System

Abstract

An object of the system according to the embodiment is to efficiently organize a large amount of photo data and facilitate searching and sharing.SOLUTION: A system includes a photograph content recognition unit, a tag generation unit, a search unit, a classification unit, and a sharing unit. The photograph content recognition unit recognizes the content of the photograph. The tag generation unit automatically generates an appropriate tag based on the content recognized by the photo content recognition unit. The search unit searches for a photo using the tag generated by the tag generation unit. The classification unit classifies the photograph using the tag generated by the tag generation unit. The sharing unit shares the photo using the tag generated by the tag generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently organize, search, and share large amounts of photo data.

[0005] The system according to the embodiment aims to efficiently organize large amounts of photo data and make it easy to search and share. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo content recognition unit, a tag generation unit, a search unit, a classification unit, and a sharing unit. The photo content recognition unit recognizes the content of a photo. The tag generation unit automatically generates appropriate tags based on the content recognized by the photo content recognition unit. The search unit searches for photos using the tags generated by the tag generation unit. The classification unit classifies photos using the tags generated by the tag generation unit. The sharing unit shares photos using the tags generated by the tag generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize large amounts of photo data and make it easy to search and share them. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 photo organization application according to an embodiment of the present invention is a system that uses AI to recognize the content of photos and automatically generate appropriate tags. This system allows users to easily search, classify, and share photos. This allows the photo organization application to efficiently manage photos and utilize large amounts of photo data in business operations.

[0029] A photo organizing application according to an embodiment includes a photo content recognition unit, a tag generation unit, a search unit, a classification unit, and a sharing unit. The photo content recognition unit recognizes the content of a photo. For example, the photo content recognition unit generates tags such as "mountain," "ocean," and "sunset" for a landscape photo, and tags such as "family," "friends," and "event" for a portrait photo. The tag generation unit automatically generates appropriate tags based on the content recognized by the photo content recognition unit. For example, the tag generation unit analyzes photo data using a generation AI to generate appropriate tags. The search unit searches for photos using the tags generated by the tag generation unit. For example, the search unit displays a list of mountain photos by searching for the tag "mountain." The classification unit classifies photos using the tags generated by the tag generation unit. For example, the classification unit automatically classifies photos tagged "travel" into a "travel" folder and photos tagged "work" into a "work" folder. The sharing unit shares photos using the tags generated by the tag generation unit. For example, when sharing a photo tagged "family" with family members, the sharing unit allows all family members to access the photo by specifying the tag. This allows the photo organizing application according to the embodiment to enable users to easily search, classify, and share photos. For example, a user can easily search for photos using the generated tags and quickly find a specific photo. Furthermore, automatically classifying photos using the generated tags can eliminate the need for manual organization. Furthermore, easily sharing photos using the generated tags can reduce the effort required to share photos with family and friends.

[0030] The photo content recognition unit can analyze background information of a photo and generate tags based on that information. For example, the photo content recognition unit analyzes the metadata of a photo and generates tags based on the location where the photo was taken. For example, a photo taken in Paris is tagged with tags such as "Paris" or "Eiffel Tower." The photo content recognition unit also analyzes the time of the photo and generates tags based on that time period. For example, a photo taken in the evening is tagged with tags such as "evening" or "sunset." The photo content recognition unit also analyzes background information of a photo and generates related tags. For example, a photo with a mountain background is tagged with tags such as "mountain" or "nature." In this way, by analyzing background information of a photo and generating tags based on that information, it becomes possible to search and classify photos related to the location and time of their capture.

[0031] The photo content recognition unit can recognize the brand or product name of an object in a photo and generate tags based on that. For example, the photo content recognition unit analyzes objects in a photo, recognizes their brand or product name, and generates tags. For example, a photo containing an Apple product is tagged with "Apple" or "iPhone." The photo content recognition unit also uses object recognition technology to identify product names in a photo and generate tags based on those product names. For example, a photo containing Nike sneakers is tagged with "Nike" or "Sneakers." The photo content recognition unit also analyzes brand logos in a photo and generates tags related to that brand. For example, a photo containing the Coca-Cola logo is tagged with "Coca-Cola" or "Drinks." This allows the system to recognize the brand or product name of objects in a photo and generate tags based on that, making it possible to search and classify photos related to brand or product names.

