system

Abstract

The system according to the embodiment aims to record audio and images of a specific person with a timestamp and automatically share them on social networking sites. [Solution] A system according to an embodiment includes an audio recording unit, a timestamp assigning unit, an image recording unit, and a sharing unit. The audio recording unit records the audio of a specific person. The timestamp assigning unit assigns a timestamp to the audio recorded by the audio recording unit. The image recording unit records what the specific person is holding in their hand. The timestamp assigning unit assigns a timestamp to the image recorded by the image recording unit. The sharing unit shares the audio or image to which the timestamp has been assigned by the timestamp assigning unit on a social networking site.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to record audio and images of specific individuals with timestamps and automatically share them on social media.

[0005] The system according to the embodiment aims to record audio and images of a specific person with a timestamp and automatically share them on social networking sites. [Means for solving the problem]

[0006] The system according to the embodiment includes an audio recording unit, a timestamp assigning unit, an image recording unit, and a sharing unit. The audio recording unit records the audio of a specific person. The timestamp assigning unit assigns a timestamp to the audio recorded by the audio recording unit. The image recording unit records what the specific person is holding in their hand. The timestamp assigning unit assigns a timestamp to the image recorded by the image recording unit. The sharing unit shares the audio or image assigned a timestamp by the timestamp assigning unit on an SNS. [Effects of the Invention]

[0007] The system according to the embodiment can record audio and images of specific people with timestamps and automatically share them on social media. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A record sharing system according to an embodiment of the present invention automatically records the voice of a specific person and what they are holding, saves the recorded voice and what they are holding with a timestamp, and shares the recorded voice and what they are holding via social networking services (SNS). This record sharing system automatically records the voice of a specific person using a smartphone and saves the recorded voice and what they are holding with a timestamp. Furthermore, it automatically records what a specific person is holding with a device such as a home camera and saves the recorded voice and what they are holding as an image with a timestamp. These recorded voice and images are automatically shared via social networking services (SNS). For example, the record sharing system can record the moment a child speaks for the first time without missing a beat. Next, the record sharing system can record what the child picks up. Furthermore, the record sharing system can automatically share the recorded voice and images via social networking services such as LINE (registered trademark). This system allows users to easily share memories with family and friends. This allows the record sharing system to automatically record the voice of a specific person and what they are holding with a timestamp, save the recorded voice and what they are holding, and share the recorded voice and what they are holding via social networking services (SNS).

[0029] A record sharing system according to an embodiment includes an audio recording unit, a timestamp assigning unit, an image recording unit, and a sharing unit. The audio recording unit records the audio of a specific person. For example, the audio recording unit can automatically record the audio of a specific person using a smartphone. The audio recording unit can also use voice recognition technology using deep learning. For example, the audio recording unit can record the moment a child speaks for the first time without missing a beat. The timestamp assigning unit assigns a timestamp to the audio recorded by the audio recording unit. For example, the timestamp assigning unit can automatically assign a timestamp at the time of recording. The image recording unit records an object held by a specific person. For example, the image recording unit can automatically record an object held by a specific person using a device such as a home camera. The image recording unit can also use image recognition technology using deep learning. For example, the image recording unit can record an object a child has picked up. The timestamp assigning unit assigns a timestamp to an image recorded by the image recording unit. For example, the timestamp assigning unit can automatically assign a timestamp at the time of capturing a photo. The sharing unit shares the audio and images that have been timestamped by the timestamp assignment unit on a social networking site. For example, the sharing unit can automatically share on a social networking site using an API. This allows the recording and sharing system according to the embodiment to automatically record the audio and objects held by a specific person, save them with a timestamp, and share them via a social networking site.

[0030] The voice recording unit can utilize deep learning-based speech recognition technology. Deep learning is a type of neural network that achieves high-precision speech recognition by learning from large amounts of data. For example, the voice recording unit can use deep learning-based speech recognition technology to record the moment a child speaks for the first time without missing it. By using deep learning, the accuracy of speech recognition is improved, and the voice of a specific person can be accurately recorded. For example, the voice recording unit can recognize a child's voice and record it. Furthermore, the voice recording unit can also use deep learning to filter out background noise and record only the voice of a specific person. For example, when recording a child's voice in a noisy environment, the voice recording unit can remove background noise and record only the child's voice clearly. In this way, by using deep learning, the accuracy of speech recognition is improved, and the voice of a specific person can be accurately recorded.

[0031] The image recording unit can utilize image recognition technology based on deep learning. Deep learning is a type of neural network that achieves high-precision image recognition by learning from large amounts of data. For example, the image recording unit can use image recognition technology based on deep learning to record objects picked up by a child. By using deep learning, the accuracy of image recognition is improved, and objects held in the hands of a specific person can be accurately recorded. For example, the image recording unit can recognize a toy held by a child and record its image. Furthermore, the image recording unit can use deep learning to filter out background objects and record only objects held in the hands of a specific person. For example, the image recording unit can filter out other objects in order to prioritize recording the toy held by the child. Thus, by using deep learning, the accuracy of image recognition is improved, and objects held in the hands of a specific person can be accurately recorded.

[0032] The timestamp assigning unit can automatically assign a timestamp at the timing of recording or capturing an image. A timestamp assigns date and time information to recorded audio or images. For example, the timestamp assigning unit can automatically assign a timestamp when recording starts. This makes it possible to accurately determine when the audio was recorded. The timestamp assigning unit can also automatically assign a timestamp when capturing an image. This makes it possible to accurately determine when the recorded image was captured. For example, the timestamp assigning unit can also assign a timestamp when recording ends. This makes it possible to accurately record the start and end times of recording. The timestamp assigning unit can also assign a timestamp when capturing an image. This makes it possible to accurately record the start and end times of capturing an image. Automatically assigning a timestamp at the timing of recording or capturing an image enables accurate recording.

[0033] The sharing unit can automatically share to social media platforms using an API. An API (Application Programming Interface) is an interface for exchanging data between different software programs. For example, the sharing unit can automatically share recorded audio and images using the LINE API. This allows users to easily share memories with family and friends. The sharing unit can also automatically share recorded audio and images using the Facebook (registered trademark) API. This allows users to share widely through social media platforms. For example, the sharing unit can automatically share recorded audio and images using the X (formerly Twitter (registered trademark)) API. This allows for real-time sharing. The sharing unit can also automatically share recorded audio and images using the Instagram (registered trademark) API. This allows users to share in a visually appealing format. This allows for automatic sharing to social media platforms using an API.

[0034] The audio recording unit can filter background sounds during audio recording to record only the audio of a specific person. For example, when recording a child's voice in a noisy environment, the audio recording unit can remove background noise to clearly record only the child's voice. For example, when recording a child's voice during a family conversation, the audio recording unit can filter out the voices of other family members to record only the child's voice. Furthermore, when recording a child's voice outdoors, such as in a park, the audio recording unit can remove wind and car sounds to record only the child's voice. In this way, by filtering the background sounds, the audio of a specific person can be clearly recorded. Filtering the background sounds is performed using, for example, noise canceling technology or a filtering algorithm. For example, the audio recording unit can remove background noise using noise canceling technology. Furthermore, the audio recording unit can also use a filtering algorithm to emphasize audio in a specific frequency band and suppress background sounds. In this way, by filtering the background sounds, the audio of a specific person can be clearly recorded.

