system

Abstract

The system according to this embodiment aims to efficiently record observation results and capture visual changes. [Solution] The system according to the embodiment comprises an upload unit, a generation unit, a recording unit, and a management unit. The upload unit uploads images taken with a smartphone in succession at regular intervals via the cloud. The generation unit analyzes the images uploaded by the upload unit and automatically generates a time-lapse video. The recording unit documents the changes in the photographs based on the time-lapse video generated by the generation unit. The management unit manages the documents documented by the recording unit in conjunction with cloud storage.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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 it is time-consuming to summarize the observation results and it is impossible to capture all visual changes.

[0005] The system according to the embodiment aims to efficiently record the observation results and capture visual changes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an upload unit, a generation unit, a recording unit, and a management unit. The upload unit uploads images taken with a smartphone in succession at regular intervals via the cloud. The generation unit analyzes the images uploaded by the upload unit and automatically generates a time-lapse video. The recording unit documents the changes in the photographs based on the time-lapse video generated by the generation unit. The management unit manages the documents documented by the recording unit in conjunction with cloud storage. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently record observation results and capture visual changes. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. 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 observation recording system according to an embodiment of the present invention is a system for efficiently recording observations. This observation recording system addresses the problems of conventional observation recording methods, where compiling observation results is time-consuming, visual changes are not fully captured, and recordings tend to be monotonous. To solve these problems, it includes the following components: First, it has photo analysis and video generation functions. Images taken consecutively with a smartphone at regular intervals are uploaded via the cloud, and an AI agent automatically generates a time-lapse video. This makes it easy to capture visual changes. Next, it has a function for recording changes in text. The AI ​​transcribes changes in photographs into text and records the process in natural language. This allows visual changes to be expressed in text, aiding understanding. Furthermore, it has cloud integration and data management functions. It can integrate with cloud storage, making data management, backup, and sharing easy. This prevents data loss and confusion. It also has integration functions with other tools. It can be integrated with educational apps and diary apps to centralize observation records. This prevents omissions in recordings and improves work efficiency. This system reduces effort and enables efficient observation recording. Furthermore, it allows for the creation of easily viewable and reviewable records, and its usability can be improved by linking it with other digital tools. As a result, the observation recording system can efficiently record observations, capture visual changes, and centralize records.

[0029] The observation recording system according to the embodiment comprises an upload unit, a generation unit, a recording unit, and a management unit. The upload unit uploads images taken with a smartphone in succession at regular intervals via the cloud. The upload unit uploads images taken with a smartphone to cloud storage, for example. The upload unit can, for example, set a schedule to automatically upload images to the cloud. The upload unit also allows users to manually upload images to the cloud, for example. The generation unit analyzes the images uploaded by the upload unit and automatically generates a time-lapse video. The generation unit analyzes images using, for example, image recognition technology and generates a time-lapse video. The generation unit generates a time-lapse video that displays images taken at regular intervals in succession, for example. The generation unit generates a time-lapse video with effects that emphasize changes in the images, for example. The recording unit transcribes the changes in the photographs into text based on the time-lapse video generated by the generation unit. The recording unit transcribes the content of the time-lapse video into text using, for example, natural language generation technology. The recording unit generates text that describes the content of each frame of the time-lapse video in detail, for example. The recording unit generates, for example, a concise text summarizing the key points of a time-lapse video. The management unit manages the changes documented by the recording unit in conjunction with cloud storage. The management unit automatically backs up the data stored in cloud storage. The management unit shares the data stored in cloud storage with other users. The management unit provides a tagging function to make the data stored in cloud storage easier to search. As a result, the observation recording system according to this embodiment can efficiently record observations, capture visual changes, and centralize the recording.

[0030] The upload unit uploads images taken with a smartphone in succession over a set period of time via the cloud. Specifically, it has a function to automatically upload images taken with a smartphone camera to cloud storage. The upload unit can upload images to the cloud at specific times, for example, daily, weekly, or monthly, according to a schedule set by the user. This eliminates the need for users to manually upload images. Users can also manually upload images, allowing them to instantly save specific events or important moments to the cloud. The upload unit also has a function to check the internet connection status when uploading images and to retry if the connection is unstable. Furthermore, the upload unit automatically adjusts the image resolution and file size, enabling efficient use of cloud storage capacity. In this way, the upload unit can reliably save the user's observation records to the cloud and provide the data necessary for subsequent processing.

[0031] The generation unit analyzes images uploaded by the upload unit and automatically generates time-lapse videos. Specifically, it uses image recognition technology to analyze uploaded images and stitches together consecutive images to create a time-lapse video. In analyzing images, the generation unit considers the date, time, and location information of each image and arranges the images in an appropriate order. It also automatically adjusts the brightness and contrast of the images to improve the quality of the time-lapse video. The generation unit can generate time-lapse videos based on a period specified by the user, such as one week, one month, or one year. Furthermore, the generation unit can add effects to emphasize changes in the images. For example, it can add effects that visually emphasize changes over time, such as the growth of plants or the construction process of a building. The generation unit saves the generated time-lapse videos to cloud storage, allowing users to access them at any time. This allows the generation unit to visually capture the user's observation records and provide them in an easily shareable format.

[0032] The recording unit transcribes photographic changes based on the time-lapse video generated by the generation unit. Specifically, it analyzes the content of the time-lapse video using natural language generation technology and generates text that describes the content of each frame in detail. The recording unit extracts important changes and events in each frame of the time-lapse video and transcribes them in chronological order. For example, when recording the growth process of a plant, it describes important changes such as sprouting, leaf expansion, and flowering in detail. The recording unit can also generate a concise summary of the key points of the time-lapse video. This allows users to quickly grasp the content of the time-lapse video. The recording unit saves the generated text to cloud storage, making it accessible to users at any time. Furthermore, the recording unit has a function to tag the generated text, allowing users to easily search for specific events or changes. In this way, the recording unit can transcribe users' observation records in detail and efficiently, and provide them in a format that is easy to refer to later.

