System

Abstract

An object of a system according to an embodiment is to enable a user to efficiently select an unnecessary photo and easily delete the photo.SOLUTION: A system includes a photograph analysis unit, an unnecessary photograph selection unit, a proposal unit, and a deletion unit. The photograph analysis unit analyzes the content of the photograph. The unnecessary-photograph selection unit selects unnecessary photographs from the photographs analyzed by the photograph analysis unit. The proposal unit proposes the unnecessary photograph selected by the unnecessary photograph selection unit to the user. The deletion unit deletes the unnecessary photograph proposed by the proposal 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] With conventional technology, when a smartphone's storage capacity becomes full, the task of sorting and deleting unnecessary photos can be time-consuming and difficult.

[0005] The system according to the embodiment aims to enable the user to efficiently select unnecessary photos and easily delete them. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo analysis unit, an unnecessary photo selection unit, a suggestion unit, and a deletion unit. The photo analysis unit analyzes the content of photos. The unnecessary photo selection unit selects unnecessary photos from the photos analyzed by the photo analysis unit. The suggestion unit suggests the unnecessary photos selected by the unnecessary photo selection unit to a user. The deletion unit deletes the unnecessary photos suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently sort out unnecessary photos and allow users to easily delete 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) The photo organization AI according to an embodiment of the present invention is a system that uses AI to automatically select unnecessary photos and allows users to easily delete them in order to solve the problem of smartphones' photo storage becoming full. This allows users to easily delete unnecessary photos and free up space on their smartphones.

[0029] A photo organizing AI according to an embodiment includes a photo analysis unit, an unnecessary photo selection unit, a suggestion unit, and a deletion unit. The photo analysis unit analyzes the content of photos. For example, the generation AI analyzes photos stored on a smartphone and understands the content of each photo. The generation AI identifies blurry photos, duplicate photos, and clearly unnecessary screenshots. The generation AI analyzes the content of photos based on prompts containing instructions from the user about what the generation AI wants the generation AI to do. The unnecessary photo selection unit selects unnecessary photos from among the photos analyzed by the photo analysis unit. For example, if the same scene is photographed multiple times, the generation AI keeps the clearest one and determines the others as unnecessary. The generation AI also determines blurry, too dark, or too bright photos as unnecessary. The suggestion unit suggests unnecessary photos selected by the unnecessary photo selection unit to the user. For example, the generation AI suggests the unnecessary photos it has picked out to the user, and the user may ask, "This photo is blurry. Do you want to delete it?" The photo is deleted when the user answers "Yes." The deletion unit deletes the unnecessary photos suggested by the suggestion unit. For example, when a user deletes unnecessary photos, the smartphone's storage capacity is secured. As a result, the photo organizing AI according to the embodiment allows the user to easily delete unnecessary photos and secure the smartphone's storage capacity. For example, even if a user takes a large number of photos while traveling, the generation AI automatically picks out and suggests unnecessary photos, allowing the user to organize photos without hassle.

[0030] The photo analysis unit analyzes background information and can prioritize photos related to specific events or locations. For example, the generation AI analyzes the location where a photo was taken from GPS data and prioritizes photos taken at specific tourist spots or event venues. For example, it identifies photos taken at famous places at travel destinations. This allows photos related to specific events or locations to be prioritized.

[0031] The photo analysis unit analyzes metadata and can identify technically superior photos. For example, the generative AI analyzes the resolution and exposure settings of photos to identify technically superior photos. For example, photos with high resolution and appropriate exposure settings are prioritized. This allows technically superior photos to be identified.

[0032] The photo analysis unit can analyze the content of the video and identify unnecessary video clips. For example, the generation AI analyzes the content of each frame of the video and identifies blurry or duplicated scenes. For example, it determines that video clips with the same scene repeated are unnecessary. This makes it possible to identify unnecessary video clips.