[0035] The audio recording unit can automatically start recording when a specific keyword or phrase is triggered during audio recording. For example, the audio recording unit can automatically start audio recording the moment a child says "mom" or "dad." For example, the audio recording unit can automatically start audio recording when a child says "hello" or "goodbye." The audio recording unit can also automatically start audio recording when a child says "thank you" or "sorry." In this way, by using specific keywords or phrases as triggers, important moments can be recorded without missing. The specific keywords or phrases can be set using, for example, a predefined list or voice recognition technology. For example, the audio recording unit can recognize specific keywords or phrases based on a predefined list. The audio recording unit can also detect specific keywords or phrases in real time using voice recognition technology. In this way, by using specific keywords or phrases as triggers, important moments can be recorded without missing.

[0036] The audio recording unit can automatically adjust recording settings based on specific location and environmental conditions when recording audio. For example, the audio recording unit can automatically apply settings to suppress echoes when recording audio indoors. For example, the audio recording unit can automatically apply settings to suppress wind noise when recording audio outdoors. Furthermore, the audio recording unit can automatically apply settings to suppress engine noise when recording audio inside a vehicle. This automatically adjusts recording settings based on location and environmental conditions, enabling optimal audio recording. Specific locations and environmental conditions can be identified using, for example, GPS data or environmental sensors. For example, the audio recording unit can identify a current location using GPS data and apply settings according to that location. Furthermore, the audio recording unit can detect surrounding environmental conditions using environmental sensors and apply settings according to those conditions. This automatically adjusts recording settings based on location and environmental conditions, enabling optimal audio recording.

[0037] The audio recording unit can learn the voice characteristics of a specific person during audio recording to improve identification accuracy. The audio recording unit can, for example, learn the voice characteristics of a child and record the child's voice separately from the voices of other family members. For example, the audio recording unit can learn the tone and pitch of a child's voice and track changes as the child grows. The audio recording unit can also learn the child's voice patterns to improve the accuracy of identifying specific phrases and words. By learning the voice characteristics, identification accuracy is improved and the audio of a specific person can be accurately recorded. Learning the voice characteristics is performed, for example, by collecting voice samples or using a machine learning algorithm. For example, the audio recording unit can collect voice samples of a child and learn the voice characteristics based on the samples using a machine learning algorithm. The audio recording unit can also use the collected voice samples to build a model that identifies specific phrases and words. By learning the voice characteristics, identification accuracy is improved and the audio of a specific person can be accurately recorded.

[0038] The timestamping unit can add additional information based on the content of the recorded audio or image when timestamping. For example, the timestamping unit can add keywords to the timestamp of an audio recording based on what is being said. For example, the timestamping unit can add tags to the timestamp of an image recording based on the objects depicted. The timestamping unit can also add the name of the person speaking to the timestamp of an audio recording. By adding additional information based on the recorded content, the details of the recording can be made clearer. The addition of additional information is done, for example, using metadata or tag information. For example, the timestamping unit can add keywords as metadata to an audio recording based on what is being said. The timestamping unit can also add tag information to an image recording based on the objects depicted. By adding additional information based on the recorded content, the details of the recording can be made clearer.

[0039] The timestamp assigning unit can assign timestamps corresponding to multiple time zones when assigning a timestamp. For example, for audio or images recorded while traveling abroad, the timestamp assigning unit can assign both local time and home time to the timestamp. For example, if family members are in different time zones, the timestamp assigning unit can assign timestamps corresponding to each time zone. Furthermore, if an individual is attending an international event, the timestamp assigning unit can assign timestamps corresponding to the time zone of the location where the event is being held. This allows for support of multiple time zones, thereby enabling accurate management of recordings made in different locations. Support for multiple time zones is achieved, for example, by using time zone settings or a time conversion method. For example, the timestamp assigning unit can assign both local time and home time to the timestamp of recorded audio or images. Furthermore, the timestamp assigning unit can also assign timestamps corresponding to different time zones using time zone settings. This allows for support of multiple time zones, thereby enabling accurate management of recordings made in different locations.

[0040] The timestamp assigning unit can customize the timestamp based on specific event or calendar information when assigning a timestamp. For example, the timestamp assigning unit can add a birthday icon to the timestamp for audio or images recorded on a child's birthday. For example, the timestamp assigning unit can add a travel icon to the timestamp for audio or images recorded during a family trip. Furthermore, if the user is participating in a school event, the timestamp assigning unit can add an event icon to the timestamp. This allows the details of the record to be more clearly defined by customizing the timestamp based on the event or calendar information. The specific event or calendar information can be acquired using, for example, a calendar API or an event database. For example, the timestamp assigning unit can acquire specific event or calendar information using the calendar API and customize the timestamp based on that information. Furthermore, the timestamp assigning unit can acquire specific event information using an event database and customize the timestamp based on that information. This allows the details of the record to be more clearly defined by customizing the timestamp based on the event or calendar information.

[0041] The timestamping unit can adjust the precision of the timestamp based on the importance of the recorded data when timestamping. For example, the timestamping unit can assign timestamps with second-level precision to audio and images recorded at important events. For example, the timestamping unit can assign timestamps with minute-level precision to everyday records. Furthermore, the timestamping unit can assign timestamps with day-level precision to long-term records. This allows for more accurate management of important records by adjusting the precision of the timestamp based on the importance of the data. The evaluation of data importance is performed, for example, based on user settings or the content of the data. For example, the timestamping unit can adjust the precision of the timestamp based on the importance level set by the user. The timestamping unit can also analyze the content of the recorded data and adjust the precision of the timestamp based on its importance. This allows for more accurate management of important records by adjusting the precision of the timestamp based on the importance of the data.

[0042] The image recording unit can perform filtering during image recording to prioritize the recording of specific objects or scenes. For example, the image recording unit can filter other objects to prioritize the recording of a toy held by a child. For example, the image recording unit can filter the background scenery to prioritize the recording of a flower picked up by a child. Furthermore, the image recording unit can filter surrounding objects to prioritize the recording of a drawing made by a child. This allows for the recording of important moments without missing them by prioritizing the recording of specific objects or scenes. Identifying specific objects or scenes can be done, for example, using image recognition technology or a predefined list. For example, the image recording unit can use image recognition technology to detect and filter specific objects or scenes in real time. The image recording unit can also prioritize the recording of specific objects or scenes based on a predefined list. This allows for the recording of important moments without missing them by prioritizing the recording of specific objects or scenes.

