System

Abstract

An object of a system according to an embodiment is to generate music based on an element of a photograph taken by a user and deepen an impression at a travel destination.SOLUTION: A system includes a photograph input unit, an analysis unit, and a music generation unit. The photograph input unit receives a photograph taken by a user. The analysis unit analyzes the photograph received by the photograph input unit and extracts at least one element of a landscape, a scene, a color, and a composition. The music generation unit generates music based on the element extracted by the analysis 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 the problem that it is not possible to generate music based on the scenery or scenes in photographs, limiting the means of deepening impressions of travel destinations.

[0005] The system according to the embodiment aims to create music based on elements of photos taken by a user, thereby deepening the impression of the travel destination. [Means for solving the problem]

[0006] A system according to an embodiment includes a photo input unit, an analysis unit, and a music generation unit. The photo input unit accepts a photo taken by a user. The analysis unit analyzes the photo accepted by the photo input unit and extracts at least one element from among landscape, scenery, color, and composition. The music generation unit generates music based on the element extracted by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment generates music based on elements of photos taken by the user, thereby enhancing the impression of the travel destination. [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 music generation service according to the embodiment of the present invention is a service that inputs photos taken by a user into AI and generates music based on the scenery and scenes. This allows the music generation service to deepen the impression of the user's travel destination and leave it as a stronger memory.

[0029] A music generation service according to an embodiment includes a photo input unit, an analysis unit, and a music generation unit. The photo input unit accepts photos taken by a user. For example, photos taken with a smartphone or digital camera can be uploaded. The photo input unit can also acquire photos from cloud storage. For example, a user can select and upload photos stored in Google Photos or iCloud. The analysis unit analyzes the photos accepted by the photo input unit and extracts at least one element of landscape, scene, color, or composition. For example, the generation AI recognizes mountain, ocean, or cityscapes in the photos. The generation AI can also analyze the color and composition of the photos and extract specific patterns. For example, the generation AI analyzes the proportion of blue in the photos and the composition based on the rule of thirds. The music generation unit generates music based on the elements extracted by the analysis unit. For example, the generation AI can generate grand music for a mountain scene. For example, the generation AI can generate relaxing music including the sound of waves for a seascape. For example, the generation AI can draw inspiration from the color and composition of the photos to adjust the tempo and atmosphere of the music. As a result, the music generation service according to the embodiment can deepen the user's impression of the travel destination and leave a stronger memory. For example, by listening to the generated music, the user can more vividly recall the experience at the travel destination. Furthermore, by sharing the generated music with friends and family, the user can share common memories.

[0030] The analysis unit can incorporate the metadata of a photo into the analysis to understand the landscape or scene in more detail. For example, the analysis unit can incorporate the metadata of a photo into the analysis to understand the details of the landscape or scene based on the date and time of the photo and location information. For example, if the photo was taken in the evening, it will be analyzed as a sunset scene. The analysis unit can also analyze the landscape of a specific location based on the location information of the photo. For example, if the location information is a beach, it will be analyzed as a seascape. The analysis unit can also analyze the color and brightness of the photo based on the camera settings (e.g., exposure and white balance). For example, if the exposure is high, it will be analyzed as a bright landscape. In this way, by incorporating the metadata of a photo into the analysis, it is possible to understand the landscape or scene in more detail.

[0031] The analysis unit can add voice memos and text memos to the photo analysis and reflect the user's intentions and impressions in the analysis. For example, when analyzing a photo, the analysis unit analyzes voice memos added by the user and reflects their contents. For example, if the user says "beautiful sunset," the photo is analyzed as a sunset landscape. The analysis unit can also analyze text memos added by the user and reflect their contents when analyzing a photo. For example, if the user writes "fun trip," the analysis unit generates a positive analysis result. The analysis unit can also develop algorithms to analyze the user's intentions and impressions based on the voice memos and text memos. For example, the generation AI can use voice recognition technology to convert voice memos into text and analyze its content. By incorporating voice memos and text memos into the analysis, music can be generated that more accurately reflects the user's intentions and impressions.

[0032] The analysis unit can compare the analysis results of a photo with photos of the same location taken by other users to extract similarities and differences. For example, the analysis unit can compare the analysis results of a photo with photos of the same location taken by other users to extract similarities. For example, if the photos are of the same mountain scenery, the analysis results reflect the common features. The analysis unit can also compare the analysis results of a photo with photos of the same location taken by other users to extract differences. For example, if the photos were taken in different seasons, the analysis results reflect the seasonal differences. The analysis unit can also develop algorithms to adjust the analysis results of a photo based on similarities and differences. For example, the generative AI can adjust the analysis results based on color matches and differences in composition. This allows for comparison with photos of the same location taken by other users to extract similarities and differences, enabling more detailed analysis.

[0033] The analysis unit can improve the accuracy of analysis by referring to past analysis results of similar photos when analyzing a photo. For example, the analysis unit can improve the accuracy of analysis by referring to past analysis results of similar photos when analyzing a photo. For example, the analysis results can be adjusted based on past photos taken at the same location. The analysis unit can also improve the accuracy of analysis of a photo based on past analysis results of similar photos. For example, past analysis results can be stored in a database and referenced when analyzing a new photo. The analysis unit can also develop an algorithm to improve the accuracy of analysis based on past analysis results of similar photos. For example, the generative AI can use a machine learning model to learn past analysis results and apply them to the analysis of a new photo. In this way, the analysis accuracy can be improved by referring to past analysis results of similar photos.

