system

Abstract

The system according to this embodiment aims to generate and provide music to the user based on the atmosphere and emotions of a photograph. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos uploaded by the user. The analysis unit detects the characteristics of the photos received by the reception unit and analyzes the atmosphere, color tone, and emotion. The generation unit generates music based on the results analyzed by the analysis unit. The provision unit provides the music generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to generate music based on the atmosphere or emotion of a photo and it is impossible to provide music that suits the user's sensibility.

[0005] The system according to the embodiment aims to generate music based on the atmosphere or emotion of a photo and provide it to the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos uploaded by the user. The analysis unit detects the characteristics of the photos received by the reception unit and analyzes their atmosphere, color tones, and emotions. The generation unit generates music based on the results analyzed by the analysis unit. The provision unit provides the music generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate and provide music to the user based on the atmosphere and emotions of a photograph. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that analyzes the atmosphere, color tones, and emotions of a photograph uploaded by a user and automatically generates original music that matches it. In this AI agent system, the user uploads a photograph, and the AI ​​detects the characteristics of the photograph to analyze its atmosphere, color tones, and emotions. Based on this analysis, the AI ​​uses a proprietary algorithm to create the genre, melody, harmony, and rhythm of the music. For example, a pop song is generated from a photograph with bright colors, and a classical song is generated from a photograph with subdued colors. This mechanism allows users to enjoy original music that perfectly matches their photographs. Thus, the AI ​​agent system can automatically generate and provide music based on the photographs uploaded by the user.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos uploaded by the user. The reception unit enables users to upload photos, for example, through a website or application. The reception unit can also check the format and size of the photos and convert them to an appropriate format. The analysis unit detects the features of the photos received by the reception unit and analyzes their atmosphere, color tones, and emotions. The analysis unit analyzes the color distribution and shape of the photos, for example, using image processing technology. The analysis unit can also estimate the emotions of people in the photos using facial recognition technology. The analysis unit can also analyze emotions from the color tones of the photos based on color psychology. The generation unit generates music based on the results analyzed by the analysis unit. The generation unit creates the genre, melody, harmony, and rhythm of the music, for example, using generation AI. The generation unit can also adjust the tempo and timbre of the music to match the atmosphere of the photos. The generation unit may also include an evaluation unit that evaluates the quality of the music and makes corrections as necessary. The provision unit provides the music generated by the generation unit to the user. The service provider can, for example, provide music in streaming format. It can also provide music in download format. Furthermore, it can provide music in a format optimized for the user's device. This allows the AI ​​agent system to automatically generate and provide music based on photos uploaded by the user.

[0030] The reception unit receives photos uploaded by users. For example, it enables users to upload photos through websites or applications. Specifically, users can select photos through a dedicated interface and send them to the system by pressing an upload button. The reception unit can also check the format and size of the photos and convert them to the appropriate format. For example, it supports common image formats such as JPEG, PNG, and TIFF, and optimizes image resolution and file size as needed. Furthermore, the reception unit can extract metadata from uploaded photos (such as date and time of shooting, location information, and camera settings) and provide it to the analysis unit. This allows the reception unit to provide an environment where users can easily and quickly upload photos, preparing them for smooth subsequent analysis processing.

[0031] The analysis unit detects the features of photographs received by the reception unit and analyzes their atmosphere, color tones, and emotions. For example, the analysis unit uses image processing technology to analyze the color distribution and shape of the photograph. Specifically, it generates an image histogram and analyzes the color distribution to evaluate the overall tone and contrast of the photograph. Furthermore, it uses edge detection algorithms to analyze the shape and structure of the photograph and identify major objects and backgrounds. The analysis unit can also estimate the emotions of people in a photograph using facial recognition technology. For example, it detects facial feature points and analyzes changes in facial expression to estimate emotions such as joy, sadness, and surprise. The analysis unit can also analyze emotions from the color tones of a photograph based on color psychology. For example, it analyzes that photographs with many warm colors indicate warmth and happiness, while photographs with many cool colors indicate calmness and sadness. In this way, the analysis unit can analyze the visual features and emotional elements of a photograph in detail and provide the generation unit with rich information for music generation.

[0032] The generation unit generates music based on the results analyzed by the analysis unit. For example, the generation unit uses a generation AI to create the genre, melody, harmony, and rhythm of the music. Specifically, the generation AI selects an appropriate music genre and generates a melody line based on the atmosphere and emotion of the photograph provided by the analysis unit. For example, if the photograph has a bright and cheerful atmosphere, a pop or up-tempo song will be generated. On the other hand, if the photograph has a calm atmosphere, a classical or jazz song will be generated. The generation unit can also adjust the tempo and tone of the music to match the atmosphere of the photograph. For example, if the photograph depicts a tranquil landscape, a slow-tempo song will be generated, and a soft tone will be used. The generation unit may also include an evaluation unit that assesses the quality of the music and makes corrections as needed. The evaluation unit evaluates the consistency of the melody and harmony, the accuracy of the rhythm, etc., of the generated music and makes corrections as necessary. This allows the generation unit to generate high-quality music optimized for the atmosphere and emotion of the photograph.