[0032] The photo content recognition unit can also generate similar tags for video data and attach tags to specific scenes. The photo content recognition unit, for example, analyzes video data and attaches tags to specific scenes. For example, a mountain scene in a video is tagged with tags such as "mountain" or "nature." The photo content recognition unit also analyzes the content of the video and generates appropriate tags for each scene. For example, a scene in the video in which a person is smiling is tagged with tags such as "smile" or "joy." The photo content recognition unit also analyzes specific scenes in the video and generates tags based on those scenes. For example, an ocean scene in the video is tagged with tags such as "ocean" or "wave." In this way, by generating similar tags for video data and attaching tags to specific scenes, videos can be searched and classified.

[0033] The search unit can analyze the search history and learn the user's search patterns to optimize future searches. For example, the search unit analyzes the user's search history and learns the search patterns to optimize future searches. For example, for a user who has searched for "travel" many times in the past, photos related to "travel" are preferentially displayed. The search unit also learns the user's search patterns based on the search history and builds a system that customizes future search results. For example, for a user who frequently searches for a specific tag, photos related to that tag are preferentially displayed. The search unit also analyzes the user's search history and learns the search patterns to make future searches more efficient. For example, related tags are automatically suggested based on the past search history. In this way, the search history is analyzed, the user's search patterns are learned, and future searches are optimized, improving the user's search experience.

[0034] The classification unit can classify photos chronologically or geographically based on the date and time the photos were taken and the location where they were taken. For example, the classification unit analyzes the date and time the photos were taken and classifies the photos chronologically. For example, photos taken in a specific year or month are classified into respective folders. The classification unit also builds a system that classifies photos geographically based on the location where the photos were taken. For example, photos taken in a specific city or country are classified into respective folders. The classification unit also classifies photos chronologically and geographically by combining the date and time of the photos and the location. For example, photos taken in a specific year and location are classified. This makes it easier to organize photos by classifying them chronologically or geographically based on the date and time of the photos and the location where they were taken.

[0035] The sharing unit can automatically generate comments and messages for photos when they are shared, improving the sharing experience. For example, the sharing unit uses AI to automatically generate comments and messages for photos to be shared. For example, adding a message such as "This photo is a wonderful memory!" The sharing unit also builds a system that automatically generates appropriate comments and messages based on the content of the photo. For example, adding a message such as "You can feel the family bond" to a family photo. The sharing unit also automatically generates comments and messages for photos when they are shared, improving the sharing experience. For example, adding a message such as "What wonderful travel memories!" to a travel photo. In this way, comments and messages are automatically generated for photos when they are shared, improving the sharing experience and increasing user satisfaction.

[0036] The sharing unit can analyze the sharing history and suggest an optimal sharing method based on past sharing patterns. The sharing unit, for example, analyzes the sharing history and suggests an optimal sharing method based on past sharing patterns. For example, the sharing unit makes suggestions based on tags of photos frequently shared with a specific user. The sharing unit also builds a system that suggests an optimal sharing method based on the sharing history. For example, the sharing unit makes suggestions the next time sharing is performed based on tags of photos previously shared with family. The sharing unit also analyzes the sharing history and suggests an optimal sharing method based on past sharing patterns. For example, the sharing unit makes suggestions based on tags of photos shared at a specific event. In this way, the sharing experience is improved by analyzing the sharing history and suggesting an optimal sharing method based on past sharing patterns.

[0037] The business operations department can integrate the photo data with other business data to improve overall business efficiency. For example, the business operations department integrates the photo data with project management data to build a system that visually grasps the progress of a project. For example, photos of a construction site are linked to a project management tool. The business operations department also integrates the photo data with other business data to improve overall business efficiency. For example, in a product development process, photos of prototypes are linked to development data. The business operations department also integrates the photo data with business data to build a system that visually grasps areas for improvement in business processes. For example, photos of the manufacturing process are linked to quality control data. In this way, the photo data can be integrated with other business data to improve overall business efficiency, thereby improving business efficiency and quality.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The photo organization application may further include a voice recognition unit. The voice recognition unit analyzes voice data related to a photo and generates tags based on the content of the voice data. For example, if a voice memo related to a photo mentions "travel," the tag "travel" is generated. The voice recognition unit may also analyze ambient sounds at the time of photo capture and generate tags based on the sounds. For example, if the sound includes the sound of waves, tags such as "ocean" or "beach" are generated. The voice recognition unit may also analyze comments dictated by the user on the photo and generate tags based on the contents of the comments. This makes it possible to search and classify photos using voice data.