[0033] The management unit manages the changes documented by the recording unit in conjunction with cloud storage. Specifically, it has a function to automatically back up data stored in cloud storage to prevent data loss or corruption. The management unit performs regular backups, providing an environment in which users can store data with peace of mind. The management unit also has a function to share data stored in cloud storage with other users. Users can easily share data with other users by selecting specific data and generating a sharing link. Furthermore, the management unit provides a tagging function to make data stored in cloud storage easier to search. By tagging data, users can easily search for specific events or changes. The management unit is designed to allow intuitive data management, searching, and sharing through its user interface. This enables the management unit to efficiently manage users' observation records and quickly provide necessary information. In addition, the management unit has a function to set data access permissions, allowing users to restrict who is allowed to view or edit their data. This enables the management unit to safely and efficiently manage user data and achieve centralized observation records.

[0034] The management department can work with cloud storage to back up and share data. For example, the management department can periodically back up data stored in cloud storage. For example, the management department can generate links to share data stored in cloud storage with other users. For example, the management department can encrypt and securely share data stored in cloud storage. This makes data backup and sharing easier. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input a backup schedule for data stored in cloud storage into a generating AI and have the generating AI perform the backups.

[0035] The management unit can integrate with educational apps and diary apps to centralize observation records. For example, the management unit can automatically synchronize observation records in conjunction with educational apps. For example, the management unit can centrally manage observation records in conjunction with diary apps. For example, the management unit can share observation records in conjunction with educational apps and diary apps. This enables the centralization of observation records. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the integration settings with educational apps and diary apps into a generating AI and have the generating AI execute the integration.

[0036] The generation unit can analyze images taken with a smartphone in succession at regular intervals and automatically generate a time-lapse video. For example, the generation unit analyzes images taken with a smartphone and generates a time-lapse video. For example, the generation unit generates a time-lapse video that displays images taken at regular intervals in succession. For example, the generation unit generates a time-lapse video with effects added to emphasize changes in the images. This enables the automatic generation of time-lapse videos. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input images taken with a smartphone into a generation AI and have the generation AI perform the generation of a time-lapse video.

[0037] The recording unit can transcribe the changes in the photographs into text based on the generated time-lapse video. The recording unit can, for example, use natural language generation technology to transcribe the content of the time-lapse video into text. The recording unit can, for example, generate text that describes the content of each frame of the time-lapse video in detail. The recording unit can, for example, generate text that concisely summarizes the key points of the time-lapse video. This deepens understanding by transcribing the changes in the photographs into text. Some or all of the above processing in the recording unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recording unit can input the generated time-lapse video into a generation AI and have the generation AI transcribe the changes in the photographs into text.

[0038] The upload unit can analyze the user's past upload history and select the optimal upload method. For example, the upload unit can analyze the time periods when the user frequently uploaded in the past and recommend uploading during those times. For example, the upload unit can prioritize suggesting upload methods the user has used in the past (Wi-Fi, mobile data, etc.). For example, the upload unit can predict uploads at specific events or locations based on the user's past upload history and suggest the optimal method. This allows the system to select the optimal upload method based on past history. Some or all of the above processing in the upload unit may be performed using, for example, a generative AI, or without a generative AI. For example, the upload unit can input the user's past upload history data into a generative AI and have the generative AI select the optimal upload method.

[0039] The upload unit can filter images based on the user's current projects and areas of interest when uploading them. For example, the upload unit can filter to upload only images related to the project the user is currently working on. For example, the upload unit can prioritize uploading highly relevant images based on the user's areas of interest. For example, if the user is interested in a particular theme, the upload unit can automatically select and upload images related to that theme. This allows users to upload images that match their interests. Some or all of the above processing in the upload unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the upload unit can input data on the user's current projects and areas of interest into a generative AI and have the generative AI perform the filtering.

[0040] The upload unit can prioritize uploading images that are highly relevant to the user's geographical location, taking this information into account when uploading images. For example, if the user is in a specific location, the upload unit will prioritize uploading images related to that location. For example, if the user is traveling, the upload unit will prioritize uploading images of scenery or tourist attractions at the travel destination. For example, if the user is attending an event, the upload unit will prioritize uploading images related to that event. This allows for the uploading of highly relevant images based on geographical location information. Some or all of the above processing in the upload unit may be performed using, for example, a generative AI, or without a generative AI. For example, the upload unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant images.

[0041] The upload unit can analyze a user's social media activity when uploading images and upload relevant images. For example, the upload unit can analyze images shared by the user on social media and prioritize uploading relevant images. For example, the upload unit can prioritize uploading images that are likely to be of interest to the user's social media followers. For example, if the user uses a specific hashtag on social media, the upload unit can prioritize uploading images related to that hashtag. This allows for the uploading of relevant images based on social media activity. Some or all of the above processing in the upload unit may be performed using, for example, a generative AI, or without a generative AI. For example, the upload unit can input the user's social media activity data into a generative AI and have the generative AI select relevant images.

[0042] The generation unit can adjust the level of detail in the time-lapse video based on the importance of the images. For example, the generation unit can generate a time-lapse video that displays images of important events in detail. For example, the generation unit can generate a time-lapse video that displays everyday images in a simplified manner. For example, the generation unit can generate a time-lapse video that highlights and displays high-importance images in detail. This makes it possible to generate time-lapse videos according to the importance of the images. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in the generation.