[0034] When analyzing the content of a photo, the analysis unit can perform a more detailed analysis based on environmental factors such as the season or weather. For example, when analyzing the content of a photo, the analysis unit can take the season into consideration and perform a more detailed analysis. For example, if the photo is a spring landscape, elements such as flowers and fresh greenery can be reflected in the analysis results. The analysis unit can also perform a more detailed analysis by taking the weather into consideration when analyzing the content of a photo. For example, if the photo is a landscape taken on a rainy day, elements such as rain can be reflected in the analysis results. The analysis unit can also develop an algorithm for analyzing the content of a photo based on environmental factors such as the season and weather. For example, the generative AI can learn the characteristics of each season and the effects of weather and reflect these in the analysis results. This enables a more detailed analysis by taking environmental factors such as the season and weather into consideration.

[0035] The analysis unit can integrate the results of photo analysis with other media to perform a more multifaceted analysis. For example, the analysis unit can integrate the results of photo analysis with video to perform a more multifaceted analysis. For example, the analysis results can reflect the movement of a landscape based on videos taken at the same location. The analysis unit can also integrate the results of photo analysis with audio to perform a more multifaceted analysis. For example, the analysis results can reflect the atmosphere of a landscape based on local audio. The analysis unit can also develop algorithms for integration with other media. For example, the generative AI can integrate the results of photo analysis with video analysis technology and audio analysis technology. This makes it possible to perform a more multifaceted analysis by integrating with other media.

[0036] The analysis unit can reanalyze the results of photo analysis from the perspective of a different culture or region to gain different inspiration. For example, the analysis unit can reanalyze the results of photo analysis from the perspective of a different culture to gain different inspiration. For example, analyzing a Japanese landscape from a Western perspective to discover new elements. The analysis unit can also reanalyze the results of photo analysis from the perspective of a different region to gain different inspiration. For example, analyzing an Asian landscape from a European perspective to discover new elements. The analysis unit can also develop algorithms to reanalyze the results of photo analysis based on the perspectives of different cultures and regions. For example, the generative AI can learn cultural backgrounds and local customs and reflect them in the analysis results. This allows different inspiration to be gained by reanalyzing from the perspectives of different cultures and regions.

[0037] The music generation unit can emphasize specific instruments and tones in the music generated based on the results of photo analysis. For example, the generation AI can emphasize specific instruments in the music generated based on the results of photo analysis. For example, music that emphasizes string instruments is generated for mountain landscapes. The music generation unit can also emphasize specific tones. For example, music that emphasizes the sound of waves is generated for seascapes. The music generation unit can also develop algorithms for generating music based on specific instruments and tones. For example, the generation AI selects instruments and adjusts tones. In this way, more impressive music can be generated by emphasizing specific instruments and tones in the music generated based on the results of photo analysis.

[0038] The music generation unit can reflect the user's past musical preferences in the music generated based on the photo analysis results. For example, the music generation unit reflects the user's past musical preferences in the music generated by the generation AI based on the photo analysis results. For example, it generates music in a genre that the user prefers. The music generation unit can also adjust musical elements based on the user's past musical preferences. For example, it can reflect the user's preferred tempo and melody. The music generation unit can also develop algorithms for generating music based on the user's past musical preferences. For example, the generation AI can learn past playback history and user ratings and reflect them in the music. In this way, more personalized music can be generated by reflecting the user's past musical preferences in the music generated based on the photo analysis results.

[0039] The music generation unit can incorporate natural sounds and environmental sounds into the music generated based on the results of photo analysis. For example, the generation AI incorporates natural sounds into the music generated based on the results of photo analysis. For example, music that incorporates birdsong is generated for a mountain landscape. The music generation unit can also incorporate environmental sounds. For example, music that incorporates the sound of waves is generated for an ocean landscape. The music generation unit can also develop algorithms for generating music based on natural sounds and environmental sounds. For example, the generation AI selects and incorporates natural sounds and environmental sounds. In this way, by incorporating natural sounds and environmental sounds into the music generated based on the results of photo analysis, more realistic music can be generated.

[0040] The music generation unit can combine elements of different musical genres into the music generated based on the results of photo analysis. For example, the generative AI can combine elements of different musical genres into the music generated based on the results of photo analysis. For example, it can generate music that combines classical and electronica for mountain scenery. The music generation unit can also combine elements of different musical genres. For example, it can generate music that combines jazz and ambient for seascapes. The music generation unit can also develop algorithms for generating music based on elements of different musical genres. For example, the generative AI can select genres and combine elements. This allows for a wider variety of music to be generated by combining elements of different musical genres into the music generated based on the results of photo analysis.

[0041] The music generation unit can emphasize specific instruments and tones in the music generated based on the results of photo analysis. For example, the generation AI can emphasize specific instruments in the music generated based on the results of photo analysis. For example, music that emphasizes string instruments is generated for mountain landscapes. The music generation unit can also emphasize specific tones. For example, music that emphasizes the sound of waves is generated for seascapes. The music generation unit can also develop algorithms for generating music based on specific instruments and tones. For example, the generation AI selects instruments and adjusts tones. In this way, more impressive music can be generated by emphasizing specific instruments and tones in the music generated based on the results of photo analysis.