[0040] The photo organization application may further include a weather information acquisition unit. The weather information acquisition unit acquires weather information at the time based on the date, time, and location of a photo's capture and generates a tag based on the weather. For example, it generates tags such as "sunny" or "blue sky" for a photo taken on a sunny day. The weather information acquisition unit may also analyze the weather depicted in the background of a photo and generate a tag based on that weather. For example, it may generate tags such as "snow" or "winter" for a photo of a snowy landscape. Furthermore, the weather information acquisition unit may utilize weather forecast data to provide information useful for future photo planning. This makes it possible to search and classify photos using weather information.

[0041] The photo organization application may further include a facial recognition unit. The facial recognition unit analyzes the faces of people in a photo and generates tags based on the people. For example, for a photo that includes a specific family member, it may generate tags such as "family" or the name of the family member. The facial recognition unit may also analyze the age and gender of the people in the photo and generate tags based on that information. For example, for a photo of a child, it may generate tags such as "child" or "play." The facial recognition unit may also analyze the relationships between people in the photo and generate tags based on those relationships. This makes it possible to search and classify photos using facial recognition technology.

[0042] The photo organization application may further include an event recognition unit. The event recognition unit obtains information about events taking place at the time a photo was taken based on the date, time, and location of the photo, and generates tags based on the events. For example, if a specific concert or sporting event was taking place, tags such as "concert" or "sports" are generated. The event recognition unit can also analyze the content of a photo and generate event tags based on that content. For example, a photo of a birthday party may generate tags such as "birthday" or "party." The event recognition unit can also link with the user's calendar information and generate tags based on scheduled events. This makes it possible to search and classify photos using event information.

[0043] The photo organization application may further include a color analysis unit. The color analysis unit analyzes the color information of a photo and generates tags based on the color. For example, if the entire photo is primarily blue, tags such as "blue" or "ocean" may be generated. The color analysis unit may also analyze areas where a specific color is emphasized and generate tags based on that color. For example, a photo containing red flowers may generate tags such as "red" or "flowers." The color analysis unit may also analyze the color balance of a photo and generate tags based on that balance. This makes it possible to search and classify photos using color information.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The photo content recognition unit recognizes the content of the photo. For example, for a landscape photo, it generates tags such as "mountain," "ocean," and "sunset," and for a portrait photo, it generates tags such as "family," "friends," and "event." Step 2: The tag generation unit automatically generates appropriate tags based on the content recognized by the photo content recognition unit. For example, it uses generation AI to analyze the photo data and generate appropriate tags. Step 3: The search unit searches for photos using the tags generated by the tag generation unit. For example, by searching for the tag "mountain," photos of mountains are displayed in a list. Step 4: The classification unit classifies the photos using the tags generated by the tag generation unit. For example, photos tagged with "travel" are automatically classified into a "travel" folder, and photos tagged with "work" are automatically classified into a "work" folder. Step 5: The sharing unit shares the photo using the tag generated by the tag generation unit. For example, if a photo tagged with "family" is to be shared with family members, the photo can be accessed by all family members by specifying the tag and sharing it.

[0046] (Example 2) A photo organization application according to an embodiment of the present invention is a system that uses AI to recognize the content of photos and automatically generate appropriate tags. This system allows users to easily search, classify, and share photos. This allows the photo organization application to efficiently manage photos and utilize large amounts of photo data in business operations.

[0047] A photo organizing application according to an embodiment includes a photo content recognition unit, a tag generation unit, a search unit, a classification unit, and a sharing unit. The photo content recognition unit recognizes the content of a photo. For example, the photo content recognition unit generates tags such as "mountain," "ocean," and "sunset" for a landscape photo, and tags such as "family," "friends," and "event" for a portrait photo. The tag generation unit automatically generates appropriate tags based on the content recognized by the photo content recognition unit. For example, the tag generation unit analyzes photo data using a generation AI to generate appropriate tags. The search unit searches for photos using the tags generated by the tag generation unit. For example, the search unit displays a list of mountain photos by searching for the tag "mountain." The classification unit classifies photos using the tags generated by the tag generation unit. For example, the classification unit automatically classifies photos tagged "travel" into a "travel" folder and photos tagged "work" into a "work" folder. The sharing unit shares photos using the tags generated by the tag generation unit. For example, when sharing a photo tagged "family" with family members, the sharing unit allows all family members to access the photo by specifying the tag. This allows the photo organizing application according to the embodiment to enable users to easily search, classify, and share photos. For example, a user can easily search for photos using the generated tags and quickly find a specific photo. Furthermore, automatically classifying photos using the generated tags can eliminate the need for manual organization. Furthermore, easily sharing photos using the generated tags can reduce the effort required to share photos with family and friends.