[0033] The photo analysis unit can analyze audio data and identify unnecessary voice memos and recordings. For example, the photo analysis unit uses a generation AI to analyze the content of the audio data and identify parts with silence or a lot of noise. For example, a voice memo with a long period of silence during recording can be deemed unnecessary. This makes it possible to identify unnecessary voice memos and recordings.

[0034] The suggestion unit learns past selection history and can make more personalized suggestions. For example, the suggestion unit's generation AI learns the user's past photo deletion history and preferentially suggests photos with similar characteristics. For example, it identifies unnecessary photos based on the characteristics of photos deleted in the past. This allows for more personalized suggestions.

[0035] The suggestion unit can collect real-time feedback and continuously improve the accuracy of suggestions. For example, the suggestion unit uses a generation AI to collect real-time feedback from users and improve the accuracy of suggestions based on that data. For example, the suggestion unit learns the characteristics of photos that users have rated as "unnecessary" and prevents similar photos from being suggested. This allows the accuracy of suggestions to be continuously improved.

[0036] The suggestion unit can not only delete unnecessary photos, but also suggest organizing photos and creating albums. For example, the generation AI can suggest to the user not only deleting unnecessary photos, but also organizing photos and creating albums. For example, albums can be automatically generated based on specific events or themes. This allows the suggestion unit to suggest organizing photos and creating albums.

[0037] The suggestion unit can not only delete unnecessary photos but also suggest backing up photos to cloud storage. For example, the generation AI can suggest to the user not only deleting unnecessary photos but also backing up photos to cloud storage. For example, important photos can be automatically backed up to the cloud. This makes it possible to suggest backing up photos to cloud storage.

[0038] The deletion unit can analyze metadata and identify technically superior photos. For example, the generative AI can analyze the resolution and exposure settings of photos to identify technically superior photos. For example, photos with high resolution and appropriate exposure settings can be preferentially retained. This allows technically superior photos to be identified.

[0039] The deletion unit can analyze the content of the video and identify unnecessary video clips. For example, the deletion unit uses a generation AI to analyze the content of each frame of the video and identify blurry or duplicated scenes. For example, it determines that video clips with the same scene repeated are unnecessary. This allows the deletion unit to identify unnecessary video clips.

[0040] The deletion unit can analyze the audio data and identify unnecessary voice memos and recordings. For example, the deletion unit uses a generation AI to analyze the content of the audio data and identify parts with silence or a lot of noise. For example, a voice memo with a long period of silence during recording can be determined to be unnecessary. This makes it possible to identify unnecessary voice memos and recordings.

[0041] The photo analysis unit analyzes the user's past selection history and can make more personalized suggestions. For example, the generation AI in the photo analysis unit learns the user's past photo deletion history and prioritizes suggestions of photos with similar characteristics. For example, it identifies unnecessary photos based on the characteristics of photos deleted in the past. This allows for more personalized suggestions.

[0042] The photo analysis unit can collect real-time feedback and continuously improve the accuracy of suggestions. For example, the generative AI can collect real-time feedback from users and improve the accuracy of suggestions based on that data. For example, it can learn the characteristics of photos that users have rated as "unnecessary" and avoid suggesting similar photos. This allows the accuracy of suggestions to be continuously improved.

[0043] The photo analysis unit analyzes the user's social media activity and can make more personalized suggestions. For example, the generative AI in the photo analysis unit analyzes the content of the user's social media posts and learns the user's preferences. For example, it analyzes the themes and tags that the user frequently posts. This allows the analysis of social media activity and makes more personalized suggestions.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The photo analysis unit analyzes the types of objects in a photo and prioritizes photos that feature specific objects. For example, the generation AI detects objects such as pets, cars, and buildings in a photo and prioritizes photos that feature objects that the user is particularly interested in. This allows photos that feature objects that the user finds important to be prioritized.

[0046] The photo analysis unit analyzes the color information of a photo and can prioritize photos with a specific color tone or atmosphere. For example, the generation AI analyzes the color information of a photo and prioritizes photos with a specific color tone, such as warm-colored photos or monochrome photos. This allows photos with a color tone that matches the user's preferences to be prioritized.