[0042] The music generation unit can reflect the user's past musical preferences in the music generated based on the photo analysis results. For example, the music generation unit reflects the user's past musical preferences in the music generated by the generation AI based on the photo analysis results. For example, it generates music in a genre that the user prefers. The music generation unit can also adjust musical elements based on the user's past musical preferences. For example, it can reflect the user's preferred tempo and melody. The music generation unit can also develop algorithms for generating music based on the user's past musical preferences. For example, the generation AI can learn past playback history and user ratings and reflect them in the music. In this way, more personalized music can be generated by reflecting the user's past musical preferences in the music generated based on the photo analysis results.

[0043] The music generation unit can incorporate natural sounds and environmental sounds into the music generated based on the results of photo analysis. For example, the generation AI incorporates natural sounds into the music generated based on the results of photo analysis. For example, music that incorporates birdsong is generated for a mountain landscape. The music generation unit can also incorporate environmental sounds. For example, music that incorporates the sound of waves is generated for an ocean landscape. The music generation unit can also develop algorithms for generating music based on natural sounds and environmental sounds. For example, the generation AI selects and incorporates natural sounds and environmental sounds. In this way, by incorporating natural sounds and environmental sounds into the music generated based on the results of photo analysis, more realistic music can be generated.

[0044] The music generation unit can combine elements of different musical genres into the music generated based on the results of photo analysis. For example, the generative AI can combine elements of different musical genres into the music generated based on the results of photo analysis. For example, it can generate music that combines classical and electronica for mountain scenery. The music generation unit can also combine elements of different musical genres. For example, it can generate music that combines jazz and ambient for seascapes. The music generation unit can also develop algorithms for generating music based on elements of different musical genres. For example, the generative AI can select genres and combine elements. This allows for a wider variety of music to be generated by combining elements of different musical genres into the music generated based on the results of photo analysis.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] When analyzing photos, the analysis unit can refer to the user's past travel history and reflect it in the analysis results. For example, by analyzing photos of places the user has visited in the past and learning the characteristics of those places, the analysis results for the current photo can be adjusted with higher accuracy. The analysis unit can also identify the user's preferred landscapes and scenes based on the user's past travel history and reflect that information in the analysis results. For example, if the user prefers mountainous areas, the analysis unit can perform a more detailed analysis of mountain scenery. Furthermore, the analysis unit can analyze the user's travel patterns based on the user's past travel history and predict future travel destinations. This makes it possible to provide more personalized analysis results by referring to the user's past travel history.

[0047] The analysis unit can reflect the user's hobbies and interests when analyzing photos. For example, if the user is interested in a particular sport or art, the analysis results can be adjusted based on that information. The analysis unit can also develop algorithms to adjust the photo analysis results based on the user's hobbies and interests. For example, if the user likes natural landscapes, the analysis unit can perform a more detailed analysis of natural landscapes. Furthermore, the analysis unit can dynamically adjust the photo analysis results based on the user's hobbies and interests. This allows the analysis results to be more personalized by reflecting the user's hobbies and interests.

[0048] When analyzing photos, the analysis unit can refer to the user's lifestyle data and reflect it in the analysis results. For example, the analysis unit can analyze the user's eating and exercise habits and adjust the analysis results based on that information. The analysis unit can also identify the user's lifestyle patterns based on the lifestyle data and reflect that information in the analysis results. For example, if the user likes outdoor activities, the analysis unit can perform a more detailed analysis of outdoor scenes. Furthermore, the analysis unit can dynamically adjust the photo analysis results based on the lifestyle data. This makes it possible to provide more personalized analysis results by referring to the user's lifestyle data.

[0049] When analyzing a photo, the analysis unit can refer to the user's reading history and reflect this in the analysis results. For example, it can analyze the genres and themes of books the user has read in the past and adjust the analysis results based on this information. The analysis unit can also identify the user's interests and concerns based on the reading history and reflect this information in the analysis results. For example, if the user likes mystery novels, it can generate analysis results with a mysterious atmosphere. Furthermore, the analysis unit can dynamically adjust the photo analysis results based on the reading history. This makes it possible to provide more personalized analysis results by referring to the user's reading history.

[0050] When analyzing photos, the analysis unit can refer to the user's travel plans and reflect this in the analysis results. For example, the analysis unit can analyze information about the travel destination the user is planning and adjust the analysis results based on that information. The analysis unit can also identify the user's expectations and wishes based on the travel plans and reflect that information in the analysis results. For example, if the user is planning a beach resort, the analysis unit can perform a more detailed analysis of the beach scenery. Furthermore, the analysis unit can dynamically adjust the photo analysis results based on the travel plans. This makes it possible to provide more personalized analysis results by referring to the user's travel plans.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The photo input section accepts photos taken by the user. For example, photos taken with a smartphone or digital camera can be uploaded. Photos can also be obtained from cloud storage, and users can select and upload photos stored in Google Photos or iCloud. Step 2: The analysis unit analyzes the photo received by the photo input unit and extracts at least one element from the following: landscape, scene, color, and composition. For example, the generation AI recognizes mountain, ocean, and cityscapes in the photo, and analyzes the color and composition of the photo to extract specific patterns. Specifically, it analyzes the proportion of blue in the photo and the composition based on the rule of thirds. Step 3: The music generation unit generates music based on the elements extracted by the analysis unit. For example, the generation AI generates grand music for mountain scenery, and relaxing music including the sound of waves for seascapes. It also adjusts the tempo and atmosphere of the music based on inspiration from the color and composition of the photograph.

