system

Abstract

The system according to this embodiment aims to efficiently record and organize daily events and health management information, and to utilize this information for future actions. [Solution] The system according to the embodiment comprises a recording unit, an organization unit, a guidance unit, a meal recording unit, a suggestion unit, and an editing unit. The recording unit records the faces, names, and conversations of people met. The organization unit organizes the information recorded by the recording unit and registers appointments in a calendar. The guidance unit guides users to their destinations based on the times recorded in the calendar. The meal recording unit records images, ingredients, and calories of meals eaten. The suggestion unit suggests the next healthy meal based on the information recorded by the meal recording unit. The editing unit analyzes images taken by the camera and edits memorable moments from the day into short videos.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that daily events and health management are not sufficiently recorded, organized, and utilized for the next actions.

[0005] The system according to the embodiment aims to efficiently record, organize, and utilize daily events and health management for the next actions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recording unit, an organization unit, a guidance unit, a meal recording unit, a suggestion unit, and an editing unit. The recording unit records the faces, names, and conversations of people met. The organization unit organizes the information recorded by the recording unit and registers appointments in a calendar. The guidance unit guides users to their destinations based on the times recorded in the calendar. The meal recording unit records images, ingredients, and calories of meals eaten. The suggestion unit suggests the next healthy meal based on the information recorded by the meal recording unit. The editing unit analyzes images captured by a camera and edits memorable moments from the day into short videos. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently record and organize daily events and health management information, and use this information to inform future actions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The multifunctional AI terminal system according to an embodiment of the present invention is a multifunctional device that supports the user's life. This multifunctional AI terminal system is equipped with a camera, a recorder, and GPS, and provides the following functions. First, it organizes the faces, names, and conversations of people the user has met, and registers appointments in a calendar. Next, it guides the user to their destination based on the time recorded in the calendar. It also records images, ingredients, and calories of meals eaten, and suggests healthy meals for the next time. Furthermore, it edits memorable moments from the day into short videos. The terminal is shaped like a stuffed animal that can be worn on the shoulder and attached with a clip, and the built-in equipment makes it easier to utilize the camera's functions. For example, the camera and recorder record the faces, names, and conversations of people the user has met. The AI ​​analyzes this information, identifies the faces of people met using facial recognition technology, and organizes their names and conversations. For example, it automatically organizes the names and conversations of people met at a business meeting and registers appointments in a calendar. Next, it guides the user to their destination based on the time recorded in the calendar. It uses GPS to determine the user's current location and calculates the optimal route based on the schedule registered in the calendar. For example, it guides the user to the optimal route to arrive on time for the start of a meeting. Furthermore, it records images, ingredients, and calories of meals eaten. The camera captures images of meals, and the AI ​​identifies ingredients and calculates calories. Based on this, it suggests healthy meals for the next meal. For example, if a high-calorie meal is eaten for lunch, a low-calorie meal is suggested for dinner. In addition, it edits memorable moments from the day into short videos. The AI ​​analyzes the images captured by the camera, extracts important scenes, and edits them into short videos. For example, it can create a video summarizing enjoyable moments during a trip. In this way, the AI ​​terminal equipped with a camera, recorder, and GPS supports the user's life, managing schedules, health, and recording memories. The terminal's shape resembles a plush toy that rests on the shoulder and clips onto it, and the built-in equipment makes it easier to utilize the camera's functions. This allows the multi-functional AI terminal system to support the user's life, managing schedules, health, and recording memories.

[0029] The multifunctional AI terminal system according to this embodiment comprises a recording unit, an organization unit, a guidance unit, a meal recording unit, a suggestion unit, and an editing unit. The recording unit records the faces, names, and conversations of people met. For example, the recording unit can use a camera to photograph the faces of people met and a recorder to record the conversations. The recording unit can also use facial recognition technology to identify the faces of people met and organize their names and conversations. For example, the recording unit can automatically organize the names and conversations of people met at a business meeting and register the appointments in a calendar. The organization unit organizes the information recorded by the recording unit and registers appointments in a calendar. For example, the organization unit can analyze the faces, names, and conversations recorded by the recording unit and extract and organize important information. The organization unit can also use AI to classify information and determine priorities. For example, the organization unit prioritizes the organization of business meeting content and registers important appointments in a calendar. The guidance unit guides the user to their destination based on the time recorded in the calendar. The navigation unit uses GPS to determine the user's current location and calculates the optimal route based on the user's schedule registered in the calendar. For example, the navigation unit can guide the user along the best route to ensure they arrive on time for a meeting. The navigation unit can also guide the user using voice guidance and map displays. For example, the navigation unit can provide directions via voice guidance and display the user's current location and destination on a map. The meal recording unit records images, ingredients, and calories of meals eaten. The meal recording unit takes pictures of meals with a camera, and AI identifies ingredients and calculates calories. For example, if the user has a high-calorie meal for lunch, the meal recording unit records this information and uses it to guide the user's next meal. The suggestion unit suggests healthy meals based on the information recorded by the meal recording unit. The suggestion unit uses AI to evaluate the nutritional balance of meals and suggests the next meal. For example, if the user has a high-calorie meal for lunch, the suggestion unit suggests a low-calorie meal for dinner. The editing unit analyzes the images taken with the camera and edits memorable moments from the day into short videos. The editorial team uses AI to analyze the footage, extract important scenes, and edit them into short videos. For example, they might create a video summarizing the fun moments during a trip.As a result, the multi-functional AI terminal system according to this embodiment can support the user's life and perform tasks such as schedule management, health management, and recording of memories.

[0030] The recording unit records the faces, names, and content of conversations with people met. For example, it might use a camera to photograph a person's face and a recorder to record their conversation. The recording unit can also use facial recognition technology to identify faces and organize them along with their names and conversation content. Specifically, the camera captures high-resolution images, and a facial recognition algorithm identifies individual faces. The facial recognition algorithm uses deep learning to train on a large amount of facial image data, achieving highly accurate facial recognition. The recorder is equipped with a high-sensitivity microphone, recording clear audio while removing ambient noise. The recorded audio data is converted into text data using speech recognition technology. Speech recognition technology uses natural language processing (NLP) to analyze the speaker's intent and extract important keywords and phrases. For example, it can analyze conversations in business meetings and automatically extract important topics and decisions. The recording unit centrally manages this information, making it easy for users to search and refer to later. Furthermore, the recording unit can encrypt data and control access to protect user privacy. This allows the recording unit to safely and efficiently record and manage important user information.

[0031] The organization unit organizes the information recorded by the recording unit and registers appointments in the calendar. For example, the organization unit analyzes faces, names, and conversation content recorded by the recording unit to extract and organize important information. The organization unit can also classify information and determine priorities using AI. Specifically, it uses natural language processing (NLP) technology to extract important keywords and phrases from conversation content and classify information based on them. For example, it analyzes the content of a business meeting and organizes the information by agenda item. In addition, to determine priorities, it refers to the user's past activity history and calendar appointments and prioritizes the organization of highly important information. The organization unit automatically registers this information in the calendar, making it easy for the user to check their appointments. Furthermore, the organization unit can continuously improve its information organization methods based on user feedback. For example, if a user determines that certain information is important, the algorithm is adjusted to prioritize the organization of that information. This allows the organization unit to achieve flexible information organization that meets user needs and streamline schedule management.

[0032] The navigation system guides users to their destinations based on the times listed on their calendars. It uses GPS to determine the user's current location and calculates the optimal route based on the schedule registered in the calendar. Specifically, the GPS device receives signals from satellites to pinpoint the user's current location with high accuracy. Based on the user's current location and destination, the navigation system consults a map database to calculate the optimal route. The route calculation uses algorithms that consider traffic conditions, road congestion, and mode of transport (walking, cycling, driving, etc.). For example, it can guide users to the shortest route to arrive on time for a meeting. The navigation system can also guide users using voice guidance and map displays. Voice guidance provides directions verbally, allowing users to reach their destinations without relying on visual information. Map displays visually show the user's current location and destination, enabling them to intuitively understand the direction they should be heading. Furthermore, the navigation system can dynamically change routes based on real-time updated traffic information. For example, in the event of traffic congestion or an accident, it calculates and guides the user to the optimal detour route. This allows the navigation system to support users in reaching their destinations efficiently and safely.

