System

Abstract

An object of a system according to an embodiment is to easily generate generated AI by utilizing video recording information and audio recording information.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video recording information or audio recording information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates generation AI based on the information analyzed by the analysis unit. The providing unit provides the generated AI generated by the generation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of generating generative AI using video and audio information was complicated and difficult to do easily.

[0005] The system according to the embodiment aims to easily generate generative AI by utilizing video recording information and audio recording information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video recording information or audio recording information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a generation AI based on the information analyzed by the analysis unit. The provision unit provides the generation AI generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can easily generate generative AI by utilizing video recording information and audio recording information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention implements a generative AI in an AI device and generates a generative AI by utilizing video and audio recordings in cooperation with various devices and APIs. This system implements a generative AI in an AI device, collects video and audio recordings in cooperation with various devices and APIs, and the generative AI analyzes the collected information to generate a generative AI. Furthermore, ideas that arise in daily life can be easily created as generative AI through dialogue. For example, an AI device cooperates with devices such as smartphones, tablets, and smart speakers to collect video and audio recordings. The collected information is analyzed by the generative AI to generate a generative AI. This allows users to create generative AI without coding. This system allows users to create generative AI without coding. For example, even users without programming knowledge can realize new ideas by utilizing the generative AI. Furthermore, by cooperating with various devices and APIs, the generative AI can create a generative AI based on the user's daily activities. This allows users to live more convenient and efficient lives.

[0029] A generative AI system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video recording information or audio recording information. For example, the collection unit can collect information in cooperation with devices such as smartphones, tablets, and smart speakers. The collection unit can also collect information such as video conference recordings and voice memos. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit converts audio recording information into text data using speech recognition technology. The analysis unit can also analyze the video recording information using image analysis technology. The analysis unit can also analyze the collected information using text analysis technology. The generation unit generates a generative AI based on the information analyzed by the analysis unit. For example, the generation unit can generate new text using a text generation AI. The generation unit can also generate new images using an image generation AI. The generation unit can also generate new audio using an audio generation AI. The provision unit provides the generative AI generated by the generation unit to a user. For example, the provision unit can provide the generative AI through a web application. The provision unit can also provide the generative AI through a mobile application. Furthermore, the providing unit can provide the generating AI through an API. As a result, the generating AI system according to the embodiment can generate the generating AI using video recording information and audio recording information and provide it to the user.

[0030] The collection unit can collect information in cooperation with at least one device selected from the group consisting of a smartphone, a tablet, and a smart speaker. The collection unit collects information using, for example, a smartphone's camera or microphone. For example, the collection unit can record the user's daily activities using the smartphone's camera. The collection unit can also record the user's voice using the smartphone's microphone. Furthermore, the collection unit can collect information in cooperation with a tablet or smart speaker. For example, the collection unit can record the user's actions using the tablet's camera. The collection unit can also record the user's voice using the smart speaker's microphone. This allows the collection unit to collect information in cooperation with various devices.

[0031] The analysis unit can analyze the collected video or audio information. The analysis unit can convert the audio information into text data using, for example, voice recognition technology. For example, the analysis unit can analyze the audio information using voice recognition software and save it as text data. The analysis unit can also analyze the video information using image analysis technology. For example, the analysis unit can analyze the video information using image recognition software to extract important scenes. The analysis unit can also analyze the collected information using text analysis technology. For example, the analysis unit can analyze the text data using natural language processing technology to extract important information. In this way, the analysis unit can provide the data necessary for generating generative AI by analyzing the collected information.

[0032] The generation unit can generate a generation AI based on the collected information. The generation unit, for example, generates new text using a text generation AI. For example, the generation unit generates new sentences based on collected text data. The generation unit can also generate new images using an image generation AI. For example, the generation unit generates new images based on collected image data. The generation unit can also generate new audio using a voice generation AI. For example, the generation unit generates new audio based on collected audio data. This allows the generation unit to generate a generation AI based on the collected information.

[0033] The providing unit can provide the generated generated AI to the user. The providing unit can provide the generated AI through, for example, a web application. For example, the providing unit can provide the generated AI to the user through a web browser. The providing unit can also provide the generated AI through a mobile application. For example, the providing unit can provide the generated AI to the user through an app on a smartphone or tablet. Furthermore, the providing unit can also provide the generated AI through an API. For example, the providing unit can provide an API for providing the generated AI in cooperation with other systems. This allows the providing unit to provide the generated generated AI to the user.

[0034] The providing unit can create the generative AI through dialogue. For example, the providing unit creates the generative AI by interacting with the user using a chatbot. For example, when the user instructs the chatbot to "think of a new recipe," the generative AI generates a new recipe. The providing unit can also create the generative AI by interacting with the user using a voice assistant. For example, when the user instructs the voice assistant to "tell me a new idea," the generative AI generates a new idea. In this way, the providing unit can create the generative AI through dialogue.

[0035] The collection unit can analyze the user's past behavioral history at the time of collection and select an appropriate collection method. The collection unit, for example, analyzes the user's past behavioral history and sets the optimal collection timing. For example, the collection unit sets the optimal collection timing based on time periods during which the user frequently recorded in the past. The collection unit can also prioritize selecting devices that the user has used favorably in the past. For example, the collection unit prioritizes smartphones or tablets that the user has used favorably in the past. The collection unit can also analyze the user's past behavioral patterns and suggest the most efficient collection method. For example, the collection unit suggests the optimal collection method based on the user's past behavioral patterns. This allows the collection unit to select the optimal collection method based on the user's past behavioral history, thereby enabling efficient information collection.