[0053] (Example 2) The music generation service according to the embodiment of the present invention is a service that inputs photos taken by a user into AI and generates music based on the scenery and scenes. This allows the music generation service to deepen the impression of the user's travel destination and leave it as a stronger memory.

[0054] A music generation service according to an embodiment includes a photo input unit, an analysis unit, and a music generation unit. The photo input unit accepts photos taken by a user. For example, photos taken with a smartphone or digital camera can be uploaded. The photo input unit can also acquire photos from cloud storage. For example, a user can select and upload photos stored in Google Photos or iCloud. The analysis unit analyzes the photos accepted by the photo input unit and extracts at least one element of landscape, scene, color, or composition. For example, the generation AI recognizes mountain, ocean, or cityscapes in the photos. The generation AI can also analyze the color and composition of the photos and extract specific patterns. For example, the generation AI analyzes the proportion of blue in the photos and the composition based on the rule of thirds. The music generation unit generates music based on the elements extracted by the analysis unit. For example, the generation AI can generate grand music for a mountain scene. For example, the generation AI can generate relaxing music including the sound of waves for a seascape. For example, the generation AI can draw inspiration from the color and composition of the photos to adjust the tempo and atmosphere of the music. As a result, the music generation service according to the embodiment can deepen the user's impression of the travel destination and leave a stronger memory. For example, by listening to the generated music, the user can more vividly recall the experience at the travel destination. Furthermore, by sharing the generated music with friends and family, the user can share common memories.

[0055] The analysis unit can estimate the user's emotional state when analyzing a photo and adjust the analysis results based on that emotion. For example, when analyzing a photo, the analysis unit uses the generation AI to analyze the user's facial expressions and voice in real time to estimate the user's emotional state. For example, if the user is smiling while taking a photo, the analysis results are adjusted to be positive to reflect that emotion. The analysis unit can also adjust the photo analysis results based on the user's emotional state. For example, if the user is relaxed, the analysis results will have a relaxed atmosphere. The analysis unit can also develop an algorithm to adjust the photo analysis results based on the user's emotional state. For example, the generation AI adjusts the analysis results based on the user's emotional score. This allows for more personalized music to be generated by adjusting the analysis results based on the user's emotional state.

[0056] The analysis unit can incorporate the metadata of a photo into the analysis to understand the landscape or scene in more detail. For example, the analysis unit can incorporate the metadata of a photo into the analysis to understand the details of the landscape or scene based on the date and time of the photo and location information. For example, if the photo was taken in the evening, it will be analyzed as a sunset scene. The analysis unit can also analyze the landscape of a specific location based on the location information of the photo. For example, if the location information is a beach, it will be analyzed as a seascape. The analysis unit can also analyze the color and brightness of the photo based on the camera settings (e.g., exposure and white balance). For example, if the exposure is high, it will be analyzed as a bright landscape. In this way, by incorporating the metadata of a photo into the analysis, it is possible to understand the landscape or scene in more detail.

[0057] The analysis unit can analyze the facial expressions and poses of people in photos, infer their emotions and intentions, and reflect them in the analysis results. For example, the analysis unit can analyze the facial expressions of people in photos, infer their emotions, and reflect them in the analysis results. For example, if a person is smiling, the analysis unit can generate a positive analysis result. The analysis unit can also analyze the poses of people in photos, infer their intentions, and reflect them in the analysis results. For example, if a person is raising their hands, the analysis unit can generate an analysis result that reflects their intention of joy. The analysis unit can also develop algorithms to infer emotions and intentions based on people's facial expressions and poses. For example, the generative AI can use facial expression recognition technology to analyze people's emotions. This allows the emotions and intentions of people in photos to be reflected in the analysis results, making it possible to generate more personalized music.

[0058] The analysis unit can add voice memos and text memos to the photo analysis and reflect the user's intentions and impressions in the analysis. For example, when analyzing a photo, the analysis unit analyzes voice memos added by the user and reflects their contents. For example, if the user says "beautiful sunset," the photo is analyzed as a sunset landscape. The analysis unit can also analyze text memos added by the user and reflect their contents when analyzing a photo. For example, if the user writes "fun trip," the analysis unit generates a positive analysis result. The analysis unit can also develop algorithms to analyze the user's intentions and impressions based on the voice memos and text memos. For example, the generation AI can use voice recognition technology to convert voice memos into text and analyze its content. By incorporating voice memos and text memos into the analysis, music can be generated that more accurately reflects the user's intentions and impressions.