[0043] The image recording unit can simultaneously record images from multiple camera angles and select the optimal image. For example, the image recording unit can record a scene of a child playing from multiple camera angles and select the image from the best angle. For example, the image recording unit can record a scene of a child blowing out a birthday cake from multiple camera angles and select the image from the best angle. The image recording unit can also record a scene of a child running at a sports day from multiple camera angles and select the image from the best angle. In this way, the optimal image can be selected by recording from multiple camera angles. The multiple camera angles are set using, for example, a camera arrangement or an angle selection method. For example, the image recording unit can arrange multiple cameras at different angles and record simultaneously. The image recording unit can also use an algorithm that automatically selects the image from the best angle from the recorded images. In this way, the optimal image can be selected by recording from multiple camera angles.

[0044] The image recording unit can automatically adjust recording settings based on specific location and environmental conditions when recording an image. For example, the image recording unit can automatically adjust the brightness of lighting when recording an image indoors. For example, the image recording unit can automatically adjust the intensity of sunlight when recording an image outdoors. Furthermore, the image recording unit can automatically adjust the reflection of light from windows when recording an image inside a car. This enables optimal image recording by automatically adjusting recording settings based on location and environmental conditions. Specific locations and environmental conditions can be identified using, for example, GPS data or an environmental sensor. For example, the image recording unit can identify the current location using GPS data and apply settings according to that location. Furthermore, the image recording unit can detect surrounding environmental conditions using an environmental sensor and apply settings according to those conditions. This enables optimal image recording by automatically adjusting recording settings based on location and environmental conditions.

[0045] The image recording unit can detect a specific person's movement or gesture when recording an image and start recording. The image recording unit can start image recording, for example, the moment a child waves their hand. For example, the image recording unit can start image recording the moment a child jumps. The image recording unit can also start image recording the moment a child smiles. In this way, by detecting movements and gestures, important moments can be recorded without missing. Movements and gestures are detected using, for example, image recognition technology or a sensor. For example, the image recording unit can use image recognition technology to detect specific movements and gestures in real time and start recording. The image recording unit can also use a sensor to detect specific movements and gestures and start recording. In this way, by detecting movements and gestures, important moments can be recorded without missing.

[0046] The sharing unit can share data in a format optimized for a specific SNS platform when sharing. For example, when sharing to LINE, the sharing unit can share images and audio in an optimal resolution and format. For example, when sharing to Facebook, the sharing unit can share images and audio in an optimal resolution and format. Furthermore, when sharing to Instagram, the sharing unit can share images and audio in an optimal resolution and format. This improves the efficiency of sharing by sharing data in a format optimized for the SNS platform. Settings for a specific SNS platform are made based on, for example, the API or guidelines of each platform. For example, the sharing unit can share data in an optimal resolution and format using the LINE API. Furthermore, the sharing unit can share data in an optimal resolution and format using the Facebook API. This improves the efficiency of sharing by sharing data in a format optimized for the SNS platform.

[0047] The sharing unit can customize the shared content based on the interests and past responses of the sharing destination user when sharing. The sharing unit can, for example, select images and audio to be shared based on content that the sharing destination user has given many "likes" in the past. For example, if the sharing destination user is interested in a particular theme, the sharing unit can prioritize sharing content related to that theme. The sharing unit can also customize the images and audio to be shared based on content on which the sharing destination user has left many comments in the past. This improves the effectiveness of sharing by customizing the shared content based on the user's interests and past responses. The user's interests and past responses are acquired based on, for example, a user profile and past behavioral data. For example, the sharing unit can analyze the user profile to identify the user's interests. The sharing unit can also analyze past behavioral data to customize the shared content based on the user's responses. This improves the effectiveness of sharing by customizing the shared content based on the user's interests and past responses.

[0048] The sharing unit can automatically schedule sharing based on specific event or calendar information when sharing. For example, the sharing unit can automatically share audio and images recorded on a child's birthday on the actual birthday. For example, the sharing unit can automatically share audio and images recorded during a family trip on the last day of the trip. Furthermore, if a user is participating in a school event, the sharing unit can automatically share the audio and images after the event ends. In this way, by scheduling sharing based on event or calendar information, sharing can be achieved at important times. Specific event or calendar information can be acquired using, for example, a calendar API or an event database. For example, the sharing unit can acquire specific event or calendar information using a calendar API and schedule sharing based on that information. Furthermore, the sharing unit can acquire specific event information using an event database and schedule sharing based on that information. In this way, by scheduling sharing based on event or calendar information, sharing can be achieved at important times.

[0049] When sharing, the sharing unit can select the optimal sharing method by taking into account the geographical location information of the user to whom the sharing is to be performed. For example, if the user to whom the sharing is to be performed is nearby, the sharing unit can share in real time. For example, if the user to whom the sharing is to be performed is far away, the sharing unit can share via a social networking service. Furthermore, if the user to whom the sharing is to be performed is in a different time zone, the sharing unit can share at an appropriate time. This allows sharing to be performed in the optimal way by taking into account the geographical location information. The geographical location information is acquired using, for example, GPS data or a location information service. For example, the sharing unit can identify the current location of the user to whom the sharing is to be performed using GPS data and select a sharing method according to that location. Furthermore, the sharing unit can also acquire the geographical location information of the user to whom the sharing is to be performed using a location information service and select a sharing method based on that information. This allows sharing to be performed in the optimal way by taking into account the geographical location information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The record sharing system can further include a voice analysis unit. The voice analysis unit can analyze the voice of a specific person and automatically assign tags based on the content of the voice. For example, if a child says "toys," the voice analysis unit can assign the tag "toys" to the voice. Also, if a child says "park," the voice analysis unit can assign the tag "park" to the voice. Furthermore, if a child says "friends," the voice analysis unit can assign the tag "friends" to the voice. In this way, assigning tags based on the content of the voice makes it easier to search later.

[0052] The record sharing system can further include an image analysis unit. The image analysis unit can analyze what a specific person is holding in their hand and automatically assign tags based on the content of the object. For example, the image analysis unit can analyze the toys a child is holding and assign a tag "toy" to the image. The image analysis unit can also analyze the books the child is holding and assign a tag "book" to the image. The image analysis unit can also analyze the food the child is holding and assign a tag "food" to the image. In this way, assigning tags based on the content of the image makes it easier to search for the image later.

[0053] The record sharing system can further include an audio trigger unit. The audio trigger unit can start audio recording when a specific keyword or phrase is used as a trigger. For example, the audio trigger unit can start audio recording the moment a child says "Mom." The audio trigger unit can also start audio recording the moment a child says "Dad." The audio trigger unit can also start audio recording the moment a child says "Hello." This makes it possible to record important moments without missing them by using specific keywords or phrases as triggers.

[0054] The record sharing system can further include an image trigger unit. The image trigger unit can start image recording using a specific action or gesture as a trigger. For example, the image trigger unit can start image recording the moment a child waves their hand. The image trigger unit can also start image recording the moment a child jumps. The image trigger unit can also start image recording the moment a child smiles. In this way, by using an action or gesture as a trigger, important moments can be recorded without missing them.