[0036] The collection unit can perform filtering based on the user's current activity at the time of collection. For example, if the user is in a meeting, the collection unit collects only information related to the content of the meeting. For example, the collection unit prioritizes collecting remarks made during the meeting and minutes of the meeting. Furthermore, if the user is exercising, the collection unit can also prioritize collecting data related to exercise. For example, the collection unit collects the heart rate and calorie consumption during exercise. Furthermore, if the user is relaxing, the collection unit can also collect music and environmental sounds related to relaxation. For example, the collection unit collects music and natural sounds played while relaxing. In this way, the collection unit can collect highly relevant information by filtering based on the user's current activity.

[0037] The collection unit can select an appropriate collection means depending on the user's input method when collecting data. For example, when the user is using voice input, the collection unit prioritizes collecting voice data. For example, when the user is using voice input, the collection unit collects voice data using a microphone. The collection unit can also prioritize collecting text data when the user is using text input. For example, when the user is using text input, the collection unit collects text data using a keyboard or touch screen. The collection unit can also prioritize collecting gesture data when the user is using gesture input. For example, when the user is using gesture input, the collection unit collects gesture data using a camera or sensor. This allows the collection unit to select the optimal collection means depending on the user's input method, thereby enabling efficient information collection.

[0038] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, when the user is in a specific location, the collection unit prioritizes collecting information related to that location. For example, when the user is in a tourist destination, the collection unit prioritizes collecting information about the tourist destination. Furthermore, when the user is traveling, the collection unit can also prioritize collecting tourist information about the travel destination. For example, the collection unit collects information about tourist spots and restaurants at the user's travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting information about the area around the user's home. For example, the collection unit collects event information and store information about the area around the user's home. This allows the collection unit to prioritize collecting highly relevant information by taking into account the user's geographical location information, thereby enabling efficient information collection.

[0039] The collection unit can analyze the user's social media activities at the time of collection and collect related information. The collection unit, for example, collects information about places where the user has checked in on social media. For example, the collection unit collects information about restaurants and cafes where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related information. For example, the collection unit analyzes photos and comments posted by the user on social media and collects related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit collects related information based on information shared by the user's friends on social media. In this way, the collection unit can analyze the user's social media activities and collect related information, thereby enabling efficient information collection.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit preferentially uses the collection method that the user previously preferred. For example, the collection unit customizes the collection method based on the user's past feedback. The collection unit can also improve the collection method based on the user's past feedback. For example, the collection unit analyzes feedback provided by the user in the past and optimizes the collection method. The collection unit can also suggest a new collection method by referring to the user's past feedback. For example, the collection unit suggests a new device or timing based on feedback provided by the user in the past. In this way, the collection unit can customize the collection method by reflecting the user's past feedback, thereby enabling efficient information collection.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of information collected during analysis. The analysis unit, for example, performs a detailed analysis on important information. For example, the analysis unit evaluates the importance of the collected information and performs a detailed analysis on the important information. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit evaluates the importance of the collected information and performs a concise analysis on the general information. The analysis unit can also perform a detailed analysis on information that is of great interest to the user. For example, the analysis unit prioritizes analysis of information that is of great interest to the user and provides a detailed analysis result. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the collected information, thereby enabling efficient analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a video analysis algorithm to video information. For example, the analysis unit uses a video analysis algorithm to analyze video information. The analysis unit can also apply an audio analysis algorithm to audio information. For example, the analysis unit uses an audio analysis algorithm to analyze audio information. The analysis unit can also apply a text analysis algorithm to text information. For example, the analysis unit uses a text analysis algorithm to analyze text information. This allows the analysis unit to apply different analysis algorithms depending on the category of information, thereby enabling efficient analysis.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and optimizes the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and suggest an optimal analysis method. For example, the analysis unit suggests an optimal analysis method based on the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results, thereby enabling efficient analysis.

[0044] The analysis unit can determine the priority of analysis based on the time when information was collected during analysis. The analysis unit, for example, prioritizes analysis of the most recent information. For example, the analysis unit prioritizes analysis of the most recent information based on the time when the collected information was collected. The analysis unit can also prioritize analysis of important information by referring to past information. For example, the analysis unit prioritizes analysis of important information by referring to past information based on the time when the collected information was collected. The analysis unit can also prioritize analysis of information from a period when the user was most interested. For example, the analysis unit prioritizes analysis based on information from a period when the user was most interested. In this way, the analysis unit can determine the priority of analysis based on the time when the information was collected, thereby enabling efficient analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, prioritizes analysis of highly relevant information. For example, the analysis unit evaluates the relevance of collected information and prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit evaluates the relevance of collected information and prioritizes analysis of less relevant information. The analysis unit can also prioritize analysis of information that is of great interest to the user. For example, the analysis unit prioritizes analysis based on information that is of great interest to the user. In this way, the analysis unit can adjust the order of analysis based on the relevance of information, thereby enabling efficient analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. For example, the analysis unit evaluates the user's level of expertise and uses a lot of technical terms for users who have technical expertise. The analysis unit can also avoid technical terms if the user does not have technical expertise. For example, the analysis unit evaluates the user's level of expertise and avoids technical terms for users who do not have technical expertise. The analysis unit can also use appropriate terms according to the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise and uses appropriate terms. In this way, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand.

[0047] The generation unit can adjust the level of detail of the generation based on the importance of the information analyzed during generation. The generation unit, for example, generates detailed information for important information. For example, the generation unit evaluates the importance of the analyzed information and generates detailed information for the important information. The generation unit can also generate concise information for general information. For example, the generation unit evaluates the importance of the analyzed information and generates concise information for the general information. The generation unit can also generate detailed information for information that is of great interest to the user. For example, the generation unit prioritizes generating information that is of great interest to the user and provides a detailed generation result. This allows the generation unit to adjust the level of detail of the generation based on the importance of the analyzed information, thereby enabling efficient generation.