[0048] The photo content recognition unit can estimate the emotions of people in a photo and generate tags based on those emotions. For example, the photo content recognition unit analyzes the facial expressions of people in a photo to estimate their emotions. For example, it generates tags such as "joy" or "fun" for photos of smiling people. The photo content recognition unit also uses an emotion estimation function to analyze the emotions of people in a photo in real time and generate tags based on those emotions. For example, it tags photos of people with sad expressions with "sadness" or "sentimental." The photo content recognition unit also automatically generates emotion-related tags based on the emotion estimation results for the photo. For example, it tags photos of people with surprised expressions with "surprise" or "surprise." This makes it possible to search and classify photos related to emotions by estimating the emotions of people in a photo and generating tags based on those emotions.

[0049] The photo content recognition unit can analyze background information of a photo and generate tags based on that information. For example, the photo content recognition unit analyzes the metadata of a photo and generates tags based on the location where the photo was taken. For example, a photo taken in Paris is tagged with tags such as "Paris" or "Eiffel Tower." The photo content recognition unit also analyzes the time of the photo and generates tags based on that time period. For example, a photo taken in the evening is tagged with tags such as "evening" or "sunset." The photo content recognition unit also analyzes background information of a photo and generates related tags. For example, a photo with a mountain background is tagged with tags such as "mountain" or "nature." In this way, by analyzing background information of a photo and generating tags based on that information, it becomes possible to search and classify photos related to the location and time of their capture.

[0050] The photo content recognition unit can recognize the brand or product name of an object in a photo and generate tags based on that. For example, the photo content recognition unit analyzes objects in a photo, recognizes their brand or product name, and generates tags. For example, a photo containing an Apple product is tagged with "Apple" or "iPhone." The photo content recognition unit also uses object recognition technology to identify product names in a photo and generate tags based on those product names. For example, a photo containing Nike sneakers is tagged with "Nike" or "Sneakers." The photo content recognition unit also analyzes brand logos in a photo and generates tags related to that brand. For example, a photo containing the Coca-Cola logo is tagged with "Coca-Cola" or "Drinks." This allows the system to recognize the brand or product name of objects in a photo and generate tags based on that, making it possible to search and classify photos related to brand or product names.

[0051] The photo content recognition unit can also generate similar tags for video data and attach tags to specific scenes. The photo content recognition unit, for example, analyzes video data and attaches tags to specific scenes. For example, a mountain scene in a video is tagged with tags such as "mountain" or "nature." The photo content recognition unit also analyzes the content of the video and generates appropriate tags for each scene. For example, a scene in the video in which a person is smiling is tagged with tags such as "smile" or "joy." The photo content recognition unit also analyzes specific scenes in the video and generates tags based on those scenes. For example, an ocean scene in the video is tagged with tags such as "ocean" or "wave." In this way, by generating similar tags for video data and attaching tags to specific scenes, videos can be searched and classified.

[0052] The search unit can use the emotion estimation function to estimate the emotion the user is feeling at the time of search and prioritize displaying photos that match that emotion. The search unit, for example, analyzes the emotion the user is feeling at the time of search and prioritizes displaying photos that match that emotion. For example, when the user is in a happy mood, photos tagged with "joy" or "fun" are displayed. The search unit also uses the emotion estimation function to analyze the user's emotion in real time and optimize search results based on that emotion. For example, when the user is in a sad mood, photos tagged with "sentimental" or "sadness" are displayed. The search unit also builds a system that prioritizes displaying photos that match the emotion based on the user's emotion data. For example, when the user is surprised, photos tagged with "surprise" or "surprise" are displayed. In this way, the user's search experience is improved by estimating the emotion the user is feeling at the time of search and prioritize displaying photos that match that emotion.