[0033] The meal recording unit records images, ingredients, and calories of meals eaten. The unit uses a camera to capture images of meals, and AI identifies ingredients and calculates calories. Specifically, the camera captures high-resolution images, and the AI ​​uses image recognition technology to identify ingredients. This image recognition technology utilizes deep learning to learn from a large amount of ingredient image data, achieving highly accurate ingredient recognition. Once ingredients are identified, the AI ​​consults a nutrition database to calculate the calories of each ingredient. For example, if a high-calorie meal is consumed for lunch, this information is recorded and reflected in the next meal. The meal recording unit centrally manages the calories consumed by the user, supporting health management. Furthermore, based on the user's meal history, the meal recording unit can evaluate nutritional balance and provide advice for maintaining a healthy diet. For example, by analyzing past meal history, if a specific nutrient is deficient, it suggests incorporating ingredients containing that nutrient into the next meal. In this way, the meal recording unit efficiently supports the user's health management and helps them achieve a balanced diet.

[0034] The suggestion unit proposes the next healthy meal based on the information recorded by the meal record unit. The suggestion unit uses AI to evaluate the nutritional balance of meals and proposes the next meal. Specifically, the AI ​​analyzes the user's meal history and evaluates the balance of nutrients consumed. The evaluation of nutritional balance uses an algorithm that takes into account the recommended intake of each nutrient and the user's health condition. For example, if a high-calorie meal was eaten for lunch, a low-calorie meal will be suggested for dinner. The suggestion unit can also propose appropriate meals by taking into account the user's preferences and allergy information. For example, if the user is allergic to a specific ingredient, a menu that does not contain that ingredient will be suggested. The suggestion unit also provides specific recipes and cooking methods to support the user in easily preparing healthy meals. Furthermore, the suggestion unit can continuously improve its suggestions based on user feedback. For example, if the user likes the suggested meal, the next suggestion will be adjusted based on that information. In this way, the suggestion unit can support the user's health management and provide flexible meal suggestions that meet individual needs.

[0035] The editorial team analyzes footage captured by cameras and edits memorable moments from the day into short videos. Using AI, the team analyzes the footage, extracts key scenes, and edits them into short videos. Specifically, the AI ​​recognizes people, objects, and events within the footage to identify important moments. For example, it automatically extracts fun moments and special events during a trip. In addition to video analysis, the AI ​​also analyzes audio and text data to more accurately identify important scenes. The editorial team combines these key scenes to create visually appealing short videos. Video editing utilizes techniques such as transition effects, music addition, and text insertion. For example, when editing travel footage to create a video summarizing fun moments, transition effects are used to smoothly switch between scenes, and background music is added to enhance the atmosphere. Furthermore, the editorial team can create customized videos according to the user's preferences and style. For example, if a user likes specific music or themes, they can create videos incorporating those elements. This allows the editorial team to beautifully edit and preserve users' memories in a visually appealing way.

[0036] The recording unit can identify the faces of people met using facial recognition technology. For example, the recording unit uses facial recognition technology based on deep learning. The recording unit can also identify faces using pattern matching technology. Furthermore, the recording unit can use facial recognition technology to identify the faces of people met and organize their names and the content of conversations. This allows for accurate identification of faces of people met using facial recognition technology.

[0037] The navigation unit can determine the user's current location using GPS and calculate the optimal route based on the schedule registered in the calendar. For example, the navigation unit can acquire location information using GPS to determine the user's current location. It can also calculate the optimal route considering traffic conditions and travel time. Furthermore, the navigation unit can determine the user's current location using GPS, calculate the optimal route based on the schedule registered in the calendar, and guide the user accordingly. This allows for accurate location determination and guidance via the optimal route using GPS.

[0038] The meal recording unit can capture images of meals with a camera, and AI can identify ingredients and calculate calories. For example, the meal recording unit can use a camera to capture images of meals. Furthermore, the meal recording unit can use AI to identify ingredients and calculate calories, and then use that information to inform future meals. Thus, by using a camera and AI, ingredients can be identified from images of meals and calories can be calculated.

[0039] The suggestion unit can suggest the next healthy meal based on the information recorded by the meal log unit. For example, the suggestion unit can suggest the next meal based on the ingredients and calorie information recorded by the meal log unit. Furthermore, the suggestion unit can use AI to evaluate the nutritional balance of a meal and suggest the next meal. In addition, the suggestion unit can suggest and provide the next healthy meal to the user based on the information recorded by the meal log unit. This allows for the suggestion of the next healthy meal based on the information from the meal log unit.

[0040] The editorial team can analyze footage captured by cameras, extract important scenes, and edit them into short videos. For example, the editorial team can analyze footage captured by cameras using AI. Furthermore, the editorial team can extract important scenes and edit them into short videos. In addition, the editorial team can analyze footage captured by cameras, extract important scenes, edit them into short videos, and provide them to users. This allows for the analysis of footage captured by cameras, the extraction of important scenes, and the editing into short videos.

[0041] The recording unit can translate conversations in real time and record them in multiple languages. For example, if a user is conversing with a foreigner, the recording unit will translate the conversation in real time and record it in both the user's native language and the other person's language. Furthermore, when used in business meetings, the recording unit can record conversations in multiple languages ​​for later review. Additionally, when conversing with locals while traveling, the recording unit can translate conversations in real time and save them as a travel record. This allows for real-time translation and multilingual recording of conversations.

[0042] The recording unit can analyze the tone and speed of a conversation during recording and highlight important information. For example, it can automatically detect emphasized parts of a conversation and record them as important information. Furthermore, if the conversation is fast, the recording unit can extract and record important keywords. In addition, the recording unit can detect changes in the tone of the conversation and highlight those parts during recording. This allows the system to analyze the tone and speed of a conversation and highlight important information during recording.

[0043] The recording unit can filter out background noise during recording, resulting in clear audio. For example, when recording a conversation in a noisy environment, the recording unit filters out background noise to produce clear audio. It can also filter out sounds from air conditioners and projectors when recording a conversation in a conference room, resulting in clear audio. Furthermore, when recording a conversation outdoors, the recording unit can filter out wind and traffic noise to produce clear audio. This allows for the filtering of background noise during conversations, resulting in clear audio recording.

[0044] The recording unit can analyze the context of a conversation during recording and automatically tag relevant information. For example, it can automatically tag keywords that appear in the conversation to make them easier to search later. The recording unit can also analyze the context of the conversation and group and tag relevant information. Furthermore, it can automatically tag names of people and places that appear in the conversation for later reference. This allows the system to analyze the context of a conversation and automatically tag relevant information.

[0045] The organization unit can analyze the relationships between pieces of information during the organization process and group related information together. For example, the organization unit can group related information based on keywords that appear in a conversation. It can also analyze the context of a conversation and automatically group related information. Furthermore, the organization unit can group related information based on names of people and places that appear in a conversation. This allows for the analysis of information relationships and the grouping of related information.

[0046] The organization function can adjust the display order based on the importance of the information during organization. For example, it can prioritize displaying important appointments and tasks so that they can be reviewed later. It can also display conversation content based on importance, making related information easier to find. Furthermore, it can prioritize displaying important information based on keywords that appear in conversations. This allows for adjusting the display order based on the importance of the information.

[0047] The organization unit can automatically categorize information and display it visually during the organization process. For example, the organization unit can categorize information based on keywords that appear in a conversation and display it visually. Furthermore, the organization unit can analyze the context of a conversation and automatically categorize the information. Additionally, the organization unit can categorize information based on names of people and places that appear in a conversation and display it visually. This allows for the automatic categorization and visual display of information.

[0048] The organization unit can organize information while considering its chronological order. For example, it can organize the content of a conversation chronologically so that it can be reviewed later. It can also organize keywords that appear in a conversation chronologically, making related information easier to find. Furthermore, it can organize names of people and places that appear in a conversation chronologically so that they can be reviewed later. In this way, information can be organized while considering its chronological order.

[0049] The guidance system can analyze traffic conditions in real time and recalculate the optimal route during guidance. For example, it can suggest the optimal route based on real-time traffic congestion information. It can also suggest the optimal route considering the real-time operation status of public transportation. Furthermore, it can suggest detour routes based on real-time road construction information. This allows for real-time analysis of traffic conditions and recalculation of the optimal route.