[0048] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit applies a video generation algorithm to video information. For example, the generation unit uses a video generation algorithm to generate video information. The generation unit can also apply a voice generation algorithm to voice information. For example, the generation unit uses a voice generation algorithm to generate voice information. The generation unit can also apply a text generation algorithm to text information. For example, the generation unit uses a text generation algorithm to generate text information. This allows the generation unit to apply different generation algorithms depending on the category of information, thereby enabling efficient generation.

[0049] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. For example, the generation unit analyzes the user's past generation results and optimizes the generation algorithm. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of generation based on the user's past generation results. The generation unit can also analyze the user's past generation results and propose an optimal generation method. For example, the generation unit proposes an optimal generation method based on the user's past generation results. In this way, the generation unit can improve the accuracy of generation by referring to the user's past generation results, thereby enabling efficient generation.

[0050] The generation unit can determine the priority of generation based on the time when the information was collected at the time of generation. The generation unit, for example, prioritizes generation based on the latest information. For example, the generation unit prioritizes generation of the latest information based on the time when the collected information was collected. The generation unit can also prioritize important generation by referring to past information. For example, the generation unit prioritizes generation of important information by referring to past information based on the time when the collected information was collected. The generation unit can also prioritize generation based on information from a period when the user was most interested. For example, the generation unit prioritizes generation based on information from a period when the user was most interested. In this way, the generation unit can determine the priority of generation based on the time when the information was collected, thereby enabling efficient generation.

[0051] The generation unit can adjust the order of generation based on the relevance of information at the time of generation. The generation unit, for example, prioritizes generation based on highly relevant information. For example, the generation unit evaluates the relevance of collected information and prioritizes generation based on highly relevant information. The generation unit can also postpone information with low relevance. For example, the generation unit evaluates the relevance of collected information and postpones information with low relevance. The generation unit can also prioritize generation based on information that the user is most interested in. For example, the generation unit prioritizes generation based on information that the user is most interested in. In this way, the generation unit adjusts the order of generation based on the relevance of information, thereby enabling efficient generation.

[0052] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise at the time of generation. For example, if the user has technical expertise, the generation unit uses a lot of technical terminology. For example, the generation unit evaluates the user's level of expertise and uses a lot of technical terminology for users who have technical expertise. The generation unit can also avoid technical terminology if the user does not have technical expertise. For example, the generation unit evaluates the user's level of expertise and avoids technical terminology for users who do not have technical expertise. The generation unit can also use appropriate terminology according to the user's level of expertise. For example, the generation unit evaluates the user's level of expertise and uses appropriate terminology. In this way, the generation unit can provide a generation AI that is easy for users to understand by adjusting the use of technical terminology in the generation according to the user's level of expertise.

[0053] The providing unit can select an appropriate display method by referring to the user's past operation history when providing the display. The providing unit, for example, preferentially uses a display method that the user has previously preferred. For example, the providing unit preferentially selects a display method that the user has preferred based on the user's past operation history. The providing unit can also improve the display method based on the user's past operation history. For example, the providing unit analyzes the user's past operation history and optimizes the display method. The providing unit can also suggest a new display method by referring to the user's past operation history. For example, the providing unit suggests a new display method based on the user's past operation history. In this way, the providing unit can provide a display method that is easy for the user to understand by selecting the optimal display method by referring to the user's past operation history.

[0054] The providing unit can customize the display content according to the user's current task at the time of providing the information. For example, when the user is in a meeting, the providing unit prioritizes displaying information related to the meeting. For example, the providing unit prioritizes displaying minutes and remarks made during the meeting. Furthermore, when the user is exercising, the providing unit can also prioritize displaying data related to exercise. For example, the providing unit prioritizes displaying the heart rate and calorie consumption during exercise. Furthermore, when the user is relaxing, the providing unit can also prioritize displaying information related to relaxation. For example, the providing unit prioritizes displaying music and environmental sounds played while relaxing. In this way, the providing unit can provide optimal information for the user by customizing the display content according to the user's current task.

[0055] The providing unit can improve the providing method by reflecting the user's feedback at the time of providing information. The providing unit, for example, preferentially uses a providing method that the user has previously preferred. For example, the providing unit preferentially selects a providing method that the user has preferred based on the user's past feedback. The providing unit can also improve the providing method based on the user's past feedback. For example, the providing unit analyzes the user's past feedback and optimizes the providing method. The providing unit can also suggest a new providing method by referring to the user's past feedback. For example, the providing unit suggests a new providing method based on the user's past feedback. In this way, the providing unit can improve the providing method by reflecting the user's feedback, thereby providing optimal information for the user.

[0056] The providing unit can select an appropriate display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. For example, the providing unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit provides a display method optimized for the smartwatch screen size. In this way, the providing unit can provide a display method that is easy for the user to understand by selecting the optimal display method by taking into consideration the user's device information.

[0057] The providing unit can make the display content multilingual according to the user's language setting when providing the information. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the providing unit detects the language setting of the user's device and sets the display content based on that language. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit allows the user to select the language to use and provides the display content in the selected language. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit sets the display content based on the language selected by the user. In this way, the providing unit can provide information that is easy for the user to understand by making the display content multilingual according to the user's language setting.

[0058] The providing unit can customize the delivery method by reflecting the user's past feedback when providing information. The providing unit, for example, preferentially uses a delivery method that the user has previously preferred. For example, the providing unit preferentially selects a delivery method that the user has preferred based on the user's past feedback. The providing unit can also improve the delivery method based on the user's past feedback. For example, the providing unit analyzes the user's past feedback and optimizes the delivery method. The providing unit can also suggest a new delivery method by referring to the user's past feedback. For example, the providing unit suggests a new delivery method based on the user's past feedback. In this way, the providing unit can provide optimal information for the user by customizing the delivery method by reflecting the user's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can learn the user's behavioral patterns and predict the optimal collection timing. For example, the collection unit can analyze the time periods and locations where the user has previously recorded or recorded video and audio, and predict the next collection timing. The collection unit can also refer to the user's calendar information and adjust the collection timing based on the user's schedule. Furthermore, the collection unit can prioritize the most frequently used device based on the user's device usage history. This allows the collection unit to efficiently collect information based on the user's behavioral patterns.