[0053] The search unit can analyze the search history and learn the user's search patterns to optimize future searches. For example, the search unit analyzes the user's search history and learns the search patterns to optimize future searches. For example, for a user who has searched for "travel" many times in the past, photos related to "travel" are preferentially displayed. The search unit also learns the user's search patterns based on the search history and builds a system that customizes future search results. For example, for a user who frequently searches for a specific tag, photos related to that tag are preferentially displayed. The search unit also analyzes the user's search history and learns the search patterns to make future searches more efficient. For example, related tags are automatically suggested based on the past search history. In this way, the search history is analyzed, the user's search patterns are learned, and future searches are optimized, improving the user's search experience.

[0054] The classification unit can use the emotion estimation function to classify photos based on the emotions of people appearing in the photos. The classification unit, for example, analyzes the emotions of people appearing in photos and classifies photos based on those emotions. For example, it classifies photos of smiling people into a "joy" folder. The classification unit also uses the emotion estimation function to analyze the emotions of people appearing in photos in real time and classifies photos based on those emotions. For example, it classifies photos of sad expressions into a "sadness" folder. The classification unit also builds a system that automatically classifies photos into folders related to emotions based on the emotion estimation results of the photos. For example, it classifies photos of surprised expressions into a "surprise" folder. In this way, by using the emotion estimation function to classify photos based on the emotions of people appearing in the photos, it becomes possible to organize photos related to emotions.

[0055] The classification unit can classify photos chronologically or geographically based on the date and time the photos were taken and the location where they were taken. For example, the classification unit analyzes the date and time the photos were taken and classifies the photos chronologically. For example, photos taken in a specific year or month are classified into respective folders. The classification unit also builds a system that classifies photos geographically based on the location where the photos were taken. For example, photos taken in a specific city or country are classified into respective folders. The classification unit also classifies photos chronologically and geographically by combining the date and time of the photos and the location. For example, photos taken in a specific year and location are classified. This makes it easier to organize photos by classifying them chronologically or geographically based on the date and time of the photos and the location where they were taken.

[0056] The sharing unit can use the emotion estimation function to preferentially suggest photos that will please the user at the sharing destination. For example, the sharing unit analyzes the emotions of the user at the sharing destination and preferentially suggests photos that will please the user. For example, when sharing family photos, photos tagged with "joy" or "fun" are suggested. The sharing unit also uses the emotion estimation function to analyze the emotions of the user at the sharing destination in real time and build a system that suggests photos based on those emotions. For example, when sharing photos of friends, photos tagged with "friendship" or "fun" are suggested. The sharing unit also preferentially suggests photos related to emotions based on the emotion data of the user at the sharing destination. For example, when sharing photos of a partner, photos tagged with "love" or "happiness" are suggested. In this way, the emotion estimation function is used to preferentially suggest photos that will please the user at the sharing destination, thereby improving the sharing experience.

[0057] The sharing unit can automatically generate comments and messages for photos when they are shared, improving the sharing experience. For example, the sharing unit uses AI to automatically generate comments and messages for photos to be shared. For example, adding a message such as "This photo is a wonderful memory!" The sharing unit also builds a system that automatically generates appropriate comments and messages based on the content of the photo. For example, adding a message such as "You can feel the family bond" to a family photo. The sharing unit also automatically generates comments and messages for photos when they are shared, improving the sharing experience. For example, adding a message such as "What wonderful travel memories!" to a travel photo. In this way, comments and messages are automatically generated for photos when they are shared, improving the sharing experience and increasing user satisfaction.

[0058] The sharing unit can analyze the sharing history and suggest an optimal sharing method based on past sharing patterns. The sharing unit, for example, analyzes the sharing history and suggests an optimal sharing method based on past sharing patterns. For example, the sharing unit makes suggestions based on tags of photos frequently shared with a specific user. The sharing unit also builds a system that suggests an optimal sharing method based on the sharing history. For example, the sharing unit makes suggestions the next time sharing is performed based on tags of photos previously shared with family. The sharing unit also analyzes the sharing history and suggests an optimal sharing method based on past sharing patterns. For example, the sharing unit makes suggestions based on tags of photos shared at a specific event. In this way, the sharing experience is improved by analyzing the sharing history and suggesting an optimal sharing method based on past sharing patterns.