[0047] The photo analysis section analyzes the composition of a photo and prioritizes photos with a balanced composition. For example, the generative AI analyzes the composition of a photo and prioritizes photos with a balanced composition based on the rule of thirds or the golden ratio. This allows visually beautiful photos to be prioritized.

[0048] The photo analysis unit analyzes the time the photo was taken and can prioritize photos taken during specific times of the day. For example, the generation AI can analyze the time the photo was taken and prioritize photos taken during specific times of the day, such as sunrise or sunset. This allows photos taken during specific times of the day to be prioritized.

[0049] The photo analysis unit analyzes the device used to capture a photo and prioritizes photos taken with a specific device. For example, the generative AI analyzes the metadata of a photo and prioritizes photos taken with a high-performance camera. This allows photos taken with a technologically superior device to be prioritized.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The photo analysis unit analyzes the content of the photo. For example, the generation AI analyzes photos on a smartphone and understands the content of each photo. The generation AI identifies blurry photos, duplicate photos, and obviously unnecessary screenshots. The generation AI analyzes the content of the photo based on prompts from the user, which include instructions on what the generation AI wants the user to do. Step 2: The unnecessary photo selection unit selects unnecessary photos from those analyzed by the photo analysis unit. For example, if the same scene is photographed multiple times, the generation AI will keep the clearest one and determine the others as unnecessary. The generation AI will also determine that blurry, too dark, or too bright photos are unnecessary. Step 3: The suggestion unit suggests unnecessary photos selected by the unnecessary photo selection unit to the user. For example, the generation AI suggests the unnecessary photos it has picked out to the user, asking, "This photo is blurry. Do you want to delete it?" If the user answers "Yes," the photo is deleted. Step 4: The deletion unit deletes the unnecessary photos suggested by the suggestion unit. For example, the user deletes unnecessary photos, thereby freeing up space on the smartphone.

[0052] (Example 2) The photo organization AI according to an embodiment of the present invention is a system that uses AI to automatically select unnecessary photos and allows users to easily delete them in order to solve the problem of smartphones' photo storage becoming full. This allows users to easily delete unnecessary photos and free up space on their smartphones.

[0053] A photo organizing AI according to an embodiment includes a photo analysis unit, an unnecessary photo selection unit, a suggestion unit, and a deletion unit. The photo analysis unit analyzes the content of photos. For example, the generation AI analyzes photos stored on a smartphone and understands the content of each photo. The generation AI identifies blurry photos, duplicate photos, and clearly unnecessary screenshots. The generation AI analyzes the content of photos based on prompts containing instructions from the user about what the generation AI wants the generation AI to do. The unnecessary photo selection unit selects unnecessary photos from among the photos analyzed by the photo analysis unit. For example, if the same scene is photographed multiple times, the generation AI keeps the clearest one and determines the others as unnecessary. The generation AI also determines blurry, too dark, or too bright photos as unnecessary. The suggestion unit suggests unnecessary photos selected by the unnecessary photo selection unit to the user. For example, the generation AI suggests the unnecessary photos it has picked out to the user, and the user may ask, "This photo is blurry. Do you want to delete it?" The photo is deleted when the user answers "Yes." The deletion unit deletes the unnecessary photos suggested by the suggestion unit. For example, when a user deletes unnecessary photos, the smartphone's storage capacity is secured. As a result, the photo organizing AI according to the embodiment allows the user to easily delete unnecessary photos and secure the smartphone's storage capacity. For example, even if a user takes a large number of photos while traveling, the generation AI automatically picks out and suggests unnecessary photos, allowing the user to organize photos without hassle.

[0054] The photo analysis unit can estimate the facial expressions and emotions of people in photos and identify emotionally significant photos. For example, the photo analysis unit uses generative AI to detect the faces of people in photos and estimate their emotions using facial expression recognition technology. For example, it analyzes emotions such as smiles and tears to identify emotionally significant photos. This makes it possible to identify emotionally significant photos.