[0061] The analysis unit can evaluate the reliability of collected information and prioritize analysis of highly reliable information. For example, the analysis unit calculates a reliability score based on the source of the information and the collection method, and prioritizes analysis of highly reliable information. The analysis unit can also check the consistency of the information and prioritize analysis of consistent information. Furthermore, the analysis unit can evaluate the freshness of the information and prioritize analysis of the most recent information. This allows the analysis unit to provide accurate analysis results based on highly reliable information.

[0062] The providing unit can adjust the providing method taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the providing unit can provide the generated AI in a lightweight data format. Also, when the remaining battery level is sufficient, the providing unit can provide the generated AI in a high-quality data format. Furthermore, the providing unit can adjust the providing frequency according to the remaining battery level. This allows the providing unit to select the optimal providing method according to the battery status of the user's device.

[0063] The analysis unit can understand the context of the collected information and provide analysis results based on the context. For example, when analyzing recorded information from a meeting, the analysis unit can take into account the meeting topic and the roles of the participants. When analyzing recorded information from a trip, the analysis unit can also take into account the culture and history of the travel destination. Furthermore, when analyzing recorded information from the user's daily life, the analysis unit can take into account the user's lifestyle and hobbies. This allows the analysis unit to understand the context of the information and provide more accurate analysis results.

[0064] The collection unit can select the type of information to collect based on the user's current activity. For example, if the user is in a meeting, the collection unit can prioritize collecting audio data. Also, if the user is exercising, the collection unit can prioritize collecting video data. Furthermore, if the user is relaxing, the collection unit can prioritize collecting text data. This allows the collection unit to select the optimal information type depending on the user's activity.

[0065] The analysis unit can evaluate the diversity of collected information and integrate the diverse information to provide an analysis result. For example, the analysis unit can integrate and analyze audio data and video data. The analysis unit can also integrate and analyze text data and image data. Furthermore, the analysis unit can integrate and analyze sensor data and user input data. This allows the analysis unit to integrate the diverse information to provide a comprehensive analysis result.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects video recording information or audio recording information. For example, the collection unit can collect information in cooperation with devices such as smartphones, tablets, and smart speakers. The collection unit can also collect information such as video conference recordings and voice memos. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit converts the recorded information into text data using voice recognition technology. The analysis unit can also analyze the video recording information using image analysis technology. Furthermore, the analysis unit can also analyze the collected information using text analysis technology. Step 3: The generation unit generates a generation AI based on the information analyzed by the analysis unit. For example, the generation unit generates new text using a text generation AI. The generation unit can also generate new images using an image generation AI. The generation unit can also generate new audio using a voice generation AI. Step 4: The providing unit provides the generated AI generated by the generating unit to the user. For example, the providing unit may provide the generated AI through a web application. The providing unit may also provide the generated AI through a mobile application. Furthermore, the providing unit may also provide the generated AI through an API.

[0068] (Example 2) A system according to an embodiment of the present invention implements a generative AI in an AI device and generates a generative AI by utilizing video and audio recordings in cooperation with various devices and APIs. This system implements a generative AI in an AI device, collects video and audio recordings in cooperation with various devices and APIs, and the generative AI analyzes the collected information to generate a generative AI. Furthermore, ideas that arise in daily life can be easily created as generative AI through dialogue. For example, an AI device cooperates with devices such as smartphones, tablets, and smart speakers to collect video and audio recordings. The collected information is analyzed by the generative AI to generate a generative AI. This allows users to create generative AI without coding. This system allows users to create generative AI without coding. For example, even users without programming knowledge can realize new ideas by utilizing the generative AI. Furthermore, by cooperating with various devices and APIs, the generative AI can create a generative AI based on the user's daily activities. This allows users to live more convenient and efficient lives.

[0069] A generative AI system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video recording information or audio recording information. For example, the collection unit can collect information in cooperation with devices such as smartphones, tablets, and smart speakers. The collection unit can also collect information such as video conference recordings and voice memos. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit converts audio recording information into text data using speech recognition technology. The analysis unit can also analyze the video recording information using image analysis technology. The analysis unit can also analyze the collected information using text analysis technology. The generation unit generates a generative AI based on the information analyzed by the analysis unit. For example, the generation unit can generate new text using a text generation AI. The generation unit can also generate new images using an image generation AI. The generation unit can also generate new audio using an audio generation AI. The provision unit provides the generative AI generated by the generation unit to a user. For example, the provision unit can provide the generative AI through a web application. The provision unit can also provide the generative AI through a mobile application. Furthermore, the providing unit can provide the generating AI through an API. As a result, the generating AI system according to the embodiment can generate the generating AI using video recording information and audio recording information and provide it to the user.

[0070] The collection unit can collect information in cooperation with at least one device selected from the group consisting of a smartphone, a tablet, and a smart speaker. The collection unit collects information using, for example, a smartphone's camera or microphone. For example, the collection unit can record the user's daily activities using the smartphone's camera. The collection unit can also record the user's voice using the smartphone's microphone. Furthermore, the collection unit can collect information in cooperation with a tablet or smart speaker. For example, the collection unit can record the user's actions using the tablet's camera. The collection unit can also record the user's voice using the smart speaker's microphone. This allows the collection unit to collect information in cooperation with various devices.