[0059] The analysis unit can compare the analysis results of a photo with photos of the same location taken by other users to extract similarities and differences. For example, the analysis unit can compare the analysis results of a photo with photos of the same location taken by other users to extract similarities. For example, if the photos are of the same mountain scenery, the analysis results reflect the common features. The analysis unit can also compare the analysis results of a photo with photos of the same location taken by other users to extract differences. For example, if the photos were taken in different seasons, the analysis results reflect the seasonal differences. The analysis unit can also develop algorithms to adjust the analysis results of a photo based on similarities and differences. For example, the generative AI can adjust the analysis results based on color matches and differences in composition. This allows for comparison with photos of the same location taken by other users to extract similarities and differences, enabling more detailed analysis.

[0060] The analysis unit can use the emotion estimation function to estimate the user's emotion when the photo was taken and adjust the analysis result based on that emotion. For example, the analysis unit can use the emotion estimation function to estimate the user's emotion when the photo was taken and adjust the analysis result based on that emotion. For example, if the user is happy, the analysis unit generates a positive analysis result. The analysis unit can also use the emotion estimation function to analyze the user's emotion in real time and adjust the analysis result based on that emotion. For example, if the user is relaxed, the analysis unit generates an analysis result with a relaxed atmosphere. The analysis unit can also develop an algorithm for analyzing the user's emotion based on the emotion estimation function. For example, the generation AI can analyze the user's emotion using facial expression recognition technology. This allows the generation of more personalized music by estimating the user's emotion when the photo was taken and adjusting the analysis result based on that emotion.

[0061] The analysis unit can learn the user's emotional response to specific colors or compositions in a photograph and adjust the analysis results based on that emotional response. For example, the generation AI in the analysis unit can learn the user's emotional response to specific colors in a photograph and adjust the analysis results based on that response. For example, if a user finds blue relaxing, the generation AI can analyze blue scenes as having a relaxing atmosphere. The analysis unit can also learn the user's emotional response to specific compositions in a photograph and adjust the analysis results based on that response. For example, if a user finds the rule of thirds beautiful, the generation AI can generate an analysis result that indicates that the composition is beautiful. The analysis unit can also develop algorithms to adjust the analysis results based on the user's emotional response to colors or compositions. For example, the generation AI can analyze the user's emotional response based on color psychology and composition aesthetics. This allows the generation AI to learn the user's emotional response to specific colors or compositions and adjust the analysis results based on that response, thereby generating more personalized music.

[0062] The analysis unit can improve the accuracy of analysis by referring to past analysis results of similar photos when analyzing a photo. For example, the analysis unit can improve the accuracy of analysis by referring to past analysis results of similar photos when analyzing a photo. For example, the analysis results can be adjusted based on past photos taken at the same location. The analysis unit can also improve the accuracy of analysis of a photo based on past analysis results of similar photos. For example, past analysis results can be stored in a database and referenced when analyzing a new photo. The analysis unit can also develop an algorithm to improve the accuracy of analysis based on past analysis results of similar photos. For example, the generative AI can use a machine learning model to learn past analysis results and apply them to the analysis of a new photo. In this way, the analysis accuracy can be improved by referring to past analysis results of similar photos.

[0063] When analyzing the content of a photo, the analysis unit can perform a more detailed analysis based on environmental factors such as the season or weather. For example, when analyzing the content of a photo, the analysis unit can take the season into consideration and perform a more detailed analysis. For example, if the photo is a spring landscape, elements such as flowers and fresh greenery can be reflected in the analysis results. The analysis unit can also perform a more detailed analysis by taking the weather into consideration when analyzing the content of a photo. For example, if the photo is a landscape taken on a rainy day, elements such as rain can be reflected in the analysis results. The analysis unit can also develop an algorithm for analyzing the content of a photo based on environmental factors such as the season and weather. For example, the generative AI can learn the characteristics of each season and the effects of weather and reflect these in the analysis results. This enables a more detailed analysis by taking environmental factors such as the season and weather into consideration.

[0064] The analysis unit can integrate the results of photo analysis with other media to perform a more multifaceted analysis. For example, the analysis unit can integrate the results of photo analysis with video to perform a more multifaceted analysis. For example, the analysis results can reflect the movement of a landscape based on videos taken at the same location. The analysis unit can also integrate the results of photo analysis with audio to perform a more multifaceted analysis. For example, the analysis results can reflect the atmosphere of a landscape based on local audio. The analysis unit can also develop algorithms for integration with other media. For example, the generative AI can integrate the results of photo analysis with video analysis technology and audio analysis technology. This makes it possible to perform a more multifaceted analysis by integrating with other media.

[0065] The analysis unit can reanalyze the results of photo analysis from the perspective of a different culture or region to gain different inspiration. For example, the analysis unit can reanalyze the results of photo analysis from the perspective of a different culture to gain different inspiration. For example, analyzing a Japanese landscape from a Western perspective to discover new elements. The analysis unit can also reanalyze the results of photo analysis from the perspective of a different region to gain different inspiration. For example, analyzing an Asian landscape from a European perspective to discover new elements. The analysis unit can also develop algorithms to reanalyze the results of photo analysis based on the perspectives of different cultures and regions. For example, the generative AI can learn cultural backgrounds and local customs and reflect them in the analysis results. This allows different inspiration to be gained by reanalyzing from the perspectives of different cultures and regions.