[0055] The photo analysis unit analyzes background information and can prioritize photos related to specific events or locations. For example, the generation AI analyzes the location where a photo was taken from GPS data and prioritizes photos taken at specific tourist spots or event venues. For example, it identifies photos taken at famous places at travel destinations. This allows photos related to specific events or locations to be prioritized.

[0056] The photo analysis unit analyzes metadata and can identify technically superior photos. For example, the generative AI analyzes the resolution and exposure settings of photos to identify technically superior photos. For example, photos with high resolution and appropriate exposure settings are prioritized. This allows technically superior photos to be identified.

[0057] The photo analysis unit can analyze the content of the video and identify unnecessary video clips. For example, the generation AI analyzes the content of each frame of the video and identifies blurry or duplicated scenes. For example, it determines that video clips with the same scene repeated are unnecessary. This makes it possible to identify unnecessary video clips.

[0058] The photo analysis unit can analyze audio data and identify unnecessary voice memos and recordings. For example, the photo analysis unit uses a generation AI to analyze the content of the audio data and identify parts with silence or a lot of noise. For example, a voice memo with a long period of silence during recording can be deemed unnecessary. This makes it possible to identify unnecessary voice memos and recordings.

[0059] The photo analysis unit uses the emotion estimation function to analyze the emotions the user had when taking a photo, and can prioritize photos with positive emotions. For example, the photo analysis unit uses a generation AI to analyze the user's facial expressions when taking a photo, and prioritizes photos with many smiling or happy expressions. For example, it identifies photos with many smiling faces. This allows photos with positive emotions to be prioritized.

[0060] The suggestion unit learns past selection history and can make more personalized suggestions. For example, the suggestion unit's generation AI learns the user's past photo deletion history and preferentially suggests photos with similar characteristics. For example, it identifies unnecessary photos based on the characteristics of photos deleted in the past. This allows for more personalized suggestions.

[0061] The suggestion unit can collect real-time feedback and continuously improve the accuracy of suggestions. For example, the suggestion unit uses a generation AI to collect real-time feedback from users and improve the accuracy of suggestions based on that data. For example, the suggestion unit learns the characteristics of photos that users have rated as "unnecessary" and prevents similar photos from being suggested. This allows the accuracy of suggestions to be continuously improved.

[0062] The suggestion unit can not only delete unnecessary photos, but also suggest organizing photos and creating albums. For example, the generation AI can suggest to the user not only deleting unnecessary photos, but also organizing photos and creating albums. For example, albums can be automatically generated based on specific events or themes. This allows the suggestion unit to suggest organizing photos and creating albums.

[0063] The suggestion unit can not only delete unnecessary photos but also suggest backing up photos to cloud storage. For example, the generation AI can suggest to the user not only deleting unnecessary photos but also backing up photos to cloud storage. For example, important photos can be automatically backed up to the cloud. This makes it possible to suggest backing up photos to cloud storage.

[0064] The suggestion unit uses the emotion estimation function to estimate the timing when the user is likely to accept a suggestion and can make the suggestion at the optimal timing. For example, the suggestion unit uses the emotion estimation function of the generation AI to estimate the timing when the user is likely to accept a suggestion and can make the suggestion at the optimal timing. For example, the suggestion is made when the user is relaxed. This allows the suggestion to be made at a timing when the user is likely to accept the suggestion.

[0065] The deletion unit can evaluate the emotional value and prioritize the retention of emotionally significant photos. For example, the generative AI evaluates the emotional value of photos and prioritizes the retention of photos with a high emotional score. For example, it identifies photos that strongly express emotions such as smiles or tears. This allows the retention of emotionally significant photos.

[0066] The deletion unit can analyze metadata and identify technically superior photos. For example, the generative AI can analyze the resolution and exposure settings of photos to identify technically superior photos. For example, photos with high resolution and appropriate exposure settings can be preferentially retained. This allows technically superior photos to be identified.