[0071] The analysis unit can analyze the collected video or audio information. The analysis unit can convert the audio information into text data using, for example, voice recognition technology. For example, the analysis unit can analyze the audio information using voice recognition software and save it as text data. The analysis unit can also analyze the video information using image analysis technology. For example, the analysis unit can analyze the video information using image recognition software to extract important scenes. The analysis unit can also analyze the collected information using text analysis technology. For example, the analysis unit can analyze the text data using natural language processing technology to extract important information. In this way, the analysis unit can provide the data necessary for generating generative AI by analyzing the collected information.

[0072] The generation unit can generate a generation AI based on the collected information. The generation unit, for example, generates new text using a text generation AI. For example, the generation unit generates new sentences based on collected text data. The generation unit can also generate new images using an image generation AI. For example, the generation unit generates new images based on collected image data. The generation unit can also generate new audio using a voice generation AI. For example, the generation unit generates new audio based on collected audio data. This allows the generation unit to generate a generation AI based on the collected information.

[0073] The providing unit can provide the generated generated AI to the user. The providing unit can provide the generated AI through, for example, a web application. For example, the providing unit can provide the generated AI to the user through a web browser. The providing unit can also provide the generated AI through a mobile application. For example, the providing unit can provide the generated AI to the user through an app on a smartphone or tablet. Furthermore, the providing unit can also provide the generated AI through an API. For example, the providing unit can provide an API for providing the generated AI in cooperation with other systems. This allows the providing unit to provide the generated generated AI to the user.

[0074] The providing unit can create the generative AI through dialogue. For example, the providing unit creates the generative AI by interacting with the user using a chatbot. For example, when the user instructs the chatbot to "think of a new recipe," the generative AI generates a new recipe. The providing unit can also create the generative AI by interacting with the user using a voice assistant. For example, when the user instructs the voice assistant to "tell me a new idea," the generative AI generates a new idea. In this way, the providing unit can create the generative AI through dialogue.

[0075] The collection unit can estimate the user's emotion and adjust the start timing of video or audio recording based on the estimated user's emotion. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expression and adjusts the start timing of video or audio recording. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the start timing of video or audio recording. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on fluctuations in heart rate and adjusts the start timing of video or audio recording. In this way, the collection unit can adjust the start timing of video or audio recording according to the user's emotion, thereby collecting important moments without missing them.

[0076] The collection unit can analyze the user's past behavioral history at the time of collection and select an appropriate collection method. The collection unit, for example, analyzes the user's past behavioral history and sets the optimal collection timing. For example, the collection unit sets the optimal collection timing based on time periods during which the user frequently recorded in the past. The collection unit can also prioritize selecting devices that the user has used favorably in the past. For example, the collection unit prioritizes smartphones or tablets that the user has used favorably in the past. The collection unit can also analyze the user's past behavioral patterns and suggest the most efficient collection method. For example, the collection unit suggests the optimal collection method based on the user's past behavioral patterns. This allows the collection unit to select the optimal collection method based on the user's past behavioral history, thereby enabling efficient information collection.

[0077] The collection unit can perform filtering based on the user's current activity at the time of collection. For example, if the user is in a meeting, the collection unit collects only information related to the content of the meeting. For example, the collection unit prioritizes collecting remarks made during the meeting and minutes of the meeting. Furthermore, if the user is exercising, the collection unit can also prioritize collecting data related to exercise. For example, the collection unit collects the heart rate and calorie consumption during exercise. Furthermore, if the user is relaxing, the collection unit can also collect music and environmental sounds related to relaxation. For example, the collection unit collects music and natural sounds played while relaxing. In this way, the collection unit can collect highly relevant information by filtering based on the user's current activity.

[0078] The collection unit can select an appropriate collection means depending on the user's input method when collecting data. For example, when the user is using voice input, the collection unit prioritizes collecting voice data. For example, when the user is using voice input, the collection unit collects voice data using a microphone. The collection unit can also prioritize collecting text data when the user is using text input. For example, when the user is using text input, the collection unit collects text data using a keyboard or touch screen. The collection unit can also prioritize collecting gesture data when the user is using gesture input. For example, when the user is using gesture input, the collection unit collects gesture data using a camera or sensor. This allows the collection unit to select the optimal collection means depending on the user's input method, thereby enabling efficient information collection.

[0079] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions and determines the priority of information to be collected. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of information to be collected. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on fluctuations in heart rate and determines the priority of information to be collected. In this way, the collection unit can prioritize the collection of important information by determining the priority of information to be collected according to the user's emotions.

[0080] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, when the user is in a specific location, the collection unit prioritizes collecting information related to that location. For example, when the user is in a tourist destination, the collection unit prioritizes collecting information about the tourist destination. Furthermore, when the user is traveling, the collection unit can also prioritize collecting tourist information about the travel destination. For example, the collection unit collects information about tourist spots and restaurants at the user's travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting information about the area around the user's home. For example, the collection unit collects event information and store information about the area around the user's home. This allows the collection unit to prioritize collecting highly relevant information by taking into account the user's geographical location information, thereby enabling efficient information collection.

[0081] The collection unit can analyze the user's social media activities at the time of collection and collect related information. The collection unit, for example, collects information about places where the user has checked in on social media. For example, the collection unit collects information about restaurants and cafes where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related information. For example, the collection unit analyzes photos and comments posted by the user on social media and collects related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit collects related information based on information shared by the user's friends on social media. In this way, the collection unit can analyze the user's social media activities and collect related information, thereby enabling efficient information collection.

[0082] The collection unit can customize the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit preferentially uses the collection method that the user previously preferred. For example, the collection unit customizes the collection method based on the user's past feedback. The collection unit can also improve the collection method based on the user's past feedback. For example, the collection unit analyzes feedback provided by the user in the past and optimizes the collection method. The collection unit can also suggest a new collection method by referring to the user's past feedback. For example, the collection unit suggests a new device or timing based on feedback provided by the user in the past. In this way, the collection unit can customize the collection method by reflecting the user's past feedback, thereby enabling efficient information collection.