[0066] The analysis unit can use the emotion estimation function to analyze the user's emotional response to the content of the photo and adjust the analysis result based on the emotional response. For example, the analysis unit can use the emotion estimation function to analyze the user's emotional response to the content of the photo and adjust the analysis result based on the response. For example, if the user is moved, the analysis unit can generate an emotional analysis result. The analysis unit can also use the emotion estimation function to analyze the user's emotional response in real time and adjust the analysis result based on the response. For example, if the user is relaxed, the analysis unit can generate an analysis result with a relaxed atmosphere. The analysis unit can also develop an algorithm for analyzing the user's emotional response based on the emotion estimation function. For example, the generation AI can analyze the user's emotional response using facial expression recognition technology. This allows the generation of more personalized music by analyzing the user's emotional response to the content of the photo and adjusting the analysis result based on the reaction.

[0067] The music generation unit can reflect the user's emotional state in the music generated based on the photo analysis results. For example, the music generation unit reflects the user's emotional state in the music generated by the generation AI based on the photo analysis results. For example, if the user is relaxed, the music generation unit generates relaxing music. The music generation unit can also adjust the tempo and melody of the music based on the user's emotional state. For example, if the user is excited, the music generation unit generates music with a faster tempo. The music generation unit can also develop an algorithm for generating music based on the user's emotional state. For example, the generation AI adjusts the elements of the music based on the emotion score. This allows the user's emotional state to be reflected in the music generated based on the photo analysis results, thereby generating more personalized music.

[0068] The music generation unit can emphasize specific instruments and tones in the music generated based on the results of photo analysis. For example, the generation AI can emphasize specific instruments in the music generated based on the results of photo analysis. For example, music that emphasizes string instruments is generated for mountain landscapes. The music generation unit can also emphasize specific tones. For example, music that emphasizes the sound of waves is generated for seascapes. The music generation unit can also develop algorithms for generating music based on specific instruments and tones. For example, the generation AI selects instruments and adjusts tones. In this way, more impressive music can be generated by emphasizing specific instruments and tones in the music generated based on the results of photo analysis.

[0069] The music generation unit can reflect the user's past musical preferences in the music generated based on the photo analysis results. For example, the music generation unit reflects the user's past musical preferences in the music generated by the generation AI based on the photo analysis results. For example, it generates music in a genre that the user prefers. The music generation unit can also adjust musical elements based on the user's past musical preferences. For example, it can reflect the user's preferred tempo and melody. The music generation unit can also develop algorithms for generating music based on the user's past musical preferences. For example, the generation AI can learn past playback history and user ratings and reflect them in the music. In this way, more personalized music can be generated by reflecting the user's past musical preferences in the music generated based on the photo analysis results.

[0070] The music generation unit can incorporate natural sounds and environmental sounds into the music generated based on the results of photo analysis. For example, the generation AI incorporates natural sounds into the music generated based on the results of photo analysis. For example, music that incorporates birdsong is generated for a mountain landscape. The music generation unit can also incorporate environmental sounds. For example, music that incorporates the sound of waves is generated for an ocean landscape. The music generation unit can also develop algorithms for generating music based on natural sounds and environmental sounds. For example, the generation AI selects and incorporates natural sounds and environmental sounds. In this way, by incorporating natural sounds and environmental sounds into the music generated based on the results of photo analysis, more realistic music can be generated.

[0071] The music generation unit can combine elements of different musical genres into the music generated based on the results of photo analysis. For example, the generative AI can combine elements of different musical genres into the music generated based on the results of photo analysis. For example, it can generate music that combines classical and electronica for mountain scenery. The music generation unit can also combine elements of different musical genres. For example, it can generate music that combines jazz and ambient for seascapes. The music generation unit can also develop algorithms for generating music based on elements of different musical genres. For example, the generative AI can select genres and combine elements. This allows for a wider variety of music to be generated by combining elements of different musical genres into the music generated based on the results of photo analysis.

[0072] The music generation unit can use the emotion estimation function to reflect the user's emotional response in the music generated based on the photo analysis results. For example, the music generation unit uses the emotion estimation function to reflect the user's emotional response in the music generated based on the photo analysis results. For example, if the user is moved, the music generation unit generates moving music. The music generation unit can also adjust the elements of the music based on the user's emotional response. For example, if the user is relaxed, the music generation unit generates relaxing music. The music generation unit can also develop an algorithm for analyzing the user's emotional response based on the emotion estimation function. For example, the generation AI uses facial expression recognition technology to analyze the user's emotional response. This allows the user's emotional response to be reflected in the music generated based on the photo analysis results, thereby generating more personalized music.

[0073] The music generation unit can reflect the user's emotional state in the music generated based on the photo analysis results. For example, the music generation unit reflects the user's emotional state in the music generated by the generation AI based on the photo analysis results. For example, if the user is relaxed, the music generation unit generates relaxing music. The music generation unit can also adjust the tempo and melody of the music based on the user's emotional state. For example, if the user is excited, the music generation unit generates music with a faster tempo. The music generation unit can also develop an algorithm for generating music based on the user's emotional state. For example, the generation AI adjusts the elements of the music based on the emotion score. This allows the user's emotional state to be reflected in the music generated based on the photo analysis results, thereby generating more personalized music.