[0067] The deletion unit can analyze the content of the video and identify unnecessary video clips. For example, the deletion unit uses a generation AI to analyze the content of each frame of the video and identify blurry or duplicated scenes. For example, it determines that video clips with the same scene repeated are unnecessary. This allows the deletion unit to identify unnecessary video clips.

[0068] The deletion unit can analyze the audio data and identify unnecessary voice memos and recordings. For example, the deletion unit uses a generation AI to analyze the content of the audio data and identify parts with silence or a lot of noise. For example, a voice memo with a long period of silence during recording can be determined to be unnecessary. This makes it possible to identify unnecessary voice memos and recordings.

[0069] The deletion unit uses the emotion estimation function to analyze the emotions of the user when organizing photos, and can prioritize photos with positive emotions. For example, the deletion unit uses a generation AI to analyze the user's facial expressions when organizing photos, and prioritizes photos with many smiling or happy expressions. For example, it identifies photos with many smiling faces. This allows photos with positive emotions to be prioritized.

[0070] The photo analysis unit analyzes the user's emotional response and prioritizes the preservation of emotionally significant photos. For example, the generation AI analyzes the user's emotional response and prioritizes the preservation of emotionally significant photos. For example, it identifies photos that strongly express emotions such as smiles or tears. This allows the preservation of emotionally significant photos.

[0071] The photo analysis unit analyzes the user's past selection history and can make more personalized suggestions. For example, the generation AI in the photo analysis unit learns the user's past photo deletion history and prioritizes suggestions of photos with similar characteristics. For example, it identifies unnecessary photos based on the characteristics of photos deleted in the past. This allows for more personalized suggestions.

[0072] The photo analysis unit can collect real-time feedback and continuously improve the accuracy of suggestions. For example, the generative AI can collect real-time feedback from users and improve the accuracy of suggestions based on that data. For example, it can learn the characteristics of photos that users have rated as "unnecessary" and avoid suggesting similar photos. This allows the accuracy of suggestions to be continuously improved.

[0073] The photo analysis unit analyzes the user's social media activity and can make more personalized suggestions. For example, the generative AI in the photo analysis unit analyzes the content of the user's social media posts and learns the user's preferences. For example, it analyzes the themes and tags that the user frequently posts. This allows the analysis of social media activity and makes more personalized suggestions.

[0074] The photo analysis unit uses an emotion estimation function to analyze the user's emotional response and prioritize leaving emotionally significant photos. For example, the photo analysis unit uses a generation AI to analyze the user's emotional response and prioritize leaving emotionally significant photos. For example, it identifies photos that strongly express emotions such as smiles or tears. This allows emotionally significant photos to be prioritized.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The photo analysis unit analyzes the types of objects in a photo and prioritizes photos that feature specific objects. For example, the generation AI detects objects such as pets, cars, and buildings in a photo and prioritizes photos that feature objects that the user is particularly interested in. This allows photos that feature objects that the user finds important to be prioritized.

[0077] The photo analysis unit analyzes the color information of a photo and can prioritize photos with a specific color tone or atmosphere. For example, the generation AI analyzes the color information of a photo and prioritizes photos with a specific color tone, such as warm-colored photos or monochrome photos. This allows photos with a color tone that matches the user's preferences to be prioritized.

[0078] The photo analysis section analyzes the composition of a photo and prioritizes photos with a balanced composition. For example, the generative AI analyzes the composition of a photo and prioritizes photos with a balanced composition based on the rule of thirds or the golden ratio. This allows visually beautiful photos to be prioritized.

[0079] The photo analysis unit analyzes the time the photo was taken and can prioritize photos taken during specific times of the day. For example, the generation AI can analyze the time the photo was taken and prioritize photos taken during specific times of the day, such as sunrise or sunset. This allows photos taken during specific times of the day to be prioritized.

[0080] The photo analysis unit analyzes the device used to capture a photo and prioritizes photos taken with a specific device. For example, the generative AI analyzes the metadata of a photo and prioritizes photos taken with a high-performance camera. This allows photos taken with a technologically superior device to be prioritized.