[0083] The analysis unit can estimate the user's emotion and adjust the method of presentation of the analysis based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression and adjusts the method of presentation of the analysis. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the method of presentation of the analysis. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on fluctuations in heart rate and adjusts the method of presentation of the analysis. In this way, the analysis unit can adjust the method of presentation of the analysis according to the user's emotion, thereby providing analysis results that are easy for the user to understand.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of information collected during analysis. The analysis unit, for example, performs a detailed analysis on important information. For example, the analysis unit evaluates the importance of the collected information and performs a detailed analysis on the important information. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit evaluates the importance of the collected information and performs a concise analysis on the general information. The analysis unit can also perform a detailed analysis on information that is of great interest to the user. For example, the analysis unit prioritizes analysis of information that is of great interest to the user and provides a detailed analysis result. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the collected information, thereby enabling efficient analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a video analysis algorithm to video information. For example, the analysis unit uses a video analysis algorithm to analyze video information. The analysis unit can also apply an audio analysis algorithm to audio information. For example, the analysis unit uses an audio analysis algorithm to analyze audio information. The analysis unit can also apply a text analysis algorithm to text information. For example, the analysis unit uses a text analysis algorithm to analyze text information. This allows the analysis unit to apply different analysis algorithms depending on the category of information, thereby enabling efficient analysis.

[0086] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and optimizes the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and suggest an optimal analysis method. For example, the analysis unit suggests an optimal analysis method based on the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results, thereby enabling efficient analysis.

[0087] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression and adjusts the length of the analysis. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the analysis. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on fluctuations in heart rate and adjusts the length of the analysis. In this way, the analysis unit can adjust the length of the analysis according to the user's emotion, thereby providing analysis results that are easy for the user to understand.

[0088] The analysis unit can determine the priority of analysis based on the time when information was collected during analysis. The analysis unit, for example, prioritizes analysis of the most recent information. For example, the analysis unit prioritizes analysis of the most recent information based on the time when the collected information was collected. The analysis unit can also prioritize analysis of important information by referring to past information. For example, the analysis unit prioritizes analysis of important information by referring to past information based on the time when the collected information was collected. The analysis unit can also prioritize analysis of information from a period when the user was most interested. For example, the analysis unit prioritizes analysis based on information from a period when the user was most interested. In this way, the analysis unit can determine the priority of analysis based on the time when the information was collected, thereby enabling efficient analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, prioritizes analysis of highly relevant information. For example, the analysis unit evaluates the relevance of collected information and prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. For example, the analysis unit evaluates the relevance of collected information and prioritizes analysis of less relevant information. The analysis unit can also prioritize analysis of information that is of great interest to the user. For example, the analysis unit prioritizes analysis based on information that is of great interest to the user. In this way, the analysis unit can adjust the order of analysis based on the relevance of information, thereby enabling efficient analysis.

[0090] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. For example, the analysis unit evaluates the user's level of expertise and uses a lot of technical terms for users who have technical expertise. The analysis unit can also avoid technical terms if the user does not have technical expertise. For example, the analysis unit evaluates the user's level of expertise and avoids technical terms for users who do not have technical expertise. The analysis unit can also use appropriate terms according to the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise and uses appropriate terms. In this way, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand.

[0091] The generation unit can estimate the user's emotions and adjust the characteristics of the generated AI based on the estimated user emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions and adjusts the characteristics of the generated AI. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the characteristics of the generated AI. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on fluctuations in heart rate and adjusts the characteristics of the generated AI. In this way, the generation unit can adjust the characteristics of the generated AI according to the user's emotions, thereby providing the generated AI that is optimal for the user.

[0092] The generation unit can adjust the level of detail of the generation based on the importance of the information analyzed during generation. The generation unit, for example, generates detailed information for important information. For example, the generation unit evaluates the importance of the analyzed information and generates detailed information for the important information. The generation unit can also generate concise information for general information. For example, the generation unit evaluates the importance of the analyzed information and generates concise information for the general information. The generation unit can also generate detailed information for information that is of great interest to the user. For example, the generation unit prioritizes generating information that is of great interest to the user and provides a detailed generation result. This allows the generation unit to adjust the level of detail of the generation based on the importance of the analyzed information, thereby enabling efficient generation.

[0093] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, the generation unit applies a video generation algorithm to video information. For example, the generation unit uses a video generation algorithm to generate video information. The generation unit can also apply a voice generation algorithm to voice information. For example, the generation unit uses a voice generation algorithm to generate voice information. The generation unit can also apply a text generation algorithm to text information. For example, the generation unit uses a text generation algorithm to generate text information. This allows the generation unit to apply different generation algorithms depending on the category of information, thereby enabling efficient generation.

[0094] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. For example, the generation unit analyzes the user's past generation results and optimizes the generation algorithm. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of generation based on the user's past generation results. The generation unit can also analyze the user's past generation results and propose an optimal generation method. For example, the generation unit proposes an optimal generation method based on the user's past generation results. In this way, the generation unit can improve the accuracy of generation by referring to the user's past generation results, thereby enabling efficient generation.

[0095] The generation unit can estimate the user's emotions and determine the priority of the AI ​​to be generated based on the estimated user emotions. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression and determines the priority of the AI ​​to be generated. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the AI ​​to be generated. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on fluctuations in heart rate and determines the priority of the AI ​​to be generated. In this way, the generation unit can determine the priority of the AI ​​to be generated according to the user's emotions, thereby providing the optimal generated AI for the user.

[0096] The generation unit can determine the priority of generation based on the time when the information was collected at the time of generation. The generation unit, for example, prioritizes generation based on the latest information. For example, the generation unit prioritizes generation of the latest information based on the time when the collected information was collected. The generation unit can also prioritize important generation by referring to past information. For example, the generation unit prioritizes generation of important information by referring to past information based on the time when the collected information was collected. The generation unit can also prioritize generation based on information from a period when the user was most interested. For example, the generation unit prioritizes generation based on information from a period when the user was most interested. In this way, the generation unit can determine the priority of generation based on the time when the information was collected, thereby enabling efficient generation.