[0043] The generation unit can apply different generation algorithms depending on the image category when generating time-lapse videos. For example, the generation unit can apply a generation algorithm that emphasizes natural changes to landscape images. For example, the generation unit can apply a generation algorithm that emphasizes changes in facial expressions to portrait images. For example, the generation unit can apply a generation algorithm that emphasizes the progress of an event to event images. This allows the generation algorithm to be applied according to the image category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0044] The generation unit can determine the generation priority based on the image capture date when generating a time-lapse video. For example, the generation unit may prioritize using recently captured images to generate a time-lapse video. For example, the generation unit may prioritize using images captured during a specific event period to generate a time-lapse video. For example, the generation unit may prioritize using images captured in a specific season to emphasize seasonal changes to generate a time-lapse video. This makes it possible to generate time-lapse videos based on the image capture date. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit may input image capture date data into the generation AI and have the generation AI determine the generation priority.

[0045] The generation unit can adjust the generation order based on the relevance of images when generating time-lapse videos. For example, the generation unit can generate a time-lapse video that displays highly relevant images in sequence. For example, the generation unit can generate a time-lapse video that omits less relevant images. For example, the generation unit can generate a time-lapse video with a narrative based on the relevance of images. This makes it possible to generate time-lapse videos based on the relevance of images. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image relevance data into a generation AI and have the generation AI adjust the generation order.

[0046] The recording unit can adjust the level of detail in the text based on the importance of the image when documenting changes. For example, the recording unit generates a detailed description for an image of an important event. For example, the recording unit generates a concise description for an everyday image. For example, the recording unit generates a detailed description for an image of high importance. This makes it possible to document images according to their importance. Some or all of the above processing in the recording unit may be performed using a generation AI, for example, or without a generation AI. For example, the recording unit can input image importance data into a generation AI and have the generation AI adjust the level of detail in the text.

[0047] The recording unit can apply different text creation algorithms depending on the image category when creating texts about changes. For example, the recording unit can generate texts that emphasize natural changes for landscape images, texts that emphasize changes in facial expressions for portrait images, and texts that emphasize the progress of events for event images. This allows the recording unit to apply text creation algorithms appropriate to the image category. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input image category data into a generative AI and have the generative AI perform the application of the text creation algorithm.

[0048] The recording unit can determine the priority of texts based on when the images were taken when documenting changes. For example, the recording unit may prioritize generating texts for recently taken images. For example, the recording unit may prioritize generating texts for images taken during a specific event period. For example, the recording unit may prioritize generating texts for images taken during a specific season to highlight seasonal changes. This allows for the determination of text priority based on when the images were taken. Some or all of the above processing in the recording unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recording unit may input image capture date data into a generation AI and have the generation AI perform the determination of text priority.

[0049] The recording unit can adjust the order of texts based on the relevance of images when documenting changes. For example, the recording unit can generate texts sequentially for highly relevant images. For example, the recording unit can generate concise texts for unrelated images. For example, the recording unit can generate narrative-style texts based on the relevance of images. This allows for adjustment of the order of texts based on the relevance of images. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input image relevance data into a generative AI and have the generative AI perform the adjustment of the order of texts.

[0050] The management department can select the optimal management method by referring to past data management history when managing data. For example, the management department may prioritize suggesting data management methods previously used by the user. For example, the management department may predict and suggest a management method to be used during a specific time period based on the user's past data management history. For example, the management department may analyze the user's past data management history and suggest the most efficient management method. This allows for the selection of the optimal management method based on past data management history. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department may input past data management history data into a generative AI and have the generative AI select the optimal management method.

[0051] The management unit can apply different management algorithms depending on the data category during data management. For example, the management unit can apply a management algorithm specifically for images to image data, a management algorithm specifically for text data to text data, and a management algorithm specifically for video data to video data. This allows for the application of management algorithms appropriate to the data category. Some or all of the above-described processes in the management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the management unit can input data category data into a generative AI and have the generative AI execute the application of the management algorithm.

[0052] The management unit can determine management priorities based on when the data was captured during data management. For example, the management unit may prioritize the management of recently captured data. For example, the management unit may prioritize the management of data captured during a specific event period. For example, the management unit may prioritize the management of data captured during a specific season to highlight seasonal changes. This allows for the determination of management priorities based on when the data was captured. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input the data capture date data into a generative AI and have the generative AI perform the determination of management priorities.

[0053] The management unit can adjust the order of data management based on the relationships between data. For example, the management unit can manage highly relevant data consecutively. For example, the management unit can manage less relevant data in a simplified manner. For example, the management unit can perform narrative-based management based on the relationships between data. This allows for adjustment of the order of management based on the relationships between data. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input data relationship data into a generative AI and have the generative AI perform the adjustment of the order of management.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The observation recording system can analyze observation data and provide users with suggestions for improving their observation records. For example, based on the data analysis results, it can suggest increasing the frequency of observations, changing the observation recording method, or enriching the content of the observation records. This can improve the quality of the observation records.

[0056] The observation recording system can analyze observation data and suggest ways to improve the efficiency of observation recording for the user. For example, it can suggest automating observation recording based on the data analysis results. It can suggest simplifying observation recording procedures based on the data analysis results. It can suggest changing observation recording tools based on the data analysis results. This can improve the efficiency of observation recording.

[0057] The observation record system can analyze observation record data and suggest improvements to the user's observation record security. For example, based on the data analysis results, it can suggest improvements to the observation record backup method. Based on the data analysis results, it can suggest changes to the observation record encryption method. Based on the data analysis results, it can suggest a review of the observation record access permissions. This can improve the security of the observation record.

[0058] The observation record system can analyze observation record data and suggest improvements to the user's experience with the observation record system. For example, it can suggest improvements to the observation record interface based on the data analysis results. It can suggest changes to how the observation record is operated based on the data analysis results. It can suggest revisions to how the observation record is displayed based on the data analysis results. This can improve the overall usability of the observation record system.