[0050] The guidance unit can adjust the pace of the guidance based on the user's walking speed and mode of transportation. For example, if the user is walking slowly, the guidance unit will slow down the guidance pace. Conversely, if the user is in a hurry, the guidance unit can speed up the guidance pace. Furthermore, if the user is using a bicycle or car, the guidance unit can provide a guidance pace that matches their mode of transportation. This allows the guidance pace to be adjusted based on the user's walking speed and mode of transportation.

[0051] The information desk can provide information on nearby tourist attractions and shops when providing guidance. For example, if a user is visiting a tourist destination, the information desk can provide information on nearby tourist attractions. It can also provide information on nearby shops if the user is shopping. Furthermore, if the user is looking for a restaurant, the information desk can provide information on nearby restaurants. This improves user convenience by providing information on nearby tourist attractions and shops.

[0052] The navigation system can suggest preferred routes by referring to the user's past travel history during navigation. For example, it can suggest the optimal route based on routes the user has used in the past. It can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and suggest the most efficient route. This allows the system to suggest preferred routes by referring to the user's past travel history.

[0053] The meal log section can analyze the nutritional balance of meals and assess health status at the time of recording. For example, it can analyze the nutritional balance of meals and assess vitamin and mineral intake. It can also assess the balance of protein, fat, and carbohydrates. Furthermore, it can assess excessive or deficient nutrients. This allows for analysis of the nutritional balance of meals and assessment of health status.

[0054] The meal recording unit can evaluate the impact of meals by considering the time of meal intake during recording. For example, the meal recording unit can evaluate the efficiency of digestion and absorption by considering the time of meal intake. It can also evaluate fluctuations in blood glucose levels. Furthermore, the meal recording unit can evaluate the impact on weight management. This allows for the evaluation of the impact of meals by considering the time of meal intake.

[0055] The meal recording unit can analyze photos of meals during recording and evaluate the freshness of the ingredients. For example, it can analyze photos of meals and evaluate freshness based on the color and texture of the ingredients. Furthermore, the meal recording unit can assess health risks based on the freshness of the ingredients. In addition, the meal recording unit can evaluate the storage conditions of the ingredients. This allows for the analysis of meal photos and the evaluation of the freshness of the ingredients.

[0056] The meal recording unit can automatically generate and save recipes for meals as they are recorded. For example, it can analyze photos of meals and generate recipes based on the ingredients used. It can also estimate cooking methods and generate recipes. Furthermore, it can generate recipes that consider nutritional balance. This allows for the automatic generation and saving of meal recipes.

[0057] The suggestion function can suggest meals that match the user's preferences by referring to the user's past eating history. For example, the suggestion function can suggest meals that match the user's preferences based on the user's past eating history. Furthermore, the suggestion function can suggest meals that consider nutritional balance based on the user's past eating history. In addition, the suggestion function can analyze the user's past eating history and suggest meals appropriate for their health condition. This allows the system to suggest meals that match the user's preferences by referring to their past eating history.

[0058] The proposal department can suggest appropriate meals, taking into account the season and weather. For example, it can suggest meals using seasonal ingredients. It can also suggest meals suitable for regulating body temperature according to the weather. Furthermore, it can suggest meals that consider nutritional balance according to the season and weather. This allows for the suggestion of appropriate meals considering the season and weather.

[0059] The suggestion department can propose realistic meals by considering the availability of ingredients. For example, it can propose realistic meals based on ingredients available in the user's area. It can also propose realistic meals based on seasonally available ingredients. Furthermore, it can propose realistic meals based on available ingredients while considering the user's budget. In this way, it can propose realistic meals while considering the availability of ingredients.

[0060] The suggestion function can propose safe meals by taking into account the user's allergy information. For example, the suggestion function can propose safe meals based on the user's allergy information. Furthermore, the suggestion function can also propose meals using alternative ingredients, taking the user's allergy information into consideration. In addition, the suggestion function can propose meals that pose no allergy risk, based on the user's allergy information. This allows for the proposal of safe meals while considering the user's allergy information.

[0061] The editorial team can apply filters that automatically improve video quality during editing. For example, the editorial team can automatically adjust the brightness and contrast of the video to improve quality. They can also automatically remove noise from the video to improve quality. Furthermore, the editorial team can automatically correct the color tone of the video to improve quality. This allows for the application of filters that automatically improve video quality.

[0062] The editorial team can optimize the synchronization of audio and video during editing. For example, the editorial team can automatically adjust the timing of audio and video to optimize synchronization. Furthermore, the editorial team can automatically compensate for audio delays to synchronize with the video. In addition, the editorial team can automatically detect discrepancies between audio and video and optimize synchronization. This allows for optimal synchronization of audio and video.

[0063] The editorial team can add text and subtitles to the video during the editing process. For example, they can add text to match the video's content, supplementing the visual information. They can also transcribe the video's audio and add it as subtitles. Furthermore, they can add text to highlight important points in the video, visually emphasizing them. In this way, adding text and subtitles to the video can supplement the visual information.

[0064] The editorial team can automatically select and add music based on the video's theme during the editing process. For example, they can automatically select and add music appropriate to the video's theme. They can also select music to match the video's atmosphere, making it enjoyable both visually and aurally. Furthermore, they can select music to complement the video's content, enhancing its emotional impact. This allows for the automatic selection and addition of music based on the video's theme.

[0065] The device's shape can be adjusted to suit different usage scenarios. For example, the device's shape can be adjusted to provide a secure hold during sports activities. It can also be adjusted to be less conspicuous during work. Furthermore, it can be adjusted to be easily portable while traveling. This allows the device's shape to be adjusted to accommodate various usage situations.

[0066] By changing the shape of the device, it is possible to create space to extend battery life. For example, the device shape can be modified to create space for increasing battery capacity. Furthermore, the device shape can be modified to incorporate an efficient heat dissipation structure, thus extending battery life. Additionally, the device shape can be modified to allow for easier battery replacement. This demonstrates that by changing the device shape, space can be created to extend battery life.

[0067] The device's shape can be adjusted to accommodate clips of different sizes and shapes. For example, the device's shape can be adjusted to accommodate clips of different sizes. Furthermore, the device's shape can be designed to allow for easy removal of clips. This allows the device's shape to be adjusted to accommodate clips of different sizes and shapes.

[0068] The shape of the device can be modified to improve its waterproof performance. For example, waterproof seals can be added to improve waterproof performance. Alternatively, a waterproof cover can be attached. Furthermore, waterproof materials can be used to improve waterproof performance. In this way, the shape of the device can be modified to improve its waterproof performance.

[0069] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0070] The multi-functional AI terminal system can monitor the user's health status and issue alerts if abnormalities are detected. For example, the meal recording unit analyzes the user's diet and detects imbalances in nutrition. The suggestion unit can then suggest meals to improve nutritional balance if necessary. Furthermore, the editorial unit can create and provide reports summarizing information about the user's health status. This system can support the user's health management.

[0071] The multi-functional AI terminal system can analyze a user's activity history and provide personalized feedback. For example, the recording unit can record the user's exercise level and sleep duration, and evaluate their health status. The suggestion unit can also suggest appropriate exercise plans if the user is not getting enough exercise. Furthermore, the editing unit can create and provide motivational videos based on the activity history. This helps support the user in leading a healthy lifestyle.

[0072] The multi-functional AI terminal system can analyze a user's learning history and propose an effective learning plan. For example, the recording unit records the user's learning content and progress and evaluates the learning effectiveness. The suggestion unit can propose a learning plan for improvement if the learning effectiveness is low. Furthermore, the editing unit can create review videos based on the learning history and provide them to the user. This can improve the user's learning effectiveness.

[0073] A multi-functional AI terminal system can analyze a user's hobbies and interests and provide personalized recommendations. For example, the recording unit records and analyzes information about the user's hobbies and interests. The suggestion unit can suggest events and activities that match the user's hobbies. Furthermore, the editing unit can edit and provide content based on the user's interests. This allows the system to provide recommendations tailored to the user's hobbies and interests.