[0097] The generation unit can adjust the order of generation based on the relevance of information at the time of generation. The generation unit, for example, prioritizes generation based on highly relevant information. For example, the generation unit evaluates the relevance of collected information and prioritizes generation based on highly relevant information. The generation unit can also postpone information with low relevance. For example, the generation unit evaluates the relevance of collected information and postpones information with low relevance. The generation unit can also prioritize generation based on information that the user is most interested in. For example, the generation unit prioritizes generation based on information that the user is most interested in. In this way, the generation unit adjusts the order of generation based on the relevance of information, thereby enabling efficient generation.

[0098] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise at the time of generation. For example, if the user has technical expertise, the generation unit uses a lot of technical terminology. For example, the generation unit evaluates the user's level of expertise and uses a lot of technical terminology for users who have technical expertise. The generation unit can also avoid technical terminology if the user does not have technical expertise. For example, the generation unit evaluates the user's level of expertise and avoids technical terminology for users who do not have technical expertise. The generation unit can also use appropriate terminology according to the user's level of expertise. For example, the generation unit evaluates the user's level of expertise and uses appropriate terminology. In this way, the generation unit can provide a generation AI that is easy for users to understand by adjusting the use of technical terminology in the generation according to the user's level of expertise.

[0099] The providing unit can estimate the user's emotions and adjust the display method of the AI ​​to be provided based on the estimated user emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expressions and adjusts the display method of the AI ​​to be provided. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the AI ​​to be provided. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on fluctuations in heart rate and adjusts the display method of the AI ​​to be provided. In this way, the providing unit can adjust the display method of the AI ​​to be provided according to the user's emotions, thereby providing the optimal display method for the user.

[0100] The providing unit can select an appropriate display method by referring to the user's past operation history when providing the display. The providing unit, for example, preferentially uses a display method that the user has previously preferred. For example, the providing unit preferentially selects a display method that the user has preferred based on the user's past operation history. The providing unit can also improve the display method based on the user's past operation history. For example, the providing unit analyzes the user's past operation history and optimizes the display method. The providing unit can also suggest a new display method by referring to the user's past operation history. For example, the providing unit suggests a new display method based on the user's past operation history. In this way, the providing unit can provide a display method that is easy for the user to understand by selecting the optimal display method by referring to the user's past operation history.

[0101] The providing unit can customize the display content according to the user's current task at the time of providing the information. For example, when the user is in a meeting, the providing unit prioritizes displaying information related to the meeting. For example, the providing unit prioritizes displaying minutes and remarks made during the meeting. Furthermore, when the user is exercising, the providing unit can also prioritize displaying data related to exercise. For example, the providing unit prioritizes displaying the heart rate and calorie consumption during exercise. Furthermore, when the user is relaxing, the providing unit can also prioritize displaying information related to relaxation. For example, the providing unit prioritizes displaying music and environmental sounds played while relaxing. In this way, the providing unit can provide optimal information for the user by customizing the display content according to the user's current task.

[0102] The providing unit can improve the providing method by reflecting the user's feedback at the time of providing information. The providing unit, for example, preferentially uses a providing method that the user has previously preferred. For example, the providing unit preferentially selects a providing method that the user has preferred based on the user's past feedback. The providing unit can also improve the providing method based on the user's past feedback. For example, the providing unit analyzes the user's past feedback and optimizes the providing method. The providing unit can also suggest a new providing method by referring to the user's past feedback. For example, the providing unit suggests a new providing method based on the user's past feedback. In this way, the providing unit can improve the providing method by reflecting the user's feedback, thereby providing optimal information for the user.

[0103] The providing unit can estimate the user's emotions and adjust the AI's operation procedures to be provided based on the estimated user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expressions and adjusts the AI's operation procedures to be provided. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the AI's operation procedures to be provided. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on fluctuations in heart rate and adjusts the AI's operation procedures to be provided. In this way, the providing unit can adjust the AI's operation procedures to be provided according to the user's emotions, thereby providing the optimal operation procedures for the user.

[0104] The providing unit can select an appropriate display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit provides a display method that matches the screen size. For example, the providing unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit provides a display method optimized for the smartwatch screen size. In this way, the providing unit can provide a display method that is easy for the user to understand by selecting the optimal display method by taking into consideration the user's device information.

[0105] The providing unit can make the display content multilingual according to the user's language setting when providing the information. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the providing unit detects the language setting of the user's device and sets the display content based on that language. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit allows the user to select the language to use and provides the display content in the selected language. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit sets the display content based on the language selected by the user. In this way, the providing unit can provide information that is easy for the user to understand by making the display content multilingual according to the user's language setting.

[0106] The providing unit can customize the delivery method by reflecting the user's past feedback when providing information. The providing unit, for example, preferentially uses a delivery method that the user has previously preferred. For example, the providing unit preferentially selects a delivery method that the user has preferred based on the user's past feedback. The providing unit can also improve the delivery method based on the user's past feedback. For example, the providing unit analyzes the user's past feedback and optimizes the delivery method. The providing unit can also suggest a new delivery method by referring to the user's past feedback. For example, the providing unit suggests a new delivery method based on the user's past feedback. In this way, the providing unit can provide optimal information for the user by customizing the delivery method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect video information and audio information using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The generation unit generates a generated AI based on the information analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated AI generated by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 to the user. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect video information and audio information using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The generation unit generates a generated AI based on the information analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated AI generated by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect video information and audio information using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The generation unit generates a generated AI based on the information analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated AI generated by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect video information and audio information using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The generation unit generates a generated AI based on the information analyzed by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated AI generated by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 to the user.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The collection unit can learn the user's behavioral patterns and predict the optimal collection timing. For example, the collection unit can analyze the time periods and locations where the user has previously recorded or recorded video and audio, and predict the next collection timing. The collection unit can also refer to the user's calendar information and adjust the collection timing based on the user's schedule. Furthermore, the collection unit can prioritize the most frequently used device based on the user's device usage history. This allows the collection unit to efficiently collect information based on the user's behavioral patterns.