[0055] The recording sharing system may further include an audio filtering unit. The audio filtering unit can filter background sounds to record only the audio of a specific person. For example, when recording a child's voice in a noisy environment, the audio filtering unit can remove background noise to clearly record only the child's voice. When recording a child's voice during a family conversation, the audio filtering unit can filter out the voices of other family members to record only the child's voice. When recording a child's voice outdoors, such as in a park, the audio filtering unit can remove wind and car sounds to record only the child's voice. In this way, the audio of a specific person can be clearly recorded by filtering out background sounds.

[0056] The record sharing system may further include an image filtering unit. The image filtering unit can filter out other objects in order to prioritize recording of specific objects or scenes. For example, the image filtering unit can filter out other objects in order to prioritize recording of toys held by a child. The image filtering unit can also filter out background scenery in order to prioritize recording of flowers picked up by a child. The image filtering unit can also filter out surrounding objects in order to prioritize recording of pictures drawn by a child. In this way, by prioritizing recording of specific objects or scenes, important moments can be recorded without missing any.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The voice recorder records the voice of a specific person. For example, the voice recorder can automatically record the voice of a specific person using a smartphone, or it can use voice recognition technology based on deep learning. This allows you to record the moment a child speaks for the first time. Step 2: The timestamping unit adds a timestamp to the audio recorded by the audio recording unit. For example, a timestamp can be automatically added at the time of recording. Step 3: The image recording unit records what a specific person is holding in their hand. For example, a device like a home camera can be used to automatically record what a specific person is holding in their hand, and image recognition technology using deep learning can also be used. This allows you to record what a child has picked up. Step 4: The timestamping unit adds a timestamp to the image recorded by the image recording unit. For example, a timestamp can be automatically added at the time of shooting. Step 5: The sharing unit shares the audio and images, which have been timestamped by the timestamping unit, to social media. For example, it can be automatically shared to social media using an API.

[0059] (Example 2) A record sharing system according to an embodiment of the present invention automatically records the voice of a specific person and what they are holding, saves the recorded voice and what they are holding with a timestamp, and shares the recorded voice and what they are holding via social networking services (SNS). This record sharing system automatically records the voice of a specific person using a smartphone and saves the recorded voice and what they are holding with a timestamp. Furthermore, it automatically records what a specific person is holding with a device such as a home camera and saves the recorded voice and what they are holding as an image with a timestamp. These recorded voice and images are automatically shared via social networking services (SNS). For example, the record sharing system can record the moment a child speaks for the first time without missing a beat. Next, the record sharing system can record what the child picks up. Furthermore, the record sharing system can automatically share the recorded voice and images via social networking services (SNS) such as LINE. This system allows users to easily share memories with family and friends. This allows the record sharing system to automatically record the voice of a specific person and what they are holding with a timestamp, save the recorded voice and what they are holding, and share the recorded voice and what they are holding via social networking services (SNS).

[0060] A record sharing system according to an embodiment includes an audio recording unit, a timestamp assigning unit, an image recording unit, and a sharing unit. The audio recording unit records the audio of a specific person. For example, the audio recording unit can automatically record the audio of a specific person using a smartphone. The audio recording unit can also use voice recognition technology using deep learning. For example, the audio recording unit can record the moment a child speaks for the first time without missing a beat. The timestamp assigning unit assigns a timestamp to the audio recorded by the audio recording unit. For example, the timestamp assigning unit can automatically assign a timestamp at the time of recording. The image recording unit records an object held by a specific person. For example, the image recording unit can automatically record an object held by a specific person using a device such as a home camera. The image recording unit can also use image recognition technology using deep learning. For example, the image recording unit can record an object a child has picked up. The timestamp assigning unit assigns a timestamp to an image recorded by the image recording unit. For example, the timestamp assigning unit can automatically assign a timestamp at the time of capturing a photo. The sharing unit shares the audio and images that have been timestamped by the timestamp assignment unit on a social networking site. For example, the sharing unit can automatically share on a social networking site using an API. This allows the recording and sharing system according to the embodiment to automatically record the audio and objects held by a specific person, save them with a timestamp, and share them via a social networking site.

[0061] The voice recording unit can utilize deep learning-based speech recognition technology. Deep learning is a type of neural network that achieves high-precision speech recognition by learning from large amounts of data. For example, the voice recording unit can use deep learning-based speech recognition technology to record the moment a child speaks for the first time without missing it. By using deep learning, the accuracy of speech recognition is improved, and the voice of a specific person can be accurately recorded. For example, the voice recording unit can recognize a child's voice and record it. Furthermore, the voice recording unit can also use deep learning to filter out background noise and record only the voice of a specific person. For example, when recording a child's voice in a noisy environment, the voice recording unit can remove background noise and record only the child's voice clearly. In this way, by using deep learning, the accuracy of speech recognition is improved, and the voice of a specific person can be accurately recorded.

[0062] The image recording unit can utilize image recognition technology based on deep learning. Deep learning is a type of neural network that achieves high-precision image recognition by learning from large amounts of data. For example, the image recording unit can use image recognition technology based on deep learning to record objects picked up by a child. By using deep learning, the accuracy of image recognition is improved, and objects held in the hands of a specific person can be accurately recorded. For example, the image recording unit can recognize a toy held by a child and record its image. Furthermore, the image recording unit can use deep learning to filter out background objects and record only objects held in the hands of a specific person. For example, the image recording unit can filter out other objects in order to prioritize recording the toy held by the child. Thus, by using deep learning, the accuracy of image recognition is improved, and objects held in the hands of a specific person can be accurately recorded.

[0063] The timestamp assigning unit can automatically assign a timestamp at the timing of recording or capturing an image. A timestamp assigns date and time information to recorded audio or images. For example, the timestamp assigning unit can automatically assign a timestamp when recording starts. This makes it possible to accurately determine when the audio was recorded. The timestamp assigning unit can also automatically assign a timestamp when capturing an image. This makes it possible to accurately determine when the recorded image was captured. For example, the timestamp assigning unit can also assign a timestamp when recording ends. This makes it possible to accurately record the start and end times of recording. The timestamp assigning unit can also assign a timestamp when capturing an image. This makes it possible to accurately record the start and end times of capturing an image. Automatically assigning a timestamp at the timing of recording or capturing an image enables accurate recording.

[0064] The sharing section can automatically share to social media platforms using APIs. An API (Application Programming Interface) is an interface for exchanging data between different software programs. For example, the sharing section can automatically share recorded audio and images using the LINE API. This allows you to easily share memories with family and friends. The sharing section can also automatically share recorded audio and images using the Facebook API. This allows for widespread sharing through social media platforms. For example, the sharing section can automatically share recorded audio and images using the X (formerly Twitter) API. This allows for real-time sharing. The sharing section can also automatically share recorded audio and images using the Instagram API. This allows for sharing in a visually appealing format. This allows for automatic sharing to social media platforms using APIs.