[0074] The multi-functional AI terminal system can analyze the user's lifestyle patterns and support efficient time management. For example, the recording unit records and analyzes the user's lifestyle patterns. The organization unit can optimize the schedule based on the user's lifestyle patterns. Furthermore, the suggestion unit can provide advice for efficient time management. In this way, it can support efficient time management based on the user's lifestyle patterns.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The recording unit records the faces, names, and conversations of the people it meets. For example, it can use a camera to photograph the faces of the people it meets and a recorder to record the conversations. The recording unit can also use facial recognition technology to identify the faces of the people it meets and organize their names and conversations. Step 2: The organization unit organizes the information recorded by the recording unit and registers appointments in the calendar. For example, it analyzes faces, names, and conversations recorded by the recording unit, extracting and organizing important information. The organization unit can also use AI to classify information and determine priorities. Step 3: The navigation unit guides the user to their destination based on the time recorded in the calendar. It uses GPS to determine the user's current location and calculates the optimal route based on the schedule registered in the calendar. The navigation unit can also guide the user using voice guidance and map display. Step 4: The meal log section records the image, ingredients, and calories of the meal eaten. The camera takes a picture of the meal, and the AI ​​identifies the ingredients and calculates the calories. Step 5: The suggestion unit proposes the next healthy meal based on the information recorded by the meal record unit. It uses AI to evaluate the nutritional balance of the meal and proposes the next meal. Step 6: The editorial team analyzes the footage captured by the camera and edits memorable moments from the day into short videos. AI is used to analyze the footage, extract important scenes, and edit them into short videos.

[0077] (Example of form 2) The multifunctional AI terminal system according to an embodiment of the present invention is a multifunctional device that supports the user's life. This multifunctional AI terminal system is equipped with a camera, a recorder, and GPS, and provides the following functions. First, it organizes the faces, names, and conversations of people the user has met, and registers appointments in a calendar. Next, it guides the user to their destination based on the time recorded in the calendar. It also records images, ingredients, and calories of meals eaten, and suggests healthy meals for the next time. Furthermore, it edits memorable moments from the day into short videos. The terminal is shaped like a stuffed animal that can be worn on the shoulder and attached with a clip, and the built-in equipment makes it easier to utilize the camera's functions. For example, the camera and recorder record the faces, names, and conversations of people the user has met. The AI ​​analyzes this information, identifies the faces of people met using facial recognition technology, and organizes their names and conversations. For example, it automatically organizes the names and conversations of people met at a business meeting and registers appointments in a calendar. Next, it guides the user to their destination based on the time recorded in the calendar. It uses GPS to determine the user's current location and calculates the optimal route based on the schedule registered in the calendar. For example, it guides the user to the optimal route to arrive on time for the start of a meeting. Furthermore, it records images, ingredients, and calories of meals eaten. The camera captures images of meals, and the AI ​​identifies ingredients and calculates calories. Based on this, it suggests healthy meals for the next meal. For example, if a high-calorie meal is eaten for lunch, a low-calorie meal is suggested for dinner. In addition, it edits memorable moments from the day into short videos. The AI ​​analyzes the images captured by the camera, extracts important scenes, and edits them into short videos. For example, it can create a video summarizing enjoyable moments during a trip. In this way, the AI ​​terminal equipped with a camera, recorder, and GPS supports the user's life, managing schedules, health, and recording memories. The terminal's shape resembles a plush toy that rests on the shoulder and clips onto it, and the built-in equipment makes it easier to utilize the camera's functions. This allows the multi-functional AI terminal system to support the user's life, managing schedules, health, and recording memories.

[0078] The multifunctional AI terminal system according to this embodiment comprises a recording unit, an organization unit, a guidance unit, a meal recording unit, a suggestion unit, and an editing unit. The recording unit records the faces, names, and conversations of people met. For example, the recording unit can use a camera to photograph the faces of people met and a recorder to record the conversations. The recording unit can also use facial recognition technology to identify the faces of people met and organize their names and conversations. For example, the recording unit can automatically organize the names and conversations of people met at a business meeting and register the appointments in a calendar. The organization unit organizes the information recorded by the recording unit and registers appointments in a calendar. For example, the organization unit can analyze the faces, names, and conversations recorded by the recording unit and extract and organize important information. The organization unit can also use AI to classify information and determine priorities. For example, the organization unit prioritizes the organization of business meeting content and registers important appointments in a calendar. The guidance unit guides the user to their destination based on the time recorded in the calendar. The navigation unit uses GPS to determine the user's current location and calculates the optimal route based on the user's schedule registered in the calendar. For example, the navigation unit can guide the user along the best route to ensure they arrive on time for a meeting. The navigation unit can also guide the user using voice guidance and map displays. For example, the navigation unit can provide directions via voice guidance and display the user's current location and destination on a map. The meal recording unit records images, ingredients, and calories of meals eaten. The meal recording unit takes pictures of meals with a camera, and AI identifies ingredients and calculates calories. For example, if the user has a high-calorie meal for lunch, the meal recording unit records this information and uses it to guide the user's next meal. The suggestion unit suggests healthy meals based on the information recorded by the meal recording unit. The suggestion unit uses AI to evaluate the nutritional balance of meals and suggests the next meal. For example, if the user has a high-calorie meal for lunch, the suggestion unit suggests a low-calorie meal for dinner. The editing unit analyzes the images taken with the camera and edits memorable moments from the day into short videos. The editorial team uses AI to analyze the footage, extract important scenes, and edit them into short videos. For example, they might create a video summarizing the fun moments during a trip.As a result, the multi-functional AI terminal system according to this embodiment can support the user's life and perform tasks such as schedule management, health management, and recording of memories.

[0079] The recording unit records the faces, names, and content of conversations with people met. For example, it might use a camera to photograph a person's face and a recorder to record their conversation. The recording unit can also use facial recognition technology to identify faces and organize them along with their names and conversation content. Specifically, the camera captures high-resolution images, and a facial recognition algorithm identifies individual faces. The facial recognition algorithm uses deep learning to train on a large amount of facial image data, achieving highly accurate facial recognition. The recorder is equipped with a high-sensitivity microphone, recording clear audio while removing ambient noise. The recorded audio data is converted into text data using speech recognition technology. Speech recognition technology uses natural language processing (NLP) to analyze the speaker's intent and extract important keywords and phrases. For example, it can analyze conversations in business meetings and automatically extract important topics and decisions. The recording unit centrally manages this information, making it easy for users to search and refer to later. Furthermore, the recording unit can encrypt data and control access to protect user privacy. This allows the recording unit to safely and efficiently record and manage important user information.

[0080] The organization unit organizes the information recorded by the recording unit and registers appointments in the calendar. For example, the organization unit analyzes faces, names, and conversation content recorded by the recording unit to extract and organize important information. The organization unit can also classify information and determine priorities using AI. Specifically, it uses natural language processing (NLP) technology to extract important keywords and phrases from conversation content and classify information based on them. For example, it analyzes the content of a business meeting and organizes the information by agenda item. In addition, to determine priorities, it refers to the user's past activity history and calendar appointments and prioritizes the organization of highly important information. The organization unit automatically registers this information in the calendar, making it easy for the user to check their appointments. Furthermore, the organization unit can continuously improve its information organization methods based on user feedback. For example, if a user determines that certain information is important, the algorithm is adjusted to prioritize the organization of that information. This allows the organization unit to achieve flexible information organization that meets user needs and streamline schedule management.

[0081] The navigation system guides users to their destinations based on the times listed on their calendars. It uses GPS to determine the user's current location and calculates the optimal route based on the schedule registered in the calendar. Specifically, the GPS device receives signals from satellites to pinpoint the user's current location with high accuracy. Based on the user's current location and destination, the navigation system consults a map database to calculate the optimal route. The route calculation uses algorithms that consider traffic conditions, road congestion, and mode of transport (walking, cycling, driving, etc.). For example, it can guide users to the shortest route to arrive on time for a meeting. The navigation system can also guide users using voice guidance and map displays. Voice guidance provides directions verbally, allowing users to reach their destinations without relying on visual information. Map displays visually show the user's current location and destination, enabling them to intuitively understand the direction they should be heading. Furthermore, the navigation system can dynamically change routes based on real-time updated traffic information. For example, in the event of traffic congestion or an accident, it calculates and guides the user to the optimal detour route. This allows the navigation system to support users in reaching their destinations efficiently and safely.