[0059] The observation record system can analyze observation record data and suggest customizations to the user. For example, it can suggest changing the observation record template based on the data analysis results. It can suggest customizing the observation record format based on the data analysis results. It can suggest reviewing the observation record layout based on the data analysis results. This enables customization of the observation record.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The upload unit uploads images taken with a smartphone in succession at regular intervals via the cloud. For example, it uploads images taken with a smartphone to cloud storage. The upload unit can be scheduled to automatically upload images to the cloud, and users can also manually upload images to the cloud. Step 2: The generation unit analyzes the images uploaded by the upload unit and automatically generates a time-lapse video. For example, it analyzes the images using image recognition technology and generates a time-lapse video that displays images taken at regular intervals in sequence. It can also generate a time-lapse video with effects that emphasize changes in the images. Step 3: The recording unit translates the changes in the photographs into text based on the time-lapse video generated by the generation unit. For example, it uses natural language generation technology to translate the content of the time-lapse video into text, generating detailed descriptions of each frame of the time-lapse video, as well as concise summaries of the key points of the time-lapse video. Step 4: The management department manages the changes documented by the records department in conjunction with cloud storage. For example, it automatically backs up data stored in cloud storage and shares it with other users. It also provides a tagging function to make data stored in cloud storage easier to search.

[0062] (Example of form 2) The observation recording system according to an embodiment of the present invention is a system for efficiently recording observations. This observation recording system addresses the problems of conventional observation recording methods, where compiling observation results is time-consuming, visual changes are not fully captured, and recordings tend to be monotonous. To solve these problems, it includes the following components: First, it has photo analysis and video generation functions. Images taken consecutively with a smartphone at regular intervals are uploaded via the cloud, and an AI agent automatically generates a time-lapse video. This makes it easy to capture visual changes. Next, it has a function for recording changes in text. The AI ​​transcribes changes in photographs into text and records the process in natural language. This allows visual changes to be expressed in text, aiding understanding. Furthermore, it has cloud integration and data management functions. It can integrate with cloud storage, making data management, backup, and sharing easy. This prevents data loss and confusion. It also has integration functions with other tools. It can be integrated with educational apps and diary apps to centralize observation records. This prevents omissions in recordings and improves work efficiency. This system reduces effort and enables efficient observation recording. Furthermore, it allows for the creation of easily viewable and reviewable records, and its usability can be improved by linking it with other digital tools. As a result, the observation recording system can efficiently record observations, capture visual changes, and centralize records.

[0063] The observation recording system according to the embodiment comprises an upload unit, a generation unit, a recording unit, and a management unit. The upload unit uploads images taken with a smartphone in succession at regular intervals via the cloud. The upload unit uploads images taken with a smartphone to cloud storage, for example. The upload unit can, for example, set a schedule to automatically upload images to the cloud. The upload unit also allows users to manually upload images to the cloud, for example. The generation unit analyzes the images uploaded by the upload unit and automatically generates a time-lapse video. The generation unit analyzes images using, for example, image recognition technology and generates a time-lapse video. The generation unit generates a time-lapse video that displays images taken at regular intervals in succession, for example. The generation unit generates a time-lapse video with effects that emphasize changes in the images, for example. The recording unit transcribes the changes in the photographs into text based on the time-lapse video generated by the generation unit. The recording unit transcribes the content of the time-lapse video into text using, for example, natural language generation technology. The recording unit generates text that describes the content of each frame of the time-lapse video in detail, for example. The recording unit generates, for example, a concise text summarizing the key points of a time-lapse video. The management unit manages the changes documented by the recording unit in conjunction with cloud storage. The management unit automatically backs up the data stored in cloud storage. The management unit shares the data stored in cloud storage with other users. The management unit provides a tagging function to make the data stored in cloud storage easier to search. As a result, the observation recording system according to this embodiment can efficiently record observations, capture visual changes, and centralize the recording.

[0064] The upload unit uploads images taken with a smartphone in succession over a set period of time via the cloud. Specifically, it has a function to automatically upload images taken with a smartphone camera to cloud storage. The upload unit can upload images to the cloud at specific times, for example, daily, weekly, or monthly, according to a schedule set by the user. This eliminates the need for users to manually upload images. Users can also manually upload images, allowing them to instantly save specific events or important moments to the cloud. The upload unit also has a function to check the internet connection status when uploading images and to retry if the connection is unstable. Furthermore, the upload unit automatically adjusts the image resolution and file size, enabling efficient use of cloud storage capacity. In this way, the upload unit can reliably save the user's observation records to the cloud and provide the data necessary for subsequent processing.

[0065] The generation unit analyzes images uploaded by the upload unit and automatically generates time-lapse videos. Specifically, it uses image recognition technology to analyze uploaded images and stitches together consecutive images to create a time-lapse video. In analyzing images, the generation unit considers the date, time, and location information of each image and arranges the images in an appropriate order. It also automatically adjusts the brightness and contrast of the images to improve the quality of the time-lapse video. The generation unit can generate time-lapse videos based on a period specified by the user, such as one week, one month, or one year. Furthermore, the generation unit can add effects to emphasize changes in the images. For example, it can add effects that visually emphasize changes over time, such as the growth of plants or the construction process of a building. The generation unit saves the generated time-lapse videos to cloud storage, allowing users to access them at any time. This allows the generation unit to visually capture the user's observation records and provide them in an easily shareable format.