[0065] The audio recording unit can estimate the emotion of a specific person and adjust the start timing of audio recording based on the estimated emotion. For example, if a child is excited, the audio recording unit can detect that emotion and start audio recording while the excited state continues. For example, if a child is sad, the audio recording unit can detect that emotion and start audio recording to record an event that caused the sadness. Furthermore, if a child is laughing, the audio recording unit can detect that emotion and start audio recording to record the laughter. In this way, by adjusting the start timing of audio recording based on the emotion, important moments can be recorded without missing. Emotion estimation is performed using techniques such as voice analysis and facial expression analysis. For example, the audio recording unit can analyze the tone and pitch of a child's voice to estimate the emotion. Furthermore, the audio recording unit can capture the child's facial expression with a camera and estimate the emotion using facial expression analysis technology. In this way, by adjusting the start timing of audio recording based on the emotion, important moments can be recorded without missing.

[0066] The audio recording unit can filter background sounds during audio recording to record only the audio of a specific person. For example, when recording a child's voice in a noisy environment, the audio recording unit can remove background noise to clearly record only the child's voice. For example, when recording a child's voice during a family conversation, the audio recording unit can filter out the voices of other family members to record only the child's voice. Furthermore, when recording a child's voice outdoors, such as in a park, the audio recording unit can remove wind and car sounds to record only the child's voice. In this way, by filtering the background sounds, the audio of a specific person can be clearly recorded. Filtering the background sounds is performed using, for example, noise canceling technology or a filtering algorithm. For example, the audio recording unit can remove background noise using noise canceling technology. Furthermore, the audio recording unit can also use a filtering algorithm to emphasize audio in a specific frequency band and suppress background sounds. In this way, by filtering the background sounds, the audio of a specific person can be clearly recorded.

[0067] The audio recording unit can automatically start recording when a specific keyword or phrase is triggered during audio recording. For example, the audio recording unit can automatically start audio recording the moment a child says "mom" or "dad." For example, the audio recording unit can automatically start audio recording when a child says "hello" or "goodbye." The audio recording unit can also automatically start audio recording when a child says "thank you" or "sorry." In this way, by using specific keywords or phrases as triggers, important moments can be recorded without missing. The specific keywords or phrases can be set using, for example, a predefined list or voice recognition technology. For example, the audio recording unit can recognize specific keywords or phrases based on a predefined list. The audio recording unit can also detect specific keywords or phrases in real time using voice recognition technology. In this way, by using specific keywords or phrases as triggers, important moments can be recorded without missing.

[0068] The audio recording unit can estimate the emotion of a specific person and determine the priority of audio recording based on the estimated emotion. For example, if a child is excited, the audio recording unit can detect that emotion and prioritize audio recording of the excited state. For example, if a child is sad, the audio recording unit can detect that emotion and prioritize audio recording of events that cause sadness. Furthermore, if a child is laughing, the audio recording unit can detect that emotion and prioritize audio recording of laughter. In this way, by determining the priority of audio recording based on emotion, important audio can be preferentially recorded. Emotion estimation is performed using techniques such as voice analysis and facial expression analysis. For example, the audio recording unit can analyze the tone and pitch of a child's voice to estimate the emotion. Furthermore, the audio recording unit can capture the child's facial expression with a camera and estimate the emotion using facial expression analysis technology. In this way, by determining the priority of audio recording based on emotion, important audio can be preferentially recorded.

[0069] The audio recording unit can automatically adjust recording settings based on specific location and environmental conditions when recording audio. For example, the audio recording unit can automatically apply settings to suppress echoes when recording audio indoors. For example, the audio recording unit can automatically apply settings to suppress wind noise when recording audio outdoors. Furthermore, the audio recording unit can automatically apply settings to suppress engine noise when recording audio inside a vehicle. This automatically adjusts recording settings based on location and environmental conditions, enabling optimal audio recording. Specific locations and environmental conditions can be identified using, for example, GPS data or environmental sensors. For example, the audio recording unit can identify a current location using GPS data and apply settings according to that location. Furthermore, the audio recording unit can detect surrounding environmental conditions using environmental sensors and apply settings according to those conditions. This automatically adjusts recording settings based on location and environmental conditions, enabling optimal audio recording.

[0070] The audio recording unit can learn the voice characteristics of a specific person during audio recording to improve identification accuracy. The audio recording unit can, for example, learn the voice characteristics of a child and record the child's voice separately from the voices of other family members. For example, the audio recording unit can learn the tone and pitch of a child's voice and track changes as the child grows. The audio recording unit can also learn the child's voice patterns to improve the accuracy of identifying specific phrases and words. By learning the voice characteristics, identification accuracy is improved and the audio of a specific person can be accurately recorded. Learning the voice characteristics is performed, for example, by collecting voice samples or using a machine learning algorithm. For example, the audio recording unit can collect voice samples of a child and learn the voice characteristics based on the samples using a machine learning algorithm. The audio recording unit can also use the collected voice samples to build a model that identifies specific phrases and words. By learning the voice characteristics, identification accuracy is improved and the audio of a specific person can be accurately recorded.

[0071] The timestamp assigning unit can estimate the emotion of a specific person and adjust the format of the timestamp based on the estimated emotion. For example, if a child is excited, the timestamp assigning unit can add an emotion icon to the timestamp. For example, if a child is sad, the timestamp assigning unit can add an emotion icon to the timestamp. Also, if a child is smiling, the timestamp assigning unit can add an emotion icon to the timestamp. In this way, by adjusting the format of the timestamp based on the emotion, changes in emotion can be visually expressed. Emotion estimation is performed using techniques such as voice analysis and facial expression analysis. For example, the timestamp assigning unit can analyze the tone and pitch of a child's voice to estimate the emotion. Also, the timestamp assigning unit can capture the child's facial expression with a camera and estimate the emotion using facial expression analysis technology. In this way, by adjusting the format of the timestamp based on the emotion, changes in emotion can be visually expressed.

[0072] The timestamping unit can add additional information based on the content of the recorded audio or image when timestamping. For example, the timestamping unit can add keywords to the timestamp of an audio recording based on what is being said. For example, the timestamping unit can add tags to the timestamp of an image recording based on the objects depicted. The timestamping unit can also add the name of the person speaking to the timestamp of an audio recording. By adding additional information based on the recorded content, the details of the recording can be made clearer. The addition of additional information is done, for example, using metadata or tag information. For example, the timestamping unit can add keywords as metadata to an audio recording based on what is being said. The timestamping unit can also add tag information to an image recording based on the objects depicted. By adding additional information based on the recorded content, the details of the recording can be made clearer.

[0073] The timestamp assigning unit can assign timestamps corresponding to multiple time zones when assigning a timestamp. For example, for audio or images recorded while traveling abroad, the timestamp assigning unit can assign both local time and home time to the timestamp. For example, if family members are in different time zones, the timestamp assigning unit can assign timestamps corresponding to each time zone. Furthermore, if an individual is attending an international event, the timestamp assigning unit can assign timestamps corresponding to the time zone of the location where the event is being held. This allows for support of multiple time zones, thereby enabling accurate management of recordings made in different locations. Support for multiple time zones is achieved, for example, by using time zone settings or a time conversion method. For example, the timestamp assigning unit can assign both local time and home time to the timestamp of recorded audio or images. Furthermore, the timestamp assigning unit can also assign timestamps corresponding to different time zones using time zone settings. This allows for support of multiple time zones, thereby enabling accurate management of recordings made in different locations.