[0082] The meal recording unit records images, ingredients, and calories of meals eaten. The unit uses a camera to capture images of meals, and AI identifies ingredients and calculates calories. Specifically, the camera captures high-resolution images, and the AI ​​uses image recognition technology to identify ingredients. This image recognition technology utilizes deep learning to learn from a large amount of ingredient image data, achieving highly accurate ingredient recognition. Once ingredients are identified, the AI ​​consults a nutrition database to calculate the calories of each ingredient. For example, if a high-calorie meal is consumed for lunch, this information is recorded and reflected in the next meal. The meal recording unit centrally manages the calories consumed by the user, supporting health management. Furthermore, based on the user's meal history, the meal recording unit can evaluate nutritional balance and provide advice for maintaining a healthy diet. For example, by analyzing past meal history, if a specific nutrient is deficient, it suggests incorporating ingredients containing that nutrient into the next meal. In this way, the meal recording unit efficiently supports the user's health management and helps them achieve a balanced diet.

[0083] The suggestion unit proposes the next healthy meal based on the information recorded by the meal record unit. The suggestion unit uses AI to evaluate the nutritional balance of meals and proposes the next meal. Specifically, the AI ​​analyzes the user's meal history and evaluates the balance of nutrients consumed. The evaluation of nutritional balance uses an algorithm that takes into account the recommended intake of each nutrient and the user's health condition. For example, if a high-calorie meal was eaten for lunch, a low-calorie meal will be suggested for dinner. The suggestion unit can also propose appropriate meals by taking into account the user's preferences and allergy information. For example, if the user is allergic to a specific ingredient, a menu that does not contain that ingredient will be suggested. The suggestion unit also provides specific recipes and cooking methods to support the user in easily preparing healthy meals. Furthermore, the suggestion unit can continuously improve its suggestions based on user feedback. For example, if the user likes the suggested meal, the next suggestion will be adjusted based on that information. In this way, the suggestion unit can support the user's health management and provide flexible meal suggestions that meet individual needs.

[0084] The editorial team analyzes footage captured by cameras and edits memorable moments from the day into short videos. Using AI, the team analyzes the footage, extracts key scenes, and edits them into short videos. Specifically, the AI ​​recognizes people, objects, and events within the footage to identify important moments. For example, it automatically extracts fun moments and special events during a trip. In addition to video analysis, the AI ​​also analyzes audio and text data to more accurately identify important scenes. The editorial team combines these key scenes to create visually appealing short videos. Video editing utilizes techniques such as transition effects, music addition, and text insertion. For example, when editing travel footage to create a video summarizing fun moments, transition effects are used to smoothly switch between scenes, and background music is added to enhance the atmosphere. Furthermore, the editorial team can create customized videos according to the user's preferences and style. For example, if a user likes specific music or themes, they can create videos incorporating those elements. This allows the editorial team to beautifully edit and preserve users' memories in a visually appealing way.

[0085] The recording unit can identify the faces of people met using facial recognition technology. For example, the recording unit uses facial recognition technology based on deep learning. The recording unit can also identify faces using pattern matching technology. Furthermore, the recording unit can use facial recognition technology to identify the faces of people met and organize their names and the content of conversations. This allows for accurate identification of faces of people met using facial recognition technology.

[0086] The navigation unit can determine the user's current location using GPS and calculate the optimal route based on the schedule registered in the calendar. For example, the navigation unit can acquire location information using GPS to determine the user's current location. It can also calculate the optimal route considering traffic conditions and travel time. Furthermore, the navigation unit can determine the user's current location using GPS, calculate the optimal route based on the schedule registered in the calendar, and guide the user accordingly. This allows for accurate location determination and guidance via the optimal route using GPS.

[0087] The meal recording unit can capture images of meals with a camera, and AI can identify ingredients and calculate calories. For example, the meal recording unit can use a camera to capture images of meals. Furthermore, the meal recording unit can use AI to identify ingredients and calculate calories, and then use that information to inform future meals. Thus, by using a camera and AI, ingredients can be identified from images of meals and calories can be calculated.

[0088] The suggestion unit can suggest the next healthy meal based on the information recorded by the meal log unit. For example, the suggestion unit can suggest the next meal based on the ingredients and calorie information recorded by the meal log unit. Furthermore, the suggestion unit can use AI to evaluate the nutritional balance of a meal and suggest the next meal. In addition, the suggestion unit can suggest and provide the next healthy meal to the user based on the information recorded by the meal log unit. This allows for the suggestion of the next healthy meal based on the information from the meal log unit.

[0089] The editorial team can analyze footage captured by cameras, extract important scenes, and edit them into short videos. For example, the editorial team can analyze footage captured by cameras using AI. Furthermore, the editorial team can extract important scenes and edit them into short videos. In addition, the editorial team can analyze footage captured by cameras, extract important scenes, edit them into short videos, and provide them to users. This allows for the analysis of footage captured by cameras, the extraction of important scenes, and the editing into short videos.

[0090] The recording unit can estimate the user's emotions and prioritize the information to record based on those emotions. For example, if the user is excited, the recording unit may prioritize recording the content of conversations and leave detailed notes. If the user is relaxed, the recording unit may prioritize facial recognition and record the faces and names of people met in detail. Furthermore, if the user is tired, the recording unit may prioritize recording important appointments and tasks for later review. This allows for the prioritization of information to be recorded based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The recording unit can translate conversations in real time and record them in multiple languages. For example, if a user is conversing with a foreigner, the recording unit will translate the conversation in real time and record it in both the user's native language and the other person's language. Furthermore, when used in business meetings, the recording unit can record conversations in multiple languages ​​for later review. Additionally, when conversing with locals while traveling, the recording unit can translate conversations in real time and save them as a travel record. This allows for real-time translation and multilingual recording of conversations.

[0092] The recording unit can analyze the tone and speed of a conversation during recording and highlight important information. For example, it can automatically detect emphasized parts of a conversation and record them as important information. Furthermore, if the conversation is fast, the recording unit can extract and record important keywords. In addition, the recording unit can detect changes in the tone of the conversation and highlight those parts during recording. This allows the system to analyze the tone and speed of a conversation and highlight important information during recording.

[0093] The recording unit can estimate the user's emotions and adjust the level of detail in the information recorded based on the estimated emotions. For example, if the user is excited, the recording unit can record detailed information for later review. If the user is relaxed, the recording unit can record concise information, leaving only the important points. Furthermore, if the user is tired, the recording unit can record minimal information, allowing for the addition of details later. This allows the level of detail in the information recorded to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The recording unit can filter out background noise during recording, resulting in clear audio. For example, when recording a conversation in a noisy environment, the recording unit filters out background noise to produce clear audio. It can also filter out sounds from air conditioners and projectors when recording a conversation in a conference room, resulting in clear audio. Furthermore, when recording a conversation outdoors, the recording unit can filter out wind and traffic noise to produce clear audio. This allows for the filtering of background noise during conversations, resulting in clear audio recording.

[0095] The recording unit can analyze the context of a conversation during recording and automatically tag relevant information. For example, it can automatically tag keywords that appear in the conversation to make them easier to search later. The recording unit can also analyze the context of the conversation and group and tag relevant information. Furthermore, it can automatically tag names of people and places that appear in the conversation for later reference. This allows the system to analyze the context of a conversation and automatically tag relevant information.

[0096] The organizing unit can estimate the user's emotions and determine the priority of information to organize based on the estimated emotions. For example, if the user is excited, the organizing unit will prioritize organizing important appointments and tasks. If the user is relaxed, the organizing unit can also prioritize organizing conversational content. Furthermore, if the user is tired, the organizing unit can prioritize organizing concise information, allowing for the addition of details later. This allows for the prioritization of information to organize based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The organization unit can analyze the relationships between pieces of information during the organization process and group related information together. For example, the organization unit can group related information based on keywords that appear in a conversation. It can also analyze the context of a conversation and automatically group related information. Furthermore, the organization unit can group related information based on names of people and places that appear in a conversation. This allows for the analysis of information relationships and the grouping of related information.

[0098] The organization function can adjust the display order based on the importance of the information during organization. For example, it can prioritize displaying important appointments and tasks so that they can be reviewed later. It can also display conversation content based on importance, making related information easier to find. Furthermore, it can prioritize displaying important information based on keywords that appear in conversations. This allows for adjusting the display order based on the importance of the information.

[0099] The organizing unit can estimate the user's emotions and adjust the level of detail in the information it organizes based on the estimated emotions. For example, if the user is excited, the organizing unit can organize detailed information for later review. If the user is relaxed, the organizing unit can organize concise information, leaving only the essential points. Furthermore, if the user is tired, the organizing unit can organize minimal information, allowing for the addition of details later. This allows the level of detail in the information organized to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The organization unit can automatically categorize information and display it visually during the organization process. For example, the organization unit can categorize information based on keywords that appear in a conversation and display it visually. Furthermore, the organization unit can analyze the context of a conversation and automatically categorize the information. Additionally, the organization unit can categorize information based on names of people and places that appear in a conversation and display it visually. This allows for the automatic categorization and visual display of information.