[0109] The analysis unit can evaluate the reliability of collected information and prioritize analysis of highly reliable information. For example, the analysis unit calculates a reliability score based on the source of the information and the collection method, and prioritizes analysis of highly reliable information. The analysis unit can also check the consistency of the information and prioritize analysis of consistent information. Furthermore, the analysis unit can evaluate the freshness of the information and prioritize analysis of the most recent information. This allows the analysis unit to provide accurate analysis results based on highly reliable information.

[0110] The generation unit can customize the style of the generated AI based on the user's preferences. For example, the generation unit can learn the text style and image style that the user has previously preferred and generate new ones based on that. The generation unit can also adjust the generation style based on user feedback. Furthermore, the generation unit can change the generation style depending on the user's current mood and situation. This allows the generation unit to provide a generation AI that matches the user's preferences.

[0111] The providing unit can adjust the providing method taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, the providing unit can provide the generated AI in a lightweight data format. Also, when the remaining battery level is sufficient, the providing unit can provide the generated AI in a high-quality data format. Furthermore, the providing unit can adjust the providing frequency according to the remaining battery level. This allows the providing unit to select the optimal providing method according to the battery status of the user's device.

[0112] The collection unit can estimate the user's emotions and select the type of information to collect based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information related to relaxation. Also, if the user is excited, the collection unit can prioritize collecting information related to entertainment. Furthermore, if the user is concentrating, the collection unit can prioritize collecting information related to work or study. This allows the collection unit to collect optimal information according to the user's emotions.

[0113] The analysis unit can understand the context of the collected information and provide analysis results based on the context. For example, when analyzing recorded information from a meeting, the analysis unit can take into account the meeting topic and the roles of the participants. When analyzing recorded information from a trip, the analysis unit can also take into account the culture and history of the travel destination. Furthermore, when analyzing recorded information from the user's daily life, the analysis unit can take into account the user's lifestyle and hobbies. This allows the analysis unit to understand the context of the information and provide more accurate analysis results.

[0114] The generation unit can estimate the user's emotions and adjust the tone of the generated AI based on the estimated emotions. For example, if the user is depressed, the generation unit can generate text in an encouraging tone. If the user is happy, the generation unit can also generate text in a congratulatory tone. Furthermore, if the user is relaxed, the generation unit can generate text in a calm tone. This allows the generation unit to provide the generated AI with the optimal tone depending on the user's emotions.

[0115] The providing unit can estimate the user's emotion and adjust the amount of information to be provided based on the estimated emotion. For example, the providing unit can provide a small amount of information when the user is tired. Also, the providing unit can provide a large amount of information when the user is excited. Furthermore, the providing unit can provide an appropriate amount of information when the user is relaxed. This allows the providing unit to provide an optimal amount of information according to the user's emotion.

[0116] The collection unit can select the type of information to collect based on the user's current activity. For example, if the user is in a meeting, the collection unit can prioritize collecting audio data. Also, if the user is exercising, the collection unit can prioritize collecting video data. Furthermore, if the user is relaxing, the collection unit can prioritize collecting text data. This allows the collection unit to select the optimal information type depending on the user's activity.

[0117] The analysis unit can evaluate the diversity of collected information and integrate the diverse information to provide an analysis result. For example, the analysis unit can integrate and analyze audio data and video data. The analysis unit can also integrate and analyze text data and image data. Furthermore, the analysis unit can integrate and analyze sensor data and user input data. This allows the analysis unit to integrate the diverse information to provide a comprehensive analysis result.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects video recording information or audio recording information. For example, the collection unit can collect information in cooperation with devices such as smartphones, tablets, and smart speakers. The collection unit can also collect information such as video conference recordings and voice memos. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit converts the recorded information into text data using voice recognition technology. The analysis unit can also analyze the video recording information using image analysis technology. Furthermore, the analysis unit can also analyze the collected information using text analysis technology. Step 3: The generation unit generates a generation AI based on the information analyzed by the analysis unit. For example, the generation unit generates new text using a text generation AI. The generation unit can also generate new images using an image generation AI. The generation unit can also generate new audio using a voice generation AI. Step 4: The providing unit provides the generated AI generated by the generating unit to the user. For example, the providing unit may provide the generated AI through a web application. The providing unit may also provide the generated AI through a mobile application. Furthermore, the providing unit may also provide the generated AI through an API.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects video recording information or audio recording information; an analysis unit that analyzes the information collected by the collection unit; A generation unit that generates a generated AI based on the information analyzed by the analysis unit; a providing unit that provides the generated AI generated by the generating unit to a user; Equipped with A system characterized by:

2. The collecting unit Collect information by linking with at least one device from the following: a smartphone, tablet, or smart speaker 2. The system of claim 1.

3. The analysis unit Analyze the collected video or audio information 2. The system of claim 1.

4. The generation unit Generate generative AI based on collected information 2. The system of claim 1.

5. The providing unit Providing the generated AI to the user 2. The system of claim 1.

6. The providing unit Creating generative AI through dialogue 2. The system of claim 1.

7. The collecting unit Estimate user's emotion and adjust start timing of video or audio recording based on the estimated user's emotion 2. The system of claim 1.

8. The collecting unit When collecting data, analyze the user's past behavior history and select the appropriate collection method.

2. The system of claim 1.

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