[0074] The timestamp assigning unit can estimate the emotion of a specific person and adjust the display method of the timestamp based on the estimated emotion. For example, if a child is excited, the timestamp assigning unit can highlight the timestamp. For example, if a child is sad, the timestamp assigning unit can display the timestamp in a subdued color. Also, if a child is smiling, the timestamp assigning unit can display the timestamp in a bright color. In this way, by adjusting the display method of the timestamp based on the emotion, changes in emotion can be visually expressed. Emotion estimation is performed using techniques such as voice analysis and facial expression analysis. For example, the timestamp assigning unit can analyze the tone and pitch of a child's voice to estimate the emotion. Also, the timestamp assigning unit can capture the child's facial expression with a camera and estimate the emotion using facial expression analysis technology. In this way, by adjusting the display method of the timestamp based on the emotion, changes in emotion can be visually expressed.

[0075] The timestamp assigning unit can customize the timestamp based on specific event or calendar information when assigning a timestamp. For example, the timestamp assigning unit can add a birthday icon to the timestamp for audio or images recorded on a child's birthday. For example, the timestamp assigning unit can add a travel icon to the timestamp for audio or images recorded during a family trip. Furthermore, if the user is participating in a school event, the timestamp assigning unit can add an event icon to the timestamp. This allows the details of the record to be more clearly defined by customizing the timestamp based on the event or calendar information. The specific event or calendar information can be acquired using, for example, a calendar API or an event database. For example, the timestamp assigning unit can acquire specific event or calendar information using the calendar API and customize the timestamp based on that information. Furthermore, the timestamp assigning unit can acquire specific event information using an event database and customize the timestamp based on that information. This allows the details of the record to be more clearly defined by customizing the timestamp based on the event or calendar information.

[0076] The timestamping unit can adjust the precision of the timestamp based on the importance of the recorded data when timestamping. For example, the timestamping unit can assign timestamps with second-level precision to audio and images recorded at important events. For example, the timestamping unit can assign timestamps with minute-level precision to everyday records. Furthermore, the timestamping unit can assign timestamps with day-level precision to long-term records. This allows for more accurate management of important records by adjusting the precision of the timestamp based on the importance of the data. The evaluation of data importance is performed, for example, based on user settings or the content of the data. For example, the timestamping unit can adjust the precision of the timestamp based on the importance level set by the user. The timestamping unit can also analyze the content of the recorded data and adjust the precision of the timestamp based on its importance. This allows for more accurate management of important records by adjusting the precision of the timestamp based on the importance of the data.

[0077] The image recording unit can estimate the emotion of a specific person and adjust the start timing of image recording based on the estimated emotion. For example, if a child is excited, the image recording unit can detect that emotion and start image recording while the excited state continues. For example, if a child is sad, the image recording unit can detect that emotion and start image recording to record an event that caused the sadness. Furthermore, if a child is laughing, the image recording unit can detect that emotion and start image recording to record the laughter. In this way, by adjusting the start timing of image recording based on the emotion, important moments can be recorded without missing. Emotion estimation is performed using techniques such as voice analysis and facial expression analysis. For example, the image recording unit can analyze the tone and pitch of a child's voice to estimate the emotion. Furthermore, the image recording unit can capture the child's facial expression with a camera and estimate the emotion using facial expression analysis technology. In this way, by adjusting the start timing of image recording based on the emotion, important moments can be recorded without missing.

[0078] The image recording unit can perform filtering during image recording to prioritize the recording of specific objects or scenes. For example, the image recording unit can filter other objects to prioritize the recording of a toy held by a child. For example, the image recording unit can filter the background scenery to prioritize the recording of a flower picked up by a child. Furthermore, the image recording unit can filter surrounding objects to prioritize the recording of a drawing made by a child. This allows for the recording of important moments without missing them by prioritizing the recording of specific objects or scenes. Identifying specific objects or scenes can be done, for example, using image recognition technology or a predefined list. For example, the image recording unit can use image recognition technology to detect and filter specific objects or scenes in real time. The image recording unit can also prioritize the recording of specific objects or scenes based on a predefined list. This allows for the recording of important moments without missing them by prioritizing the recording of specific objects or scenes.

[0079] The image recording unit can simultaneously record images from multiple camera angles and select the optimal image. For example, the image recording unit can record a scene of a child playing from multiple camera angles and select the image from the best angle. For example, the image recording unit can record a scene of a child blowing out a birthday cake from multiple camera angles and select the image from the best angle. The image recording unit can also record a scene of a child running at a sports day from multiple camera angles and select the image from the best angle. In this way, the optimal image can be selected by recording from multiple camera angles. The multiple camera angles are set using, for example, a camera arrangement or an angle selection method. For example, the image recording unit can arrange multiple cameras at different angles and record simultaneously. The image recording unit can also use an algorithm that automatically selects the image from the best angle from the recorded images. In this way, the optimal image can be selected by recording from multiple camera angles.

[0080] The image recording unit can estimate the emotion of a specific person and determine the priority of image recording based on the estimated emotion. For example, if a child is excited, the image recording unit can detect the emotion and prioritize image recording of the excited state. For example, if a child is sad, the image recording unit can detect the emotion and prioritize image recording of events that cause sadness. Furthermore, if a child is laughing, the image recording unit can detect the emotion and prioritize image recording of laughter. In this way, important images can be preferentially recorded by determining the priority of image recording based on emotion. Emotion estimation is performed using techniques such as voice analysis and facial expression analysis. For example, the image recording unit can analyze the tone and pitch of a child's voice to estimate the emotion. Furthermore, the image recording unit can capture the child's facial expression with a camera and estimate the emotion using facial expression analysis technology. In this way, important images can be preferentially recorded by determining the priority of image recording based on emotion.

[0081] The image recording unit can automatically adjust recording settings based on specific location and environmental conditions when recording an image. For example, the image recording unit can automatically adjust the brightness of lighting when recording an image indoors. For example, the image recording unit can automatically adjust the intensity of sunlight when recording an image outdoors. Furthermore, the image recording unit can automatically adjust the reflection of light from windows when recording an image inside a car. This enables optimal image recording by automatically adjusting recording settings based on location and environmental conditions. Specific locations and environmental conditions can be identified using, for example, GPS data or an environmental sensor. For example, the image recording unit can identify the current location using GPS data and apply settings according to that location. Furthermore, the image recording unit can detect surrounding environmental conditions using an environmental sensor and apply settings according to those conditions. This enables optimal image recording by automatically adjusting recording settings based on location and environmental conditions.