[0101] The organization unit can organize information while considering its chronological order. For example, it can organize the content of a conversation chronologically so that it can be reviewed later. It can also organize keywords that appear in a conversation chronologically, making related information easier to find. Furthermore, it can organize names of people and places that appear in a conversation chronologically so that they can be reviewed later. In this way, information can be organized while considering its chronological order.

[0102] The guidance system can estimate the user's emotions and adjust its guidance method based on those emotions. For example, if the user is nervous, the guidance system can provide a simple and highly visible guidance method. If the user is relaxed, it can also provide guidance that includes detailed information. Furthermore, if the user is in a hurry, it can provide guidance that gets straight to the point. This allows the guidance method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The guidance system can analyze traffic conditions in real time and recalculate the optimal route during guidance. For example, it can suggest the optimal route based on real-time traffic congestion information. It can also suggest the optimal route considering the real-time operation status of public transportation. Furthermore, it can suggest detour routes based on real-time road construction information. This allows for real-time analysis of traffic conditions and recalculation of the optimal route.

[0104] The guidance unit can adjust the pace of the guidance based on the user's walking speed and mode of transportation. For example, if the user is walking slowly, the guidance unit will slow down the guidance pace. Conversely, if the user is in a hurry, the guidance unit can speed up the guidance pace. Furthermore, if the user is using a bicycle or car, the guidance unit can provide a guidance pace that matches their mode of transportation. This allows the guidance pace to be adjusted based on the user's walking speed and mode of transportation.

[0105] The guidance system can estimate the user's emotions and adjust the level of detail in the guidance based on the estimated emotions. For example, if the user is nervous, the guidance system can provide a simple and highly visible guidance method. If the user is relaxed, the guidance system can also provide a guidance method that includes detailed information. Furthermore, if the user is in a hurry, the guidance system can provide a concise guidance method. This allows the level of detail in the guidance to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The information desk can provide information on nearby tourist attractions and shops when providing guidance. For example, if a user is visiting a tourist destination, the information desk can provide information on nearby tourist attractions. It can also provide information on nearby shops if the user is shopping. Furthermore, if the user is looking for a restaurant, the information desk can provide information on nearby restaurants. This improves user convenience by providing information on nearby tourist attractions and shops.

[0107] The navigation system can suggest preferred routes by referring to the user's past travel history during navigation. For example, it can suggest the optimal route based on routes the user has used in the past. It can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and suggest the most efficient route. This allows the system to suggest preferred routes by referring to the user's past travel history.

[0108] The meal log unit can estimate the user's emotions and determine the priority of meal information to record based on the estimated emotions. For example, if the user is excited, the meal log unit may prioritize recording the calories of the meal. It may also prioritize recording the details of the ingredients if the user is relaxed. Furthermore, if the user is tired, the meal log unit may prioritize recording concise meal information. This allows the system to prioritize meal information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The meal log section can analyze the nutritional balance of meals and assess health status at the time of recording. For example, it can analyze the nutritional balance of meals and assess vitamin and mineral intake. It can also assess the balance of protein, fat, and carbohydrates. Furthermore, it can assess excessive or deficient nutrients. This allows for analysis of the nutritional balance of meals and assessment of health status.

[0110] The meal recording unit can evaluate the impact of meals by considering the time of meal intake during recording. For example, the meal recording unit can evaluate the efficiency of digestion and absorption by considering the time of meal intake. It can also evaluate fluctuations in blood glucose levels. Furthermore, the meal recording unit can evaluate the impact on weight management. This allows for the evaluation of the impact of meals by considering the time of meal intake.

[0111] The meal recording unit can estimate the user's emotions and adjust the level of detail in the recorded meal information based on the estimated emotions. For example, if the user is excited, the meal recording unit will record detailed meal information. If the user is relaxed, the meal recording unit can also record concise meal information. Furthermore, if the user is tired, the meal recording unit can record minimal meal information. This allows the level of detail in the recorded meal information to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The meal recording unit can analyze photos of meals during recording and evaluate the freshness of the ingredients. For example, it can analyze photos of meals and evaluate freshness based on the color and texture of the ingredients. Furthermore, the meal recording unit can assess health risks based on the freshness of the ingredients. In addition, the meal recording unit can evaluate the storage conditions of the ingredients. This allows for the analysis of meal photos and the evaluation of the freshness of the ingredients.

[0113] The meal recording unit can automatically generate and save recipes for meals as they are recorded. For example, it can analyze photos of meals and generate recipes based on the ingredients used. It can also estimate cooking methods and generate recipes. Furthermore, it can generate recipes that consider nutritional balance. This allows for the automatic generation and saving of meal recipes.

[0114] The suggestion unit can estimate the user's emotions and adjust the suggested meal content based on those emotions. For example, if the user is excited, the suggestion unit will suggest a meal suitable for energy replenishment. It can also suggest a meal with a relaxing effect if the user is relaxed. Furthermore, if the user is tired, it can suggest a meal with a restorative effect. This allows the suggested meal content to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The suggestion function can suggest meals that match the user's preferences by referring to the user's past eating history. For example, the suggestion function can suggest meals that match the user's preferences based on the user's past eating history. Furthermore, the suggestion function can suggest meals that consider nutritional balance based on the user's past eating history. In addition, the suggestion function can analyze the user's past eating history and suggest meals appropriate for their health condition. This allows the system to suggest meals that match the user's preferences by referring to their past eating history.

[0116] The proposal department can suggest appropriate meals, taking into account the season and weather. For example, it can suggest meals using seasonal ingredients. It can also suggest meals suitable for regulating body temperature according to the weather. Furthermore, it can suggest meals that consider nutritional balance according to the season and weather. This allows for the suggestion of appropriate meals considering the season and weather.

[0117] The suggestion unit can estimate the user's emotions and adjust the level of detail in the suggested meals based on those emotions. For example, if the user is excited, the suggestion unit will provide detailed meal suggestions. If the user is relaxed, the suggestion unit can provide concise meal suggestions. Furthermore, if the user is tired, the suggestion unit can provide minimal meal suggestions. This allows the level of detail in the suggested meals to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The suggestion department can propose realistic meals by considering the availability of ingredients. For example, it can propose realistic meals based on ingredients available in the user's area. It can also propose realistic meals based on seasonally available ingredients. Furthermore, it can propose realistic meals based on available ingredients while considering the user's budget. In this way, it can propose realistic meals while considering the availability of ingredients.

[0119] The suggestion function can propose safe meals by taking into account the user's allergy information. For example, the suggestion function can propose safe meals based on the user's allergy information. Furthermore, the suggestion function can also propose meals using alternative ingredients, taking the user's allergy information into consideration. In addition, the suggestion function can propose meals that pose no allergy risk, based on the user's allergy information. This allows for the proposal of safe meals while considering the user's allergy information.

[0120] The editorial team can estimate the user's emotions and adjust the content of the video based on those emotions. For example, if the user is excited, the editorial team can edit an energetic video. If the user is relaxed, the editorial team can edit a calm video. Furthermore, if the user is tired, the editorial team can edit a short, concise video. This allows the content of the video to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The editorial team can apply filters that automatically improve video quality during editing. For example, the editorial team can automatically adjust the brightness and contrast of the video to improve quality. They can also automatically remove noise from the video to improve quality. Furthermore, the editorial team can automatically correct the color tone of the video to improve quality. This allows for the application of filters that automatically improve video quality.

[0122] The editorial team can optimize the synchronization of audio and video during editing. For example, the editorial team can automatically adjust the timing of audio and video to optimize synchronization. Furthermore, the editorial team can automatically compensate for audio delays to synchronize with the video. In addition, the editorial team can automatically detect discrepancies between audio and video and optimize synchronization. This allows for optimal synchronization of audio and video.

[0123] The editorial team can estimate the user's emotions and adjust the length of the edited video based on those emotions. For example, if the user is excited, the editorial team can edit a longer video. If the user is relaxed, the editorial team can edit a video of a moderate length. Furthermore, if the user is tired, the editorial team can edit a shorter video. This allows the length of the edited video to be adjusted based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The editorial team can add text and subtitles to the video during the editing process. For example, they can add text to match the video's content, supplementing the visual information. They can also transcribe the video's audio and add it as subtitles. Furthermore, they can add text to highlight important points in the video, visually emphasizing them. In this way, adding text and subtitles to the video can supplement the visual information.

