this way, the optimal recovery method can be selected by analyzing the user's past work history.

[0116] The protection unit can estimate the user's emotions and adjust the level of privacy protection based on those emotions. For example, if the user is stressed, the protection unit can increase the level of privacy protection. For example, if the user is relaxed, the protection unit can adjust the level of privacy protection appropriately. For example, if the user is in a hurry, the protection unit can quickly implement privacy protection. In this way, by adjusting the level of privacy protection based on the user's emotions, more appropriate privacy protection can be provided.

[0117] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of information related to the user's current location. For example, the data collection unit can collect information related to places the user has visited in the past. For example, the data collection unit can collect information related to places the user plans to visit in the future. In this way, by considering the user's geographical location, highly relevant information can be prioritized for collection.

[0118] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on information of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the information, important information can be analyzed in detail.

[0119] The information provider can apply different information provision algorithms depending on the information category at the time of provision. For example, the provider can apply a natural language generation algorithm to text information. For example, the provider can apply an image display algorithm to image information. For example, the provider can apply a speech synthesis algorithm to audio information. By applying different information provision algorithms depending on the information category, more appropriate information can be provided.

[0120] The recovery unit can estimate the user's emotions and determine recovery priorities based on those emotions. For example, if the user is stressed, the recovery unit will prioritize recovering information of high importance. For example, if the user is relaxed, the recovery unit can prioritize recovering information of interest. For example, if the user is in a hurry, the recovery unit can prioritize recovering information that can be recovered quickly. In this way, by determining recovery priorities based on the user's emotions, important information can be recovered preferentially.

[0121] The protection unit can analyze a user's social media activity and propose protection measures when protecting privacy. For example, the protection unit can protect the privacy of data related to topics the user has shown interest in on social media. For example, the protection unit can protect the privacy of data based on the content of posts from accounts the user follows. For example, the protection unit can protect the privacy of data related to the activities of groups and communities the user participates in. In this way, by analyzing the user's social media activity, it is possible to provide privacy protection for relevant data.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The collection unit collects information such as voice, text, and images. For example, it collects the voice spoken by the user using a microphone, the text entered using a keyboard, and the images taken using a camera. The collection unit can collect voice data in real time, collect text data periodically, and collect image data as needed. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes audio data using speech recognition technology, text data using natural language processing technology, and image data using image recognition technology. Step 3: The provisioning unit provides the necessary information based on the information analyzed by the analysis unit. For example, it presents the analyzed information to the user and provides the information the user needs. Step 4: The restoration unit understands the user's work context based on the information provided by the provisioning unit and restores the interrupted work. For example, it analyzes the user's work context and restores the interrupted work. Step 5: The protection unit uses edge AI to protect the privacy of the information restored by the restoration unit. For example, it processes user data locally to protect privacy.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, restoration unit, and protection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects voice, text, and image data using the microphone 38B, keyboard, camera 42, etc., of the smart device 14. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit provides the analysis results to the user using, for example, the display 40A or speaker 40B of the smart device 14. The restoration unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and understands the user's work context and restores the interrupted work. The protection unit is implemented in, for example, the control unit 46A of the smart device 14 and processes the user's data locally using edge AI to protect privacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, restoration unit, and protection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects voice, text, and image data using the microphone 238, keyboard, camera 42, etc., of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data. The provision unit provides the analysis results to the user using, for example, the display and speaker 240 of the smart glasses 214. The restoration unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and understands the user's work context and restores the interrupted work. The protection unit is implemented, for example, by the control unit 46A of the smart glasses 214, and processes the user's data locally using edge AI to protect privacy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, restoration unit, and protection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects voice, text, and image data using the microphone 238, keyboard, camera 42, etc., of the headset terminal 314. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The provision unit provides the analysis results to the user using, for example, the display 343 and speaker 240 of the headset terminal 314. The restoration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and understands the user's work context and restores the interrupted work. The protection unit is implemented, for example, by the control unit 46A of the headset terminal 314, and processes the user's data locally using edge AI to protect privacy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, restoration unit, and protection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects voice, text, and image data using the robot 414's microphone 238, keyboard, camera 42, etc. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The provision unit provides the analysis results to the user using, for example, the robot 414's display and speaker 240. The restoration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and understands the user's work context and restores the interrupted work. The protection unit is implemented, for example, by the control unit 46A of the robot 414, and processes the user's data locally using edge AI to protect privacy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A collection unit that collects information such as audio, text, and images, An analysis unit analyzes the information collected by the aforementioned collection unit, A providing unit that provides necessary information based on the information analyzed by the aforementioned analysis unit, A restoration unit that understands the user's work context based on the information provided by the aforementioned provision unit and restores the interrupted work, The system includes a protection unit that uses edge AI to protect the privacy of the information restored by the restoration unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects information such as the voice spoken by the user, the text entered, and the images taken. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes collected information such as audio, text, and images to identify the necessary information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the necessary information based on the analyzed data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The restoration unit is, Understand the user's work context and fully restore interrupted work. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned protective part is Edge AI processes user data locally, protecting privacy. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past information gathering history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we present the content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the service based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The restoration unit is, It estimates the user's emotions and adjusts the restoration method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The restoration unit is, During the restoration process, the system analyzes the user's past work history to select the optimal restoration method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The restoration unit is, During restoration, the restoration method is customized based on the user's current work status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The restoration unit is, The system estimates the user's emotions and determines the restoration priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The restoration unit is, During the restoration process, the system will select the optimal restoration method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The restoration unit is, During the recovery process, we analyze the user's social media activity and suggest recovery methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned protective part is We estimate the user's emotions and adjust our privacy protection methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned protective part is When protecting privacy, the system analyzes the user's past data usage history to select the most appropriate protection method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned protective part is When protecting privacy, customize the means of protection based on the user's current data usage. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned protective part is It estimates user sentiment and determines privacy protection priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned protective part is When protecting privacy, the optimal protection method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned protective part is When protecting privacy, we analyze users' social media activity and suggest protection measures. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information such as audio, text, and images, An analysis unit analyzes the information collected by the aforementioned collection unit, A providing unit that provides necessary information based on the information analyzed by the aforementioned analysis unit, A restoration unit that understands the user's work context based on the information provided by the aforementioned provision unit and restores the interrupted work, The system includes a protection unit that uses edge AI to protect the privacy of the information restored by the restoration unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects information such as the voice spoken by the user, the text entered, and the images taken. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes collected information such as audio, text, and images to identify the necessary information. The system according to feature 1.

4. The aforementioned supply unit is, Provide the necessary information based on the analyzed data. The system according to feature 1.

5. The restoration unit is, Understand the user's work context and fully restore interrupted work. The system according to feature 1.

6. The aforementioned protective part is Edge AI processes user data locally, protecting privacy. The system according to feature 1.

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

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

9. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

Images