[0082] The image recording unit can detect a specific person's movement or gesture when recording an image and start recording. The image recording unit can start image recording, for example, the moment a child waves their hand. For example, the image recording unit can start image recording the moment a child jumps. The image recording unit can also start image recording the moment a child smiles. In this way, by detecting movements and gestures, important moments can be recorded without missing. Movements and gestures are detected using, for example, image recognition technology or a sensor. For example, the image recording unit can use image recognition technology to detect specific movements and gestures in real time and start recording. The image recording unit can also use a sensor to detect specific movements and gestures and start recording. In this way, by detecting movements and gestures, important moments can be recorded without missing.

[0083] The sharing unit can estimate the emotions of a specific person and determine the priority of content to share based on the estimated emotions. For example, if a child is excited, the sharing unit can detect that emotion and prioritize sharing a record of the excited state. For example, if a child is sad, the sharing unit can detect that emotion and prioritize sharing a record of the event that caused the sadness. Also, if a child is laughing, the sharing unit can detect that emotion and prioritize sharing a record of the laughter. In this way, by determining the priority of content to share based on emotions, important content can be shared preferentially. Emotion estimation is performed using technologies such as voice analysis and facial expression analysis. For example, the sharing unit can analyze the tone and pitch of a child's voice to estimate their emotions. The sharing unit can also capture a child's facial expressions with a camera and estimate their emotions using facial expression analysis technology. In this way, by determining the priority of content to share based on emotions, important content can be shared preferentially.

[0084] The sharing unit can share data in a format optimized for a specific SNS platform when sharing. For example, when sharing to LINE, the sharing unit can share images and audio in an optimal resolution and format. For example, when sharing to Facebook, the sharing unit can share images and audio in an optimal resolution and format. Furthermore, when sharing to Instagram, the sharing unit can share images and audio in an optimal resolution and format. This improves the efficiency of sharing by sharing data in a format optimized for the SNS platform. Settings for a specific SNS platform are made based on, for example, the API or guidelines of each platform. For example, the sharing unit can share data in an optimal resolution and format using the LINE API. Furthermore, the sharing unit can share data in an optimal resolution and format using the Facebook API. This improves the efficiency of sharing by sharing data in a format optimized for the SNS platform.

[0085] The sharing unit can customize the shared content based on the interests and past responses of the sharing destination user when sharing. The sharing unit can, for example, select images and audio to be shared based on content that the sharing destination user has given many "likes" in the past. For example, if the sharing destination user is interested in a particular theme, the sharing unit can prioritize sharing content related to that theme. The sharing unit can also customize the images and audio to be shared based on content on which the sharing destination user has left many comments in the past. This improves the effectiveness of sharing by customizing the shared content based on the user's interests and past responses. The user's interests and past responses are acquired based on, for example, a user profile and past behavioral data. For example, the sharing unit can analyze the user profile to identify the user's interests. The sharing unit can also analyze past behavioral data to customize the shared content based on the user's responses. This improves the effectiveness of sharing by customizing the shared content based on the user's interests and past responses.

[0086] The sharing unit can estimate the emotion of a specific person and adjust the timing of sharing based on the estimated emotion. For example, if a child is excited, the sharing unit can detect the emotion and share while the excited state continues. For example, if a child is sad, the sharing unit can detect the emotion and adjust the timing of sharing an event that caused the sadness. Furthermore, if a child is laughing, the sharing unit can detect the emotion and adjust the timing of sharing a recording of the laughter. In this way, by adjusting the sharing timing based on the emotion, sharing can be performed at the optimal timing. Emotion estimation is performed using technologies such as voice analysis and facial expression analysis. For example, the sharing unit can analyze the tone and pitch of the child's voice to estimate the emotion. Furthermore, the sharing unit can capture the child's facial expression with a camera and estimate the emotion using facial expression analysis technology. In this way, by adjusting the sharing timing based on the emotion, sharing can be performed at the optimal timing.

[0087] The sharing unit can automatically schedule sharing based on specific event or calendar information when sharing. For example, the sharing unit can automatically share audio and images recorded on a child's birthday on the actual birthday. For example, the sharing unit can automatically share audio and images recorded during a family trip on the last day of the trip. Furthermore, if a user is participating in a school event, the sharing unit can automatically share the audio and images after the event ends. In this way, by scheduling sharing based on event or calendar information, sharing can be achieved at important times. Specific event or calendar information can be acquired using, for example, a calendar API or an event database. For example, the sharing unit can acquire specific event or calendar information using a calendar API and schedule sharing based on that information. Furthermore, the sharing unit can acquire specific event information using an event database and schedule sharing based on that information. In this way, by scheduling sharing based on event or calendar information, sharing can be achieved at important times.

[0088] When sharing, the sharing unit can select the optimal sharing method by taking into account the geographical location information of the user to whom the sharing is to be performed. For example, if the user to whom the sharing is to be performed is nearby, the sharing unit can share in real time. For example, if the user to whom the sharing is to be performed is far away, the sharing unit can share via a social networking service. Furthermore, if the user to whom the sharing is to be performed is in a different time zone, the sharing unit can share at an appropriate time. This allows sharing to be performed in the optimal way by taking into account the geographical location information. The geographical location information is acquired using, for example, GPS data or a location information service. For example, the sharing unit can identify the current location of the user to whom the sharing is to be performed using GPS data and select a sharing method according to that location. Furthermore, the sharing unit can also acquire the geographical location information of the user to whom the sharing is to be performed using a location information service and select a sharing method based on that information. This allows sharing to be performed in the optimal way by taking into account the geographical location information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the audio recording unit, timestamping unit, image recording unit, and sharing unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the audio recording unit records the voice of a specific person using the microphone 38B of the smart device 14 and processes the audio data with the control unit 46A. The timestamping unit adds a timestamp to the audio data with the identification processing unit 290 of the data processing unit 12. The image recording unit records what a specific person is holding using the camera 42 of the smart device 14 and processes the image data with the control unit 46A. The sharing unit shares the timestamped audio and images with SNS using the identification processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the audio recording unit, timestamping unit, image recording unit, and sharing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the audio recording unit records the voice of a specific person using the microphone 238 of the smart glasses 214 and processes the audio data with the control unit 46A. The timestamping unit adds a timestamp to the audio data with the identification processing unit 290 of the data processing unit 12. The image recording unit records what a specific person is holding in their hand using the camera 42 of the smart glasses 214 and processes the image data with the control unit 46A. The sharing unit shares the timestamped audio and images with SNS using the identification processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the audio recording unit, timestamping unit, image recording unit, and sharing unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the audio recording unit records the voice of a specific person using the microphone 238 of the headset terminal 314 and processes the audio data with the control unit 46A. The timestamping unit adds a timestamp to the audio data with the identification processing unit 290 of the data processing unit 12. The image recording unit records what a specific person is holding using the camera 42 of the headset terminal 314 and processes the image data with the control unit 46A. The sharing unit shares the timestamped audio and images with SNS using the identification processing unit 290 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the voice recording unit, timestamping unit, image recording unit, and sharing unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the voice recording unit records the voice of a specific person using the microphone 238 of the robot 414 and processes the voice data with the control unit 46A. The timestamping unit adds a timestamp to the voice data with the identification processing unit 290 of the data processing unit 12, for example. The image recording unit records what a specific person is holding in their hand using the camera 42 of the robot 414 and processes the image data with the control unit 46A. The sharing unit shares the timestamped voice and image with SNS using the identification processing unit 290 of the data processing unit 12, for example.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The record sharing system can further include a voice analysis unit. The voice analysis unit can analyze the voice of a specific person and automatically assign tags based on the content of the voice. For example, if a child says "toys," the voice analysis unit can assign the tag "toys" to the voice. Also, if a child says "park," the voice analysis unit can assign the tag "park" to the voice. Furthermore, if a child says "friends," the voice analysis unit can assign the tag "friends" to the voice. In this way, assigning tags based on the content of the voice makes it easier to search later.