[0125] The editorial team can automatically select and add music based on the video's theme during the editing process. For example, they can automatically select and add music appropriate to the video's theme. They can also select music to match the video's atmosphere, making it enjoyable both visually and aurally. Furthermore, they can select music to complement the video's content, enhancing its emotional impact. This allows for the automatic selection and addition of music based on the video's theme.

[0126] The device's shape can be customized based on the user's emotions to provide a comfortable fit. For example, if the user is excited, the device's shape can be customized to provide a secure hold. If the user is relaxed, the device's shape can be customized to be soft and comfortable. Furthermore, if the user is tired, the device's shape can be customized to be lightweight and less burdensome. This allows the device's shape to be customized based on the user's emotions to provide a comfortable fit. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The device's shape can be adjusted to suit different usage scenarios. For example, the device's shape can be adjusted to provide a secure hold during sports activities. It can also be adjusted to be less conspicuous during work. Furthermore, it can be adjusted to be easily portable while traveling. This allows the device's shape to be adjusted to accommodate various usage situations.

[0128] By changing the shape of the device, it is possible to create space to extend battery life. For example, the device shape can be modified to create space for increasing battery capacity. Furthermore, the device shape can be modified to incorporate an efficient heat dissipation structure, thus extending battery life. Additionally, the device shape can be modified to allow for easier battery replacement. This demonstrates that by changing the device shape, space can be created to extend battery life.

[0129] The shape of a device can be designed based on the user's emotions to enhance its visual appeal. For example, if the user is excited, the device's shape can be designed with vibrant colors. If the user is relaxed, the device's shape can be designed with calming colors. Furthermore, if the user is tired, the device's shape can be designed with simple and highly visible features. This allows for the design of the device's shape based on the user's emotions, thereby enhancing its visual appeal. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0130] The device's shape can be adjusted to accommodate clips of different sizes and shapes. For example, the device's shape can be adjusted to accommodate clips of different sizes. Furthermore, the device's shape can be designed to allow for easy removal of clips. This allows the device's shape to be adjusted to accommodate clips of different sizes and shapes.

[0131] The shape of the device can be modified to improve its waterproof performance. For example, waterproof seals can be added to improve waterproof performance. Alternatively, a waterproof cover can be attached. Furthermore, waterproof materials can be used to improve waterproof performance. In this way, the shape of the device can be modified to improve its waterproof performance.

[0132] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0133] The multi-functional AI terminal system can estimate the user's emotions and evaluate their stress level based on those emotions. For example, the recording unit analyzes the user's facial expressions and tone of voice to estimate their stress level. Next, the organization unit can prioritize organizing information that promotes relaxation if the stress level is high. Furthermore, the suggestion unit can suggest relaxing foods and activities according to the stress level. This supports the user's stress management.

[0134] The multi-functional AI terminal system can monitor the user's health status and issue alerts if abnormalities are detected. For example, the meal recording unit analyzes the user's diet and detects imbalances in nutrition. The suggestion unit can then suggest meals to improve nutritional balance if necessary. Furthermore, the editorial unit can create and provide reports summarizing information about the user's health status. This system can support the user's health management.

[0135] The multi-functional AI terminal system can estimate the user's emotions and adjust its communication methods based on those emotions. For example, the recording unit can meticulously record the content of conversations when the user is excited, allowing for later review. The guidance unit can provide calm voice guidance when the user is relaxed. Furthermore, the suggestion unit can offer concise and easy-to-understand suggestions when the user is tired. This enables communication that is tailored to the user's emotions.

[0136] The multi-functional AI terminal system can analyze a user's activity history and provide personalized feedback. For example, the recording unit can record the user's exercise level and sleep duration, and evaluate their health status. The suggestion unit can also suggest appropriate exercise plans if the user is not getting enough exercise. Furthermore, the editing unit can create and provide motivational videos based on the activity history. This helps support the user in leading a healthy lifestyle.

[0137] The multi-functional AI terminal system can estimate the user's emotions and suggest entertainment content based on those emotions. For example, the recording unit can suggest relaxing music or videos if the user is relaxed. The suggestion unit can suggest energetic activities if the user is excited. Furthermore, the editing unit can edit and provide entertainment content tailored to the user's emotions. This allows the system to provide entertainment that matches the user's mood.

[0138] The multi-functional AI terminal system can analyze a user's learning history and propose an effective learning plan. For example, the recording unit records the user's learning content and progress and evaluates the learning effectiveness. The suggestion unit can propose a learning plan for improvement if the learning effectiveness is low. Furthermore, the editing unit can create review videos based on the learning history and provide them to the user. This can improve the user's learning effectiveness.

[0139] The multi-functional AI terminal system can estimate the user's emotions and adjust the content of reminders based on those emotions. For example, the recording unit prioritizes reminders of important tasks when the user is stressed. The organization unit can concisely organize the content of reminders when the user is relaxed. Furthermore, the suggestion unit can reduce the frequency of reminders to alleviate the burden on the user when they are tired. This allows the system to provide reminders tailored to the user's emotions.

[0140] A multi-functional AI terminal system can analyze a user's hobbies and interests and provide personalized recommendations. For example, the recording unit records and analyzes information about the user's hobbies and interests. The suggestion unit can suggest events and activities that match the user's hobbies. Furthermore, the editing unit can edit and provide content based on the user's interests. This allows the system to provide recommendations tailored to the user's hobbies and interests.

[0141] The multi-functional AI terminal system can estimate the user's emotions and adjust the notification method based on those emotions. For example, the recording unit can visually highlight notifications when the user is excited. The guidance unit can provide gentle voice notifications when the user is relaxed. Furthermore, the suggestion unit can reduce the frequency of notifications and provide only important notifications when the user is tired. This allows the system to provide notifications that are tailored to the user's emotions.

[0142] The multi-functional AI terminal system can analyze the user's lifestyle patterns and support efficient time management. For example, the recording unit records and analyzes the user's lifestyle patterns. The organization unit can optimize the schedule based on the user's lifestyle patterns. Furthermore, the suggestion unit can provide advice for efficient time management. In this way, it can support efficient time management based on the user's lifestyle patterns.

[0143] The following briefly describes the processing flow for example form 2.