[0066] The recording unit transcribes photographic changes based on the time-lapse video generated by the generation unit. Specifically, it analyzes the content of the time-lapse video using natural language generation technology and generates text that describes the content of each frame in detail. The recording unit extracts important changes and events in each frame of the time-lapse video and transcribes them in chronological order. For example, when recording the growth process of a plant, it describes important changes such as sprouting, leaf expansion, and flowering in detail. The recording unit can also generate a concise summary of the key points of the time-lapse video. This allows users to quickly grasp the content of the time-lapse video. The recording unit saves the generated text to cloud storage, making it accessible to users at any time. Furthermore, the recording unit has a function to tag the generated text, allowing users to easily search for specific events or changes. In this way, the recording unit can transcribe users' observation records in detail and efficiently, and provide them in a format that is easy to refer to later.

[0067] The management unit manages the changes documented by the recording unit in conjunction with cloud storage. Specifically, it has a function to automatically back up data stored in cloud storage to prevent data loss or corruption. The management unit performs regular backups, providing an environment in which users can store data with peace of mind. The management unit also has a function to share data stored in cloud storage with other users. Users can easily share data with other users by selecting specific data and generating a sharing link. Furthermore, the management unit provides a tagging function to make data stored in cloud storage easier to search. By tagging data, users can easily search for specific events or changes. The management unit is designed to allow intuitive data management, searching, and sharing through its user interface. This enables the management unit to efficiently manage users' observation records and quickly provide necessary information. In addition, the management unit has a function to set data access permissions, allowing users to restrict who is allowed to view or edit their data. This enables the management unit to safely and efficiently manage user data and achieve centralized observation records.

[0068] The management department can work with cloud storage to back up and share data. For example, the management department can periodically back up data stored in cloud storage. For example, the management department can generate links to share data stored in cloud storage with other users. For example, the management department can encrypt and securely share data stored in cloud storage. This makes data backup and sharing easier. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input a backup schedule for data stored in cloud storage into a generating AI and have the generating AI perform the backups.

[0069] The management unit can integrate with educational apps and diary apps to centralize observation records. For example, the management unit can automatically synchronize observation records in conjunction with educational apps. For example, the management unit can centrally manage observation records in conjunction with diary apps. For example, the management unit can share observation records in conjunction with educational apps and diary apps. This enables the centralization of observation records. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the integration settings with educational apps and diary apps into a generating AI and have the generating AI execute the integration.

[0070] The generation unit can analyze images taken with a smartphone in succession at regular intervals and automatically generate a time-lapse video. For example, the generation unit analyzes images taken with a smartphone and generates a time-lapse video. For example, the generation unit generates a time-lapse video that displays images taken at regular intervals in succession. For example, the generation unit generates a time-lapse video with effects added to emphasize changes in the images. This enables the automatic generation of time-lapse videos. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input images taken with a smartphone into a generation AI and have the generation AI perform the generation of a time-lapse video.

[0071] The recording unit can transcribe the changes in the photographs into text based on the generated time-lapse video. The recording unit can, for example, use natural language generation technology to transcribe the content of the time-lapse video into text. The recording unit can, for example, generate text that describes the content of each frame of the time-lapse video in detail. The recording unit can, for example, generate text that concisely summarizes the key points of the time-lapse video. This deepens understanding by transcribing the changes in the photographs into text. Some or all of the above processing in the recording unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recording unit can input the generated time-lapse video into a generation AI and have the generation AI transcribe the changes in the photographs into text.

[0072] The upload unit can estimate the user's emotions and adjust the image upload timing based on the estimated emotions. For example, if the user is stressed, the upload unit can delay the upload timing so that the user can upload when relaxed. For example, if the user is excited, the upload unit can upload immediately to capture the moment when emotions are heightened. For example, if the user is tired, the upload unit can adjust the upload timing so that the user can upload after resting. This allows images to be uploaded at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the upload unit may be performed using AI, for example, or not using AI. For example, the upload unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustment of the upload timing.

[0073] The upload unit can analyze the user's past upload history and select the optimal upload method. For example, the upload unit can analyze the time periods when the user frequently uploaded in the past and recommend uploading during those times. For example, the upload unit can prioritize suggesting upload methods the user has used in the past (Wi-Fi, mobile data, etc.). For example, the upload unit can predict uploads at specific events or locations based on the user's past upload history and suggest the optimal method. This allows the system to select the optimal upload method based on past history. Some or all of the above processing in the upload unit may be performed using, for example, a generative AI, or without a generative AI. For example, the upload unit can input the user's past upload history data into a generative AI and have the generative AI select the optimal upload method.

[0074] The upload unit can filter images based on the user's current projects and areas of interest when uploading them. For example, the upload unit can filter to upload only images related to the project the user is currently working on. For example, the upload unit can prioritize uploading highly relevant images based on the user's areas of interest. For example, if the user is interested in a particular theme, the upload unit can automatically select and upload images related to that theme. This allows users to upload images that match their interests. Some or all of the above processing in the upload unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the upload unit can input data on the user's current projects and areas of interest into a generative AI and have the generative AI perform the filtering.

[0075] The upload unit can estimate the user's emotions and determine the priority of images to upload based on the estimated emotions. For example, if the user is excited, the upload unit will prioritize uploading images that capture moments of heightened emotion. For example, if the user is relaxed, the upload unit will prioritize uploading images with a calm atmosphere. For example, if the user is stressed, the upload unit will prioritize uploading images that reduce stress. This allows for the determination of image priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the upload unit may be performed using AI, for example, or not using AI. For example, the upload unit can input user emotion data into a generative AI and have the generative AI perform the determination of image priorities based on emotions.

[0076] The upload unit can prioritize uploading images that are highly relevant to the user's geographical location, taking this information into account when uploading images. For example, if the user is in a specific location, the upload unit will prioritize uploading images related to that location. For example, if the user is traveling, the upload unit will prioritize uploading images of scenery or tourist attractions at the travel destination. For example, if the user is attending an event, the upload unit will prioritize uploading images related to that event. This allows for the uploading of highly relevant images based on geographical location information. Some or all of the above processing in the upload unit may be performed using, for example, a generative AI, or without a generative AI. For example, the upload unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant images.