[0074] The music generation unit can emphasize specific instruments and tones in the music generated based on the results of photo analysis. For example, the generation AI can emphasize specific instruments in the music generated based on the results of photo analysis. For example, music that emphasizes string instruments is generated for mountain landscapes. The music generation unit can also emphasize specific tones. For example, music that emphasizes the sound of waves is generated for seascapes. The music generation unit can also develop algorithms for generating music based on specific instruments and tones. For example, the generation AI selects instruments and adjusts tones. In this way, more impressive music can be generated by emphasizing specific instruments and tones in the music generated based on the results of photo analysis.

[0075] The music generation unit can reflect the user's past musical preferences in the music generated based on the photo analysis results. For example, the music generation unit reflects the user's past musical preferences in the music generated by the generation AI based on the photo analysis results. For example, it generates music in a genre that the user prefers. The music generation unit can also adjust musical elements based on the user's past musical preferences. For example, it can reflect the user's preferred tempo and melody. The music generation unit can also develop algorithms for generating music based on the user's past musical preferences. For example, the generation AI can learn past playback history and user ratings and reflect them in the music. In this way, more personalized music can be generated by reflecting the user's past musical preferences in the music generated based on the photo analysis results.

[0076] The music generation unit can incorporate natural sounds and environmental sounds into the music generated based on the results of photo analysis. For example, the generation AI incorporates natural sounds into the music generated based on the results of photo analysis. For example, music that incorporates birdsong is generated for a mountain landscape. The music generation unit can also incorporate environmental sounds. For example, music that incorporates the sound of waves is generated for an ocean landscape. The music generation unit can also develop algorithms for generating music based on natural sounds and environmental sounds. For example, the generation AI selects and incorporates natural sounds and environmental sounds. In this way, by incorporating natural sounds and environmental sounds into the music generated based on the results of photo analysis, more realistic music can be generated.

[0077] The music generation unit can combine elements of different musical genres into the music generated based on the results of photo analysis. For example, the generative AI can combine elements of different musical genres into the music generated based on the results of photo analysis. For example, it can generate music that combines classical and electronica for mountain scenery. The music generation unit can also combine elements of different musical genres. For example, it can generate music that combines jazz and ambient for seascapes. The music generation unit can also develop algorithms for generating music based on elements of different musical genres. For example, the generative AI can select genres and combine elements. This allows for a wider variety of music to be generated by combining elements of different musical genres into the music generated based on the results of photo analysis.

[0078] The music generation unit can use the emotion estimation function to reflect the user's emotional response in the music generated based on the photo analysis results. For example, the music generation unit uses the emotion estimation function to reflect the user's emotional response in the music generated based on the photo analysis results. For example, if the user is moved, the music generation unit generates moving music. The music generation unit can also adjust the elements of the music based on the user's emotional response. For example, if the user is relaxed, the music generation unit generates relaxing music. The music generation unit can also develop an algorithm for analyzing the user's emotional response based on the emotion estimation function. For example, the generation AI uses facial expression recognition technology to analyze the user's emotional response. This allows the user's emotional response to be reflected in the music generated based on the photo analysis results, thereby generating more personalized music.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] When analyzing photos, the analysis unit can refer to the user's past travel history and reflect it in the analysis results. For example, by analyzing photos of places the user has visited in the past and learning the characteristics of those places, the analysis results for the current photo can be adjusted with higher accuracy. The analysis unit can also identify the user's preferred landscapes and scenes based on the user's past travel history and reflect that information in the analysis results. For example, if the user prefers mountainous areas, the analysis unit can perform a more detailed analysis of mountain scenery. Furthermore, the analysis unit can analyze the user's travel patterns based on the user's past travel history and predict future travel destinations. This makes it possible to provide more personalized analysis results by referring to the user's past travel history.

[0081] The analysis unit can estimate the user's emotional state when analyzing a photo and adjust the analysis results based on that emotion. For example, the analysis unit can estimate the emotion the user felt when taking a photo and adjust the analysis results to positive or negative based on that emotion. The analysis unit can also develop an algorithm for adjusting the photo analysis results based on the user's emotional state. For example, if the user is relaxed, the analysis unit can generate an analysis result with a relaxed atmosphere. Furthermore, the analysis unit can analyze the user's emotional state in real time and dynamically adjust the analysis results based on that emotion. This allows for more personalized music to be generated by adjusting the analysis results based on the user's emotional state.

[0082] When analyzing photos, the analysis unit can refer to the content of the user's social media posts and reflect this in the analysis results. For example, it can analyze photos and comments posted by the user on Instagram or Facebook and adjust the analysis results based on this content. The analysis unit can also identify the user's interests and concerns based on the content of the social media posts and reflect this information in the analysis results. For example, if the user is interested in a particular place or event, this information can be reflected in the analysis results. Furthermore, the analysis unit can infer the user's emotional state based on the content of the social media posts and adjust the analysis results based on that emotion. This makes it possible to provide more personalized analysis results by referring to the content of the user's social media posts.

[0083] When analyzing photos, the analysis unit can refer to the user's music playback history and reflect this in the analysis results. For example, it can analyze the genres and artists of music the user has listened to in the past and adjust the analysis results based on this information. The analysis unit can also identify the user's musical preferences based on the music playback history and reflect this information in the analysis results. For example, if the user prefers classical music, it can generate analysis results that include elements of classical music. Furthermore, the analysis unit can estimate the user's emotional state based on the music playback history and adjust the analysis results based on that emotion. This makes it possible to provide more personalized analysis results by referring to the user's music playback history.