[0059] The business operations department can use the emotion estimation function to evaluate the emotional value of work-related photos and prioritize the use of emotionally important photos. For example, the business operations department analyzes the emotional value of work-related photos and prioritizes the use of emotionally important photos. For example, photos of smiling customers are evaluated with tags such as "joy" and "satisfaction." The business operations department also uses the emotion estimation function to analyze the emotional value of work-related photos in real time and build a system that prioritizes the use of photos based on the emotions. For example, photos of moving moments of employees are evaluated with tags such as "emotion" and "inspiration." The business operations department also prioritizes the use of emotionally important photos based on the emotional data of work-related photos. For example, photos celebrating the success of a project are evaluated with tags such as "success" and "achievement." In this way, by using the emotion estimation function to evaluate the emotional value of work-related photos and prioritize the use of emotionally important photos, the efficiency and quality of work can be improved.

[0060] The business operations department can integrate the photo data with other business data to improve overall business efficiency. For example, the business operations department integrates the photo data with project management data to build a system that visually grasps the progress of a project. For example, photos of a construction site are linked to a project management tool. The business operations department also integrates the photo data with other business data to improve overall business efficiency. For example, in a product development process, photos of prototypes are linked to development data. The business operations department also integrates the photo data with business data to build a system that visually grasps areas for improvement in business processes. For example, photos of the manufacturing process are linked to quality control data. In this way, the photo data can be integrated with other business data to improve overall business efficiency, thereby improving business efficiency and quality.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The photo organization application may further include a voice recognition unit. The voice recognition unit analyzes voice data related to a photo and generates tags based on the content of the voice data. For example, if a voice memo related to a photo mentions "travel," the tag "travel" is generated. The voice recognition unit may also analyze ambient sounds at the time of photo capture and generate tags based on the sounds. For example, if the sound includes the sound of waves, tags such as "ocean" or "beach" are generated. The voice recognition unit may also analyze comments dictated by the user on the photo and generate tags based on the contents of the comments. This makes it possible to search and classify photos using voice data.

[0063] The photo organization application may further include a weather information acquisition unit. The weather information acquisition unit acquires weather information at the time based on the date, time, and location of a photo's capture and generates a tag based on the weather. For example, it generates tags such as "sunny" or "blue sky" for a photo taken on a sunny day. The weather information acquisition unit may also analyze the weather depicted in the background of a photo and generate a tag based on that weather. For example, it may generate tags such as "snow" or "winter" for a photo of a snowy landscape. Furthermore, the weather information acquisition unit may utilize weather forecast data to provide information useful for future photo planning. This makes it possible to search and classify photos using weather information.

[0064] The photo organization application may further include a facial recognition unit. The facial recognition unit analyzes the faces of people in a photo and generates tags based on the people. For example, for a photo that includes a specific family member, it may generate tags such as "family" or the name of the family member. The facial recognition unit may also analyze the age and gender of the people in the photo and generate tags based on that information. For example, for a photo of a child, it may generate tags such as "child" or "play." The facial recognition unit may also analyze the relationships between people in the photo and generate tags based on those relationships. This makes it possible to search and classify photos using facial recognition technology.

[0065] The photo organization application may further include an event recognition unit. The event recognition unit obtains information about events taking place at the time a photo was taken based on the date, time, and location of the photo, and generates tags based on the events. For example, if a specific concert or sporting event was taking place, tags such as "concert" or "sports" are generated. The event recognition unit can also analyze the content of a photo and generate event tags based on that content. For example, a photo of a birthday party may generate tags such as "birthday" or "party." The event recognition unit can also link with the user's calendar information and generate tags based on scheduled events. This makes it possible to search and classify photos using event information.

[0066] The photo organization application may further include a color analysis unit. The color analysis unit analyzes the color information of a photo and generates tags based on the color. For example, if the entire photo is primarily blue, tags such as "blue" or "ocean" may be generated. The color analysis unit may also analyze areas where a specific color is emphasized and generate tags based on that color. For example, a photo containing red flowers may generate tags such as "red" or "flowers." The color analysis unit may also analyze the color balance of a photo and generate tags based on that balance. This makes it possible to search and classify photos using color information.