[0077] The upload unit can analyze a user's social media activity when uploading images and upload relevant images. For example, the upload unit can analyze images shared by the user on social media and prioritize uploading relevant images. For example, the upload unit can prioritize uploading images that are likely to be of interest to the user's social media followers. For example, if the user uses a specific hashtag on social media, the upload unit can prioritize uploading images related to that hashtag. This allows for the uploading of relevant images based on social media activity. Some or all of the above processing in the upload unit may be performed using, for example, a generative AI, or without a generative AI. For example, the upload unit can input the user's social media activity data into a generative AI and have the generative AI select relevant images.

[0078] The generation unit can estimate the user's emotions and adjust the time-lapse video generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate a time-lapse video that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit will generate a time-lapse video that emphasizes the shortest route. For example, if the user is excited, the generation unit will generate a time-lapse video with visually stimulating effects. This allows the generation method of time-lapse video generation to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the time-lapse video generation method based on emotions.

[0079] The generation unit can adjust the level of detail in the time-lapse video based on the importance of the images. For example, the generation unit can generate a time-lapse video that displays images of important events in detail. For example, the generation unit can generate a time-lapse video that displays everyday images in a simplified manner. For example, the generation unit can generate a time-lapse video that highlights and displays high-importance images in detail. This makes it possible to generate time-lapse videos according to the importance of the images. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in the generation.

[0080] The generation unit can apply different generation algorithms depending on the image category when generating time-lapse videos. For example, the generation unit can apply a generation algorithm that emphasizes natural changes to landscape images. For example, the generation unit can apply a generation algorithm that emphasizes changes in facial expressions to portrait images. For example, the generation unit can apply a generation algorithm that emphasizes the progress of an event to event images. This allows the generation algorithm to be applied according to the image category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0081] The generation unit can estimate the user's emotions and adjust the length of the time-lapse video based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise time-lapse video. If the user is relaxed, the generation unit will generate a longer time-lapse video with detailed explanations. If the user is excited, the generation unit will generate a time-lapse video with visually stimulating effects. This allows the length of the time-lapse video to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the time-lapse video based on the emotions.

[0082] The generation unit can determine the generation priority based on the image capture date when generating a time-lapse video. For example, the generation unit may prioritize using recently captured images to generate a time-lapse video. For example, the generation unit may prioritize using images captured during a specific event period to generate a time-lapse video. For example, the generation unit may prioritize using images captured in a specific season to emphasize seasonal changes to generate a time-lapse video. This makes it possible to generate time-lapse videos based on the image capture date. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit may input image capture date data into the generation AI and have the generation AI determine the generation priority.

[0083] The generation unit can adjust the generation order based on the relevance of images when generating time-lapse videos. For example, the generation unit can generate a time-lapse video that displays highly relevant images in sequence. For example, the generation unit can generate a time-lapse video that omits less relevant images. For example, the generation unit can generate a time-lapse video with a narrative based on the relevance of images. This makes it possible to generate time-lapse videos based on the relevance of images. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image relevance data into a generation AI and have the generation AI adjust the generation order.

[0084] The recording unit can estimate the user's emotions and adjust the way changes are described in text based on the estimated emotions. For example, if the user is relaxed, the recording unit generates text with detailed descriptions. For example, if the user is in a hurry, the recording unit generates concise and to-the-point text. For example, if the user is excited, the recording unit generates text that emphasizes emotions. This allows the writing method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the writing method based on emotions.

[0085] The recording unit can adjust the level of detail in the text based on the importance of the image when documenting changes. For example, the recording unit generates a detailed description for an image of an important event. For example, the recording unit generates a concise description for an everyday image. For example, the recording unit generates a detailed description for an image of high importance. This makes it possible to document images according to their importance. Some or all of the above processing in the recording unit may be performed using a generation AI, for example, or without a generation AI. For example, the recording unit can input image importance data into a generation AI and have the generation AI adjust the level of detail in the text.

[0086] The recording unit can apply different text creation algorithms depending on the image category when creating texts about changes. For example, the recording unit can generate texts that emphasize natural changes for landscape images, texts that emphasize changes in facial expressions for portrait images, and texts that emphasize the progress of events for event images. This allows the recording unit to apply text creation algorithms appropriate to the image category. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input image category data into a generative AI and have the generative AI perform the application of the text creation algorithm.

[0087] The recording unit can estimate the user's emotions and adjust the length of the text based on the estimated emotions. For example, if the user is in a hurry, the recording unit generates short, concise text. If the user is relaxed, the recording unit generates longer text with detailed explanations. If the user is excited, the recording unit generates text that emphasizes emotions. This allows the length of the text to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI perform the emotion-based adjustment of text length.

[0088] The recording unit can determine the priority of texts based on when the images were taken when documenting changes. For example, the recording unit may prioritize generating texts for recently taken images. For example, the recording unit may prioritize generating texts for images taken during a specific event period. For example, the recording unit may prioritize generating texts for images taken during a specific season to highlight seasonal changes. This allows for the determination of text priority based on when the images were taken. Some or all of the above processing in the recording unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recording unit may input image capture date data into a generation AI and have the generation AI perform the determination of text priority.

[0089] The recording unit can adjust the order of texts based on the relevance of images when documenting changes. For example, the recording unit can generate texts sequentially for highly relevant images. For example, the recording unit can generate concise texts for unrelated images. For example, the recording unit can generate narrative-style texts based on the relevance of images. This allows for adjustment of the order of texts based on the relevance of images. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input image relevance data into a generative AI and have the generative AI perform the adjustment of the order of texts.