[0084] When analyzing a photo, the analysis unit can refer to the user's health data and reflect it in the analysis results. For example, it can analyze the user's heart rate and stress level and adjust the analysis results based on that information. The analysis unit can also identify the user's physical condition and mood based on the health data and reflect that information in the analysis results. For example, if the user is relaxed, it can generate an analysis result with a relaxed atmosphere. Furthermore, the analysis unit can estimate the user's emotional state based on the health data and adjust the analysis results based on that emotion. In this way, by referring to the user's health data, it is possible to provide more personalized analysis results.

[0085] The analysis unit can reflect the user's hobbies and interests when analyzing photos. For example, if the user is interested in a particular sport or art, the analysis results can be adjusted based on that information. The analysis unit can also develop algorithms to adjust the photo analysis results based on the user's hobbies and interests. For example, if the user likes natural landscapes, the analysis unit can perform a more detailed analysis of natural landscapes. Furthermore, the analysis unit can dynamically adjust the photo analysis results based on the user's hobbies and interests. This allows the analysis results to be more personalized by reflecting the user's hobbies and interests.

[0086] When analyzing photos, the analysis unit can refer to the user's lifestyle data and reflect it in the analysis results. For example, the analysis unit can analyze the user's eating and exercise habits and adjust the analysis results based on that information. The analysis unit can also identify the user's lifestyle patterns based on the lifestyle data and reflect that information in the analysis results. For example, if the user likes outdoor activities, the analysis unit can perform a more detailed analysis of outdoor scenes. Furthermore, the analysis unit can dynamically adjust the photo analysis results based on the lifestyle data. This makes it possible to provide more personalized analysis results by referring to the user's lifestyle data.

[0087] When analyzing a photo, the analysis unit can refer to the user's reading history and reflect this in the analysis results. For example, it can analyze the genres and themes of books the user has read in the past and adjust the analysis results based on this information. The analysis unit can also identify the user's interests and concerns based on the reading history and reflect this information in the analysis results. For example, if the user likes mystery novels, it can generate analysis results with a mysterious atmosphere. Furthermore, the analysis unit can dynamically adjust the photo analysis results based on the reading history. This makes it possible to provide more personalized analysis results by referring to the user's reading history.

[0088] When analyzing photos, the analysis unit can refer to the user's travel plans and reflect this in the analysis results. For example, the analysis unit can analyze information about the travel destination the user is planning and adjust the analysis results based on that information. The analysis unit can also identify the user's expectations and wishes based on the travel plans and reflect that information in the analysis results. For example, if the user is planning a beach resort, the analysis unit can perform a more detailed analysis of the beach scenery. Furthermore, the analysis unit can dynamically adjust the photo analysis results based on the travel plans. This makes it possible to provide more personalized analysis results by referring to the user's travel plans.

[0089] When analyzing a photo, the analysis unit can refer to information about the user's pet and reflect that information in the analysis results. For example, the analysis unit can analyze the type and characteristics of the user's pet and adjust the analysis results based on that information. The analysis unit can also identify the user's emotional state based on the pet information and reflect that information in the analysis results. For example, in the case of a photo taken by the user with a pet, the analysis unit generates an analysis result that takes the presence of the pet into account. Furthermore, the analysis unit can dynamically adjust the photo analysis results based on the pet information. In this way, by referring to the user's pet information, more personalized analysis results can be provided.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The photo input section accepts photos taken by the user. For example, photos taken with a smartphone or digital camera can be uploaded. Photos can also be obtained from cloud storage, and users can select and upload photos stored in Google Photos or iCloud. Step 2: The analysis unit analyzes the photo received by the photo input unit and extracts at least one element from the following: landscape, scene, color, and composition. For example, the generation AI recognizes mountain, ocean, and cityscapes in the photo, and analyzes the color and composition of the photo to extract specific patterns. Specifically, it analyzes the proportion of blue in the photo and the composition based on the rule of thirds. Step 3: The music generation unit generates music based on the elements extracted by the analysis unit. For example, the generation AI generates grand music for mountain scenery, and relaxing music including the sound of waves for seascapes. It also adjusts the tempo and atmosphere of the music based on inspiration from the color and composition of the photograph.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 AI 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 AI 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 AI 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 input unit that accepts photos taken by a user; an analysis unit that analyzes the photograph received by the photograph input unit and extracts at least one element from among scenery, landscape, color, and composition; a music generation unit that generates music based on the elements extracted by the analysis unit. A system characterized by:

2. The analysis unit Incorporating photo metadata into the analysis to gain a more detailed understanding of the landscape or scene 2. The system of claim 1.

3. The analysis unit Add voice or text memos to the photo analysis, and reflect the user's intentions and thoughts in the analysis.

2. The system of claim 1.

4. The analysis unit The user's emotion at the time of taking the photo is estimated, and the analysis results are adjusted based on the emotion.

2. The system of claim 1.

5. The music generation unit The music generated based on the analysis of the photo reflects the emotional state of the user.

2. The system of claim 1.

Images