[0091] The recording and sharing system can also be equipped with a voice feedback unit. This unit can analyze a specific person's voice in real time and provide appropriate feedback. For example, if a child says "I don't understand," the voice feedback unit can respond with "What's wrong?" Similarly, if a child says "That's fun," the voice feedback unit can respond with "That's great!" Furthermore, if a child says "I'm tired," the voice feedback unit can respond with "Shall we take a break?" This facilitates communication by providing appropriate feedback based on voice.

[0092] The record sharing system can further include an image analysis unit. The image analysis unit can analyze what a specific person is holding in their hand and automatically assign tags based on the content of the object. For example, the image analysis unit can analyze the toys a child is holding and assign a tag "toy" to the image. The image analysis unit can also analyze the books the child is holding and assign a tag "book" to the image. The image analysis unit can also analyze the food the child is holding and assign a tag "food" to the image. In this way, assigning tags based on the content of the image makes it easier to search for the image later.

[0093] The record sharing system may further include an emotion analysis unit. The emotion analysis unit may analyze the voice and facial expression of a specific person and estimate their emotion. For example, the emotion analysis unit may analyze the tone and pitch of a child's voice and estimate whether the child is excited. The emotion analysis unit may also capture the child's facial expression with a camera and estimate whether the child is smiling. The emotion analysis unit may also analyze the child's tone of voice and facial expression and estimate whether the child is sad. This allows for the estimation of emotions to enrich the content of the record.

[0094] The record sharing system can further include an audio trigger unit. The audio trigger unit can start audio recording when a specific keyword or phrase is used as a trigger. For example, the audio trigger unit can start audio recording the moment a child says "Mom." The audio trigger unit can also start audio recording the moment a child says "Dad." The audio trigger unit can also start audio recording the moment a child says "Hello." This makes it possible to record important moments without missing them by using specific keywords or phrases as triggers.

[0095] The record sharing system can further include an image trigger unit. The image trigger unit can start image recording using a specific action or gesture as a trigger. For example, the image trigger unit can start image recording the moment a child waves their hand. The image trigger unit can also start image recording the moment a child jumps. The image trigger unit can also start image recording the moment a child smiles. In this way, by using an action or gesture as a trigger, important moments can be recorded without missing them.

[0096] The recording sharing system may further include an audio filtering unit. The audio filtering unit can filter background sounds to record only the audio of a specific person. For example, when recording a child's voice in a noisy environment, the audio filtering unit can remove background noise to clearly record only the child's voice. When recording a child's voice during a family conversation, the audio filtering unit can filter out the voices of other family members to record only the child's voice. When recording a child's voice outdoors, such as in a park, the audio filtering unit can remove wind and car sounds to record only the child's voice. In this way, the audio of a specific person can be clearly recorded by filtering out background sounds.

[0097] The recording and sharing system can also be equipped with an emotional feedback unit. This unit can estimate a person's emotions and provide appropriate feedback based on those emotions. For example, if a child is excited, the emotional feedback unit can respond with "That's great!" If a child is sad, it can respond with "What's wrong?" Furthermore, if a child is laughing, it can respond with "You look like you're having fun!" This facilitates communication by providing appropriate feedback based on emotions.

[0098] The record sharing system may further include an image filtering unit. The image filtering unit can filter out other objects in order to prioritize recording of specific objects or scenes. For example, the image filtering unit can filter out other objects in order to prioritize recording of toys held by a child. The image filtering unit can also filter out background scenery in order to prioritize recording of flowers picked up by a child. The image filtering unit can also filter out surrounding objects in order to prioritize recording of pictures drawn by a child. In this way, by prioritizing recording of specific objects or scenes, important moments can be recorded without missing any.

[0099] The recording sharing system may further include an emotion trigger unit. The emotion trigger unit may estimate the emotion of a specific person and adjust the start timing of recording based on the emotion. For example, if a child is excited, the emotion trigger unit may detect the emotion and start recording while the excited state continues. Also, if a child is sad, the emotion trigger unit may detect the emotion and start recording to record an event that caused the sadness. Furthermore, if a child is laughing, the emotion trigger unit may detect the emotion and start recording to record the laughter. In this way, by adjusting the start timing of recording based on the emotion, important moments can be recorded without missing.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The voice recorder records the voice of a specific person. For example, the voice recorder can automatically record the voice of a specific person using a smartphone, or it can use voice recognition technology based on deep learning. This allows you to record the moment a child speaks for the first time. Step 2: The timestamping unit adds a timestamp to the audio recorded by the audio recording unit. For example, a timestamp can be automatically added at the time of recording. Step 3: The image recording unit records what a specific person is holding in their hand. For example, a device like a home camera can be used to automatically record what a specific person is holding in their hand, and image recognition technology using deep learning can also be used. This allows you to record what a child has picked up. Step 4: The timestamping unit adds a timestamp to the image recorded by the image recording unit. For example, a timestamp can be automatically added at the time of shooting. Step 5: The sharing unit shares the audio and images, which have been timestamped by the timestamping unit, to social media. For example, it can be automatically shared to social media using an API.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice recording unit for recording the voice of a specific person; a timestamp assigning unit that assigns a timestamp to the audio recorded by the audio recording unit; an image recording unit that records what a particular person is holding in their hand; a time stamp assigning unit that assigns a time stamp to the image recorded by the image recording unit; a sharing unit that shares the audio or image to which the timestamp has been added by the timestamp adding unit on an SNS. A system characterized by:

2. The voice recording unit Use deep learning-based voice recognition technology The system of claim 1 .

3. The image recording unit Use image recognition technology using deep learning The system of claim 1 .

4. The time stamp assignment unit Automatically add timestamps to recordings or photos The system of claim 1 .

5. The common part is Automatically share to social media using API The system of claim 1 .

6. The voice recording unit Estimate the emotion of a specific person and adjust the start timing of audio recording based on the estimated emotion. The system of claim 1 .

7. The voice recording unit When recording audio, filter out background noise and record only the voice of a specific person. The system of claim 1 .

8. The voice recording unit When recording audio, you can automatically start recording by triggering specific keywords or phrases. The system of claim 1 .

Images