[0090] The management unit can estimate the user's emotions and adjust data management methods based on the estimated emotions. For example, if the user is relaxed, the management unit can provide detailed data management options. For example, if the user is in a hurry, the management unit can provide concise and quick data management options. For example, if the user is stressed, the management unit can provide data management options that reduce stress. This allows the data management method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the data management method based on emotions.

[0091] The management department can select the optimal management method by referring to past data management history when managing data. For example, the management department may prioritize suggesting data management methods previously used by the user. For example, the management department may predict and suggest a management method to be used during a specific time period based on the user's past data management history. For example, the management department may analyze the user's past data management history and suggest the most efficient management method. This allows for the selection of the optimal management method based on past data management history. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department may input past data management history data into a generative AI and have the generative AI select the optimal management method.

[0092] The management unit can apply different management algorithms depending on the data category during data management. For example, the management unit can apply a management algorithm specifically for images to image data, a management algorithm specifically for text data to text data, and a management algorithm specifically for video data to video data. This allows for the application of management algorithms appropriate to the data category. Some or all of the above-described processes in the management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the management unit can input data category data into a generative AI and have the generative AI execute the application of the management algorithm.

[0093] The management unit can estimate the user's emotions and determine data management priorities based on those estimated emotions. For example, if the user is in a hurry, the management unit will prioritize managing important data. If the user is relaxed, the management unit will perform detailed data management. If the user is stressed, the management unit will perform data management that reduces stress. This allows for the determination of data management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the determination of emotion-based data management priorities.

[0094] The management unit can determine management priorities based on when the data was captured during data management. For example, the management unit may prioritize the management of recently captured data. For example, the management unit may prioritize the management of data captured during a specific event period. For example, the management unit may prioritize the management of data captured during a specific season to highlight seasonal changes. This allows for the determination of management priorities based on when the data was captured. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input the data capture date data into a generative AI and have the generative AI perform the determination of management priorities.

[0095] The management unit can adjust the order of data management based on the relationships between data. For example, the management unit can manage highly relevant data consecutively. For example, the management unit can manage less relevant data in a simplified manner. For example, the management unit can perform narrative-based management based on the relationships between data. This allows for adjustment of the order of management based on the relationships between data. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input data relationship data into a generative AI and have the generative AI perform the adjustment of the order of management.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The observation recording system can estimate the user's emotions and adjust how the observation records are displayed based on the estimated emotions. For example, if the user is relaxed, the display can include detailed explanations. If the user is in a hurry, the display can be concise and to the point. If the user is excited, the display can emphasize the emotion. This allows the system to provide an appropriate display method according to the user's emotions.

[0098] The observation recording system can estimate the user's emotions and adjust the notification method of the observation recording based on the estimated emotions. For example, if the user is relaxed, a gentle notification sound can be used. If the user is in a hurry, a short, to-the-point notification can be used. If the user is excited, a visually stimulating notification can be used. This allows the system to provide an appropriate notification method according to the user's emotions.

[0099] The observation recording system can estimate the user's emotions and adjust how the observation records are saved based on those estimated emotions. For example, if the user is relaxed, detailed saving options can be provided. If the user is in a hurry, concise and quick saving options can be provided. If the user is excited, emotion-emphasizing saving options can be provided. This allows the system to provide appropriate saving methods tailored to the user's emotions.

[0100] The observation recording system can estimate the user's emotions and adjust how the observation records are shared based on those estimated emotions. For example, if the user is relaxed, detailed sharing options can be provided. If the user is in a hurry, concise and quick sharing options can be provided. If the user is excited, emotion-emphasizing sharing options can be provided. This allows for the provision of appropriate sharing methods tailored to the user's emotions.

[0101] The observation recording system can estimate the user's emotions and adjust how the observation records are edited based on those estimated emotions. For example, if the user is relaxed, detailed editing options can be provided. If the user is in a hurry, concise and quick editing options can be provided. If the user is excited, emotion-emphasizing editing options can be provided. This allows the system to provide an appropriate editing method that matches the user's emotions.

[0102] The observation recording system can analyze observation data and provide users with suggestions for improving their observation records. For example, based on the data analysis results, it can suggest increasing the frequency of observations, changing the observation recording method, or enriching the content of the observation records. This can improve the quality of the observation records.

[0103] The observation recording system can analyze observation data and suggest ways to improve the efficiency of observation recording for the user. For example, it can suggest automating observation recording based on the data analysis results. It can suggest simplifying observation recording procedures based on the data analysis results. It can suggest changing observation recording tools based on the data analysis results. This can improve the efficiency of observation recording.

[0104] The observation record system can analyze observation record data and suggest improvements to the user's observation record security. For example, based on the data analysis results, it can suggest improvements to the observation record backup method. Based on the data analysis results, it can suggest changes to the observation record encryption method. Based on the data analysis results, it can suggest a review of the observation record access permissions. This can improve the security of the observation record.

[0105] The observation record system can analyze observation record data and suggest improvements to the user's experience with the observation record system. For example, it can suggest improvements to the observation record interface based on the data analysis results. It can suggest changes to how the observation record is operated based on the data analysis results. It can suggest revisions to how the observation record is displayed based on the data analysis results. This can improve the overall usability of the observation record system.