[0067] Photo organizing applications can also estimate a user's emotions and optimize the display order of photos based on those emotions. For example, if a user is feeling stressed, photos of relaxing scenery can be prioritized for display. Similarly, if a user is in a happy mood, photos of smiling faces or photos of fun events can be prioritized for display. Furthermore, a system can be built that automatically suggests photos that match the user's emotions based on the user's emotional data. This can improve user satisfaction by optimizing the display order of photos based on the user's emotions.

[0068] Photo organizing applications can further estimate a user's emotions and suggest photo editing based on those emotions. For example, if a user is feeling sad, the application can suggest applying a bright filter. If a user is feeling happy, the application can suggest adding an effect to the photo. Furthermore, a system can be built that automatically suggests editing options that match the user's emotions based on the user's emotional data. This can improve user satisfaction by suggesting photo editing based on the user's emotions.

[0069] Photo organizing applications can also estimate a user's emotions and suggest photo sharing partners based on those emotions. For example, when a user is emotional, the application can suggest friends and family with whom they would like to share that emotion. Similarly, when a user is in a happy mood, the application can suggest friends and family with whom they would like to share that joy. Furthermore, a system can be built that automatically suggests sharing partners that match the user's emotions based on the user's emotional data. This can improve the sharing experience by suggesting photo sharing partners based on the user's emotions.

[0070] The photo organizing application can further estimate the user's emotions and suggest photo deletion based on those emotions. For example, when the user is feeling sad, it can suggest deleting photos that bring sad memories. On the other hand, when the user is feeling happy, it can suggest deleting unnecessary photos. Furthermore, it is possible to build a system that automatically suggests deletion options that match the user's emotions based on the user's emotion data. In this way, by suggesting photo deletion based on the user's emotions, it is possible to improve user satisfaction.

[0071] The photo organization application can further estimate the user's emotions and suggest photo backups based on those emotions. For example, when the user is emotional, it can suggest backing up photos that capture that emotion. Also, when the user is in a happy mood, it can suggest backing up photos that capture that joy. Furthermore, it is possible to build a system that automatically suggests backup options that match the user's emotions based on the user's emotional data. This can improve user satisfaction by suggesting photo backups based on the user's emotions.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The photo content recognition unit recognizes the content of the photo. For example, for a landscape photo, it generates tags such as "mountain," "ocean," and "sunset," and for a portrait photo, it generates tags such as "family," "friends," and "event." Step 2: The tag generation unit automatically generates appropriate tags based on the content recognized by the photo content recognition unit. For example, it uses generation AI to analyze the photo data and generate appropriate tags. Step 3: The search unit searches for photos using the tags generated by the tag generation unit. For example, by searching for the tag "mountain," photos of mountains are displayed in a list. Step 4: The classification unit classifies the photos using the tags generated by the tag generation unit. For example, photos tagged with "travel" are automatically classified into a "travel" folder, and photos tagged with "work" are automatically classified into a "work" folder. Step 5: The sharing unit shares the photo using the tag generated by the tag generation unit. For example, if a photo tagged with "family" is to be shared with family members, the photo can be accessed by all family members by specifying the tag and sharing it.

[0074] 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.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0087] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0088] 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.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] 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.

[0101] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

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

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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, in order to avoid confusion and to 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.

[0140] 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. [Explanation of symbols]

[0141] 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 photo content recognition unit that recognizes the content of a photo; a tag generation unit that automatically generates appropriate tags based on the content recognized by the photo content recognition unit; a search unit that searches for photos using the tags generated by the tag generation unit; a classification unit that classifies photos using the tags generated by the tag generation unit; a sharing unit for sharing photos using the tags generated by the tag generating unit; A system characterized by:

2. The photo content recognition unit Estimate the emotions of people in photos and generate tags based on those emotions 2. The system of claim 1.

3. The photo content recognition unit Similar tag generation is performed on video data, and specific scenes are tagged.

2. The system of claim 1.

4. The search unit Analyzes search history and learns user search patterns to optimize future searches 2. The system of claim 1.

5. The classification unit Categorize photos chronologically and geographically based on when and where they were taken 2. The system of claim 1.

6. The common part is Prioritize suggestions for photos that users will enjoy the most 2. The system of claim 1.

7. The Business Operations Department Evaluate the emotional value of work-related photos and prioritize the most emotionally significant ones 2. The system of claim 1.

8. The photo content recognition unit Analyzes the background information of a photo and generates tags based on it 2. The system of claim 1.

Images