[0144] Step 1: The recording unit records the faces, names, and conversations of the people it meets. For example, it can use a camera to photograph the faces of the people it meets and a recorder to record the conversations. The recording unit can also use facial recognition technology to identify the faces of the people it meets and organize their names and conversations. Step 2: The organization unit organizes the information recorded by the recording unit and registers appointments in the calendar. For example, it analyzes faces, names, and conversations recorded by the recording unit, extracting and organizing important information. The organization unit can also use AI to classify information and determine priorities. Step 3: The navigation unit guides the user to their destination based on the time recorded in the calendar. It uses GPS to determine the user's current location and calculates the optimal route based on the schedule registered in the calendar. The navigation unit can also guide the user using voice guidance and map display. Step 4: The meal log section records the image, ingredients, and calories of the meal eaten. The camera takes a picture of the meal, and the AI ​​identifies the ingredients and calculates the calories. Step 5: The suggestion unit proposes the next healthy meal based on the information recorded by the meal record unit. It uses AI to evaluate the nutritional balance of the meal and proposes the next meal. Step 6: The editorial team analyzes the footage captured by the camera and edits memorable moments from the day into short videos. AI is used to analyze the footage, extract important scenes, and edit them into short videos.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0147] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the recording unit, organization unit, guidance unit, meal recording unit, suggestion unit, and editing unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the recording unit uses the camera 42 and recorder of the smart device 14 to record the faces of people met and the content of conversations. The organization unit uses the identification processing unit 290 of the data processing unit 12 to organize the recorded information and register it in a calendar. The guidance unit uses the GPS function of the smart device 14 to determine the current location and guide the user to the optimal route. The meal recording unit uses the camera 42 of the smart device 14 to capture images of meals, and the identification processing unit 290 of the data processing unit 12 identifies the ingredients and calculates the calories. The suggestion unit uses the identification processing unit 290 of the data processing unit 12 to suggest the next healthy meal. The editing unit uses the identification processing unit 290 of the data processing unit 12 to analyze the images captured by the camera 42 of the smart device 14 and edit them into a short video. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0150] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0152] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the recording unit, organization unit, guidance unit, meal recording unit, suggestion unit, and editing unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit uses the camera 42 and recorder of the smart glasses 214 to record the faces of people met and the content of conversations. The organization unit uses the identification processing unit 290 of the data processing unit 12 to organize the recorded information and register it in a calendar. The guidance unit uses the GPS function of the smart glasses 214 to determine the current location and guide the user to the optimal route. The meal recording unit uses the camera 42 of the smart glasses 214 to capture images of meals, and the identification processing unit 290 of the data processing unit 12 identifies the ingredients and calculates the calories. The suggestion unit uses the identification processing unit 290 of the data processing unit 12 to suggest the next healthy meal. The editing unit uses the identification processing unit 290 of the data processing unit 12 to analyze the images captured by the camera 42 of the smart glasses 214 and edit them into a short video. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0165] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0166] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the recording unit, organization unit, guidance unit, meal recording unit, suggestion unit, and editing unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit uses the camera 42 and recorder of the headset terminal 314 to record the faces of people met and the content of conversations. The organization unit uses the identification processing unit 290 of the data processing unit 12 to organize the recorded information and register it in a calendar. The guidance unit uses the GPS function of the headset terminal 314 to determine the current location and guide the user to the optimal route. The meal recording unit uses the camera 42 of the headset terminal 314 to capture images of meals, and the identification processing unit 290 of the data processing unit 12 identifies the ingredients and calculates the calories. The suggestion unit uses the identification processing unit 290 of the data processing unit 12 to suggest the next healthy meal. The editing unit uses the identification processing unit 290 of the data processing unit 12 to analyze the images captured by the camera 42 of the headset terminal 314 and edit them into a short video. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0181] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0182] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0183] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0184] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0185] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0187] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0188] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0189] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0190] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0191] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0192] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0193] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0194] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0195] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0196] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0197] Each of the multiple elements described above, including the recording unit, organization unit, guidance unit, meal recording unit, suggestion unit, and editing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the recording unit uses the camera 42 and recorder of the robot 414 to record the faces of people met and the content of conversations. The organization unit uses the identification processing unit 290 of the data processing unit 12 to organize the recorded information and register it in a calendar. The guidance unit uses the GPS function of the robot 414 to determine the current location and guide the user along the optimal route. The meal recording unit uses the camera 42 of the robot 414 to capture images of meals, and the identification processing unit 290 of the data processing unit 12 identifies the ingredients and calculates the calories. The suggestion unit uses the identification processing unit 290 of the data processing unit 12 to suggest the next healthy meal. The editing unit analyzes the images captured by the camera 42 of the robot 414 using the identification processing unit 290 of the data processing unit 12 and edits them into a short video. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0198] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0199] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0200] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0201] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0202] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0203] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0204] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0205] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0206] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0207] 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.

[0208] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0209] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0210] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0211] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0212] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0213] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0214] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0215] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0216] (Note 1) A recording department that records the faces, names, and conversations of the people met, A sorting unit organizes the information recorded by the aforementioned recording unit and registers appointments in a calendar, An information desk that guides you to your destination based on the time listed on the calendar, A food diary section where you record the images, ingredients, and calories of the meals you eat, A suggestion unit that proposes the next healthy meal based on the information recorded by the aforementioned meal record unit, It includes an editorial department that analyzes the footage captured by the camera and edits memorable moments from the day into short videos. A system characterized by the following features. (Note 2) The aforementioned recording unit is Identifying the faces of people you meet using facial recognition technology The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned guide section is It uses GPS to determine your current location and calculates the optimal route based on your calendar appointments. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned meal record section is, A camera captures a video of the meal, and AI identifies the ingredients and calculates the calories. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the information recorded by the aforementioned meal record unit, the following healthy meal plan is proposed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned editorial department, The system analyzes footage captured by the camera, extracts important scenes, and edits them into short videos. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recording unit is It estimates the user's emotions and determines the priority of information to record based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recording unit is During recording, the conversation is translated in real time and recorded in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recording unit is During recording, analyze the tone and pace of the conversation and highlight important information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recording unit is It estimates the user's emotions and adjusts the level of detail in the recorded information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recording unit is During recording, background noise from the conversation is filtered out to record clear audio. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recording unit is During recording, the context of the conversation is analyzed, and relevant information is automatically tagged. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned editing unit, It estimates the user's emotions and determines the priority of information to organize based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned editing unit, When organizing, analyze the relationships between pieces of information and group related information together. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned editing unit, When organizing, adjust the display order based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned editing unit, It estimates the user's emotions and adjusts the level of detail of the information organized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned editing unit, During organization, the system automatically categorizes information and displays it visually. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned editing unit, When organizing information, consider the chronological order of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide section is It estimates the user's emotions and adjusts the guidance method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is During navigation, the system analyzes traffic conditions in real time and recalculates the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is During guidance, the pace of the guidance is adjusted based on the user's walking speed and mode of transportation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide section is The system estimates the user's emotions and adjusts the level of detail in the guidance based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide section is When providing information, we will offer information on nearby tourist attractions and shops. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is When providing directions, the system will refer to the user's past travel history to suggest preferred routes. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned meal record section is, It estimates the user's emotions and determines the priority of meal information to record based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned meal record section is, During recording, the nutritional balance of meals is analyzed and health status is assessed. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned meal record section is, When recording data, consider the timing of meal intake and evaluate the impact of meals. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned meal record section is, It estimates the user's emotions and adjusts the level of detail in the recorded meal information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned meal record section is, During recording, the freshness of the ingredients is evaluated by analyzing photos of the meal. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned meal record section is, When recording, the meal recipe is automatically generated and saved. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggested meal content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making suggestions, the system refers to the user's past meal history to suggest meals that match their preferences. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, we will take into account the season and weather to suggest appropriate meals. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and adjusts the level of detail in the suggested meals based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, we will consider the availability of ingredients and suggest a realistic meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making suggestions, we take the user's allergy information into consideration and propose safe meals. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned editorial department, It estimates the user's emotions and adjusts the video content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned editorial department, During editing, apply a filter that automatically improves the quality of the video. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned editorial department, Optimize audio and video synchronization during editing. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned editorial department, It estimates the user's emotions and adjusts the length of the edited video based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned editorial department, Add text or subtitles to the video during editing. The system described in Appendix 1, characterized by the features described herein. (Appendix 42) The editing unit automatically selects and adds music based on the theme of the video during editing The system according to Appendix 1, characterized in that. (Appendix 43) Customize the shape of the terminal based on the user's emotions to provide a comfortable wearing feeling The system according to Appendix 1, characterized in that. (Appendix 44) Adjust the shape of the terminal to make it adaptable to different usage scenarios The system according to Appendix 1, characterized in that. (Appendix 45) Change the shape of the terminal​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

Claims

1. A recording department that records the faces, names, and conversations of the people met, A sorting unit organizes the information recorded by the aforementioned recording unit and registers appointments in a calendar, An information desk that guides you to your destination based on the time listed on the calendar, A food diary section where you record the images, ingredients, and calories of the meals you eat, A suggestion unit that proposes the next healthy meal based on the information recorded by the aforementioned meal record unit, It includes an editorial department that analyzes the footage captured by the camera and edits memorable moments from the day into short videos. A system characterized by the following features.

2. The aforementioned recording unit is Identifying the faces of people you meet using facial recognition technology The system according to feature 1.

3. The aforementioned guide section is It uses GPS to determine your current location and calculates the optimal route based on your calendar appointments. The system according to feature 1.

4. The aforementioned meal record section is, A camera captures a video of the meal, and AI identifies the ingredients and calculates the calories. The system according to feature 1.

5. The aforementioned proposal section is, Based on the information recorded by the aforementioned meal record unit, the following healthy meal plan is proposed. The system according to feature 1.

6. The aforementioned editorial department, The system analyzes footage captured by the camera, extracts important scenes, and edits them into short videos. The system according to feature 1.

7. The aforementioned recording unit is It estimates the user's emotions and determines the priority of information to record based on the estimated user emotions. The system according to feature 1.

8. The aforementioned recording unit is During recording, the conversation is translated in real time and recorded in multiple languages. The system according to feature 1.

Images