[0081] The suggestion unit can estimate the user's emotions and refrain from making suggestions when the user is feeling stressed. For example, the generation AI can infer from the user's facial expressions and voice that they are feeling stressed and refrain from making suggestions in that case. This allows suggestions to be made at an appropriate time according to the user's emotional state.

[0082] The suggestion unit can estimate the user's emotions and suggest emotionally positive photos for an album. For example, the generation AI can analyze the user's emotions and suggest an album containing photos with many smiling or happy expressions. This makes it possible to create a positive album based on the user's emotions.

[0083] The suggestion unit can estimate the user's emotions and make suggestions to delete emotionally negative photos. For example, the generative AI can analyze the user's emotions and make suggestions to delete photos with many sad or angry expressions. This makes it possible to make suggestions to delete unnecessary photos based on the user's emotions.

[0084] The suggestion unit can estimate the user's emotions and make suggestions to back up emotionally significant photos to cloud storage. For example, the generative AI can analyze the user's emotions and make suggestions to back up emotionally significant photos to the cloud, allowing for safe storage of emotionally significant photos.

[0085] The suggestion unit can estimate the user's emotions and make suggestions to display emotionally positive photos on the digital photo frame. For example, the generative AI can analyze the user's emotions and make suggestions to display photos with many smiling or happy expressions on the digital photo frame. This allows the user to enjoy positive photos based on their emotions on a daily basis.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The photo analysis unit analyzes the content of the photo. For example, the generation AI analyzes photos on a smartphone and understands the content of each photo. The generation AI identifies blurry photos, duplicate photos, and obviously unnecessary screenshots. The generation AI analyzes the content of the photo based on prompts from the user, which include instructions on what the generation AI wants the user to do. Step 2: The unnecessary photo selection unit selects unnecessary photos from those analyzed by the photo analysis unit. For example, if the same scene is photographed multiple times, the generation AI will keep the clearest one and determine the others as unnecessary. The generation AI will also determine that blurry, too dark, or too bright photos are unnecessary. Step 3: The suggestion unit suggests unnecessary photos selected by the unnecessary photo selection unit to the user. For example, the generation AI suggests the unnecessary photos it has picked out to the user, asking, "This photo is blurry. Do you want to delete it?" If the user answers "Yes," the photo is deleted. Step 4: The deletion unit deletes the unnecessary photos suggested by the suggestion unit. For example, the user deletes unnecessary photos, thereby freeing up space on the smartphone.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[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 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.

[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. 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.

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

[0116] 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.

[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 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.

[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 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

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

[0132] 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.

[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 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.

[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 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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 analysis unit that analyzes the content of the photo; an unnecessary photo selection unit that selects unnecessary photos from the photos analyzed by the photo analysis unit; a suggestion unit that suggests unnecessary photos selected by the unnecessary photo selection unit to a user; a deletion unit that deletes unnecessary photos suggested by the suggestion unit. A system characterized by:

2. The photo analysis unit Estimate facial expressions and emotions of people in photos and identify emotionally significant photos 2. The system of claim 1.

3. The photo analysis unit Analyze video content and identify unwanted video clips 2. The system of claim 1.

4. a photo analysis unit that analyzes the content of the photo; an unnecessary photo selection unit that selects unnecessary photos from the photos analyzed by the photo analysis unit; a suggestion unit that suggests unnecessary photos selected by the unnecessary photo selection unit to a user; a deletion unit that deletes unnecessary photos suggested by the suggestion unit. A system characterized by:

5. The deletion unit Evaluate emotional value and prioritize photos that are emotionally significant 2. The system of claim 1.

6. The photo analysis unit Analyze the user's emotional response and prioritize photos that are emotionally significant.

2. The system of claim 1.

7. The photo analysis unit Analyze the emotions of the user when they took the photo and prioritize photos with positive emotions.

2. The system of claim 1.

8. The proposal unit The timing when the user is likely to accept the proposal is estimated, and the proposal is made at the optimal timing.

2. The system of claim 1.

Images