[0106] The observation record system can analyze observation record data and suggest customizations to the user. For example, it can suggest changing the observation record template based on the data analysis results. It can suggest customizing the observation record format based on the data analysis results. It can suggest reviewing the observation record layout based on the data analysis results. This enables customization of the observation record.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The upload unit uploads images taken with a smartphone in succession at regular intervals via the cloud. For example, it uploads images taken with a smartphone to cloud storage. The upload unit can be scheduled to automatically upload images to the cloud, and users can also manually upload images to the cloud. Step 2: The generation unit analyzes the images uploaded by the upload unit and automatically generates a time-lapse video. For example, it analyzes the images using image recognition technology and generates a time-lapse video that displays images taken at regular intervals in sequence. It can also generate a time-lapse video with effects that emphasize changes in the images. Step 3: The recording unit translates the changes in the photographs into text based on the time-lapse video generated by the generation unit. For example, it uses natural language generation technology to translate the content of the time-lapse video into text, generating detailed descriptions of each frame of the time-lapse video, as well as concise summaries of the key points of the time-lapse video. Step 4: The management department manages the changes documented by the records department in conjunction with cloud storage. For example, it automatically backs up data stored in cloud storage and shares it with other users. It also provides a tagging function to make data stored in cloud storage easier to search.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the upload unit, generation unit, recording unit, and management unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the upload unit is implemented by the control unit 46A of the smart device 14 and uploads images taken with a smartphone to cloud storage. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a time-lapse video by analyzing the uploaded images. The recording unit is implemented by the specific processing unit 290 of the data processing device 12 and documents the changes in the photographs based on the generated time-lapse video. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and manages the data in cooperation with cloud storage. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the upload unit, generation unit, recording unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the smart glasses 214 and uploads images taken with a smartphone to cloud storage. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a time-lapse video by analyzing the uploaded images. The recording unit is implemented by the specific processing unit 290 of the data processing unit 12 and documents the changes in the photographs based on the generated time-lapse video. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the data in cooperation with cloud storage. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the upload unit, generation unit, recording unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the headset terminal 314 and uploads images taken with a smartphone to cloud storage. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a time-lapse video by analyzing the uploaded images. The recording unit is implemented by the specific processing unit 290 of the data processing unit 12 and documents the changes in the photographs based on the generated time-lapse video. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the data in cooperation with cloud storage. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the upload unit, generation unit, recording unit, and management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the robot 414 and uploads images taken with a smartphone to cloud storage. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a time-lapse video by analyzing the uploaded images. The recording unit is implemented by the specific processing unit 290 of the data processing unit 12 and transcribes the changes in the photographs based on the generated time-lapse video. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the data in cooperation with cloud storage. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The upload unit uploads images taken with a smartphone in succession at regular intervals via the cloud, The generation unit analyzes the images uploaded by the aforementioned upload unit and automatically generates a time-lapse video, A recording unit that converts the changes in a photograph into text based on the time-lapse video generated by the generation unit, The system includes a management unit that manages the changes documented by the recording unit in conjunction with cloud storage. A system characterized by the following features. (Note 2) The aforementioned management department, Integrate with cloud storage to back up and share data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, It integrates with educational apps and journaling apps to centralize observation records. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is This system analyzes images taken consecutively with a smartphone at regular intervals and automatically generates a time-lapse video. The system described in Appendix 1, characterized by the features described herein. (Note 5) The recording unit is, Textualizing the changes in the photos based on the generated time-lapse video. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned upload unit is It estimates the user's emotions and adjusts the timing of image uploads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned upload unit is Analyze the user's past upload history and select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned upload unit is When uploading images, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned upload unit is It estimates the user's emotions and prioritizes the images to upload based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned upload unit is When uploading images, the system prioritizes uploading images that are more relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned upload unit is When uploading an image, the system analyzes the user's social media activity and uploads relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the time-lapse video generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating time-lapse videos, adjust the level of detail based on the importance of each image. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating time-lapse videos, different generation algorithms are applied depending on the image category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the time-lapse video based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating time-lapse videos, the generation priority is determined based on when the images were taken. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating time-lapse videos, the generation order is adjusted based on the relevance of the images. The system described in Appendix 1, characterized by the features described herein. (Note 18) The recording unit is, It estimates the user's emotions and adjusts how the changes are described based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recording unit is, When describing changes, adjust the level of detail in the text based on the importance of the images. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recording unit is, When documenting changes, different documenting algorithms are applied depending on the image category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recording unit is, It estimates the user's emotions and adjusts the length of the change text based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recording unit is, When documenting changes, prioritize the text based on when the images were taken. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recording unit is, When describing changes, adjust the order of the text based on the relevance of the images. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, We estimate user sentiment and adjust data management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, When managing data, refer to past data management history to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing data, different management algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, It estimates user sentiment and prioritizes data management based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing data, prioritize management based on when the data was captured. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing data, adjust the order of management based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The upload unit uploads images taken with a smartphone in succession at regular intervals via the cloud, The generation unit analyzes the images uploaded by the aforementioned upload unit and automatically generates a time-lapse video, A recording unit that converts the changes in a photograph into text based on the time-lapse video generated by the generation unit, The system includes a management unit that manages the changes documented by the recording unit in conjunction with cloud storage. A system characterized by the following features.

2. The aforementioned management department, Integrate with cloud storage to back up and share data. The system according to feature 1.

3. The aforementioned management department, It integrates with educational apps and journaling apps to centralize observation records. The system according to feature 1.

4. The generating unit is This system analyzes images taken consecutively with a smartphone at regular intervals and automatically generates a time-lapse video. The system according to feature 1.

5. The aforementioned recording unit is Textualizing the changes in photographs based on the generated time-lapse video. The system according to feature 1.

6. The aforementioned upload unit, It estimates the user's emotions and adjusts the timing of image uploads based on those estimated emotions. The system according to feature 1.

7. The aforementioned upload unit, Analyze the user's past upload history and select the optimal upload method. The system according to feature 1.

8. The aforementioned upload unit, When uploading images, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned upload unit, It estimates the user's emotions and prioritizes the images to upload based on those estimated emotions. The system according to feature 1.

10. The aforementioned upload unit, When uploading images, the system prioritizes uploading images that are more relevant to the user's geographical location. The system according to feature 1.

Images