[0033] The service provider delivers the music generated by the production unit to the user. For example, the service provider can deliver music in streaming format. Specifically, users can play the music through websites or applications and enjoy it in real time. The service provider can also deliver music in download format. Users can save the generated music to their devices and play it offline. The service provider can also deliver music in a format optimized for the user's device. For example, it can select and provide the optimal sound quality and file format for different devices such as smartphones, tablets, and PCs. Furthermore, the service provider can collect user feedback to improve the quality and delivery method of the music. For example, it can collect user impressions and opinions while playing music and reflect them in future music generation and delivery. This allows the service provider to deliver high-quality music quickly and flexibly to users, improving the user experience.

[0034] The generation unit further includes an evaluation unit that assesses the quality of the generated music and makes corrections as needed. The generation unit can, for example, use a generation AI to assess the quality of the music. The generation unit can evaluate the music using evaluation criteria such as sound quality, melodic consistency, and emotional impact. The generation unit can also make corrections to the music based on the evaluation results. For example, the generation unit can modify the melody line to improve melodic consistency. The generation unit can also adjust sound effects to improve sound quality. The generation unit can also adjust the tempo and harmony of the music to enhance the emotional impact. In this way, the generation unit can improve the quality of the music by assessing the quality of the generated music and making corrections as needed.

[0035] The reception desk can analyze a user's past photo upload history and select the optimal reception method. For example, it can analyze the time periods when a user frequently uploaded photos in the past and suggest the most suitable reception method for those times. The reception desk can also analyze the types of photos a user has uploaded in the past and prioritize the reception of similar photos. Based on the user's past upload history, the reception desk can also prioritize the reception of photos related to specific events or seasons. In this way, the reception desk can select the optimal reception method by analyzing a user's past photo upload history. The optimal reception method is selected, for example, based on the user's past behavior patterns and current situation.

[0036] The reception system can filter photos based on the user's current projects and areas of interest when they are submitted. For example, the reception system can prioritize photos related to a project the user is currently working on. The reception system can also filter and submit relevant photos based on the user's areas of interest. If the user is interested in a particular theme, the reception system can prioritize photos related to that theme. This allows the reception system to prioritize submitting highly relevant photos by filtering them based on the user's current projects and areas of interest. Current projects are defined, for example, based on the type and progress of the project. Areas of interest are defined, for example, based on the user's past behavior history or survey results.

[0037] The reception desk can prioritize accepting photos that are highly relevant to the user's geographical location when they are submitted. For example, if the user is in a specific region, the reception desk will prioritize accepting photos related to that region. If the user is traveling, the reception desk can also prioritize accepting photos related to their travel destination. If the user is participating in a specific event, the reception desk can also prioritize accepting photos related to that event. This allows the reception desk to respond flexibly to the user's situation by prioritizing highly relevant photos based on the user's geographical location. Geographical location information is defined, for example, based on GPS data or location services.

[0038] The reception desk can analyze a user's social media activity when receiving photos and accept relevant photos. For example, the reception desk can prioritize photos that the user has shared on social media. It can also prioritize photos that the user has shown interest in on social media. It can also prioritize photos related to accounts that the user follows on social media. In this way, the reception desk can prioritize the acceptance of relevant photos by analyzing the user's social media activity. Social media activity is defined based on, for example, the content of posts, the number of likes, and comments.

[0039] The analysis unit can adjust the level of detail in its analysis based on the importance of each photograph. For example, it can analyze photographs of important events in detail, carefully examining the atmosphere and color tones. It can also perform a simpler analysis of everyday photographs, focusing only on key features. For photographs related to a specific theme, the analysis unit can perform a detailed analysis tailored to that theme. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of each photograph. Importance can be defined, for example, based on user ratings or the content of the photograph.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the photograph during analysis. For example, the analysis unit can apply an algorithm that analyzes the natural colors and atmosphere to landscape photographs. The analysis unit can also apply an algorithm that analyzes the facial expressions and emotions of people to portrait photographs. The analysis unit can also apply an algorithm that analyzes the atmosphere and colors of events to event photographs. This allows the analysis unit to perform efficient analysis by applying different analysis algorithms depending on the category of the photograph. Categories are defined based on, for example, landscapes, people, events, etc.

[0041] The analysis unit can determine the priority of the analysis based on when the photos were taken. For example, the analysis unit may prioritize the analysis of recently taken photos. It can also prioritize the analysis of photos taken during a specific event period. It can also prioritize the analysis of photos taken in a specific season. This allows the analysis unit to perform efficient analysis by determining the priority of the analysis based on when the photos were taken. The shooting period can be defined, for example, based on the season or time of day.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the photos during the analysis process. For example, the analysis unit can analyze photos taken at the same event together. It can also analyze photos related to the same theme together. It can also prioritize the analysis of photos related to the user's areas of interest. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the photos. Relevance is defined, for example, based on similarity of content or a common theme.

[0043] The music generator can adjust the level of detail in a song based on the characteristics of the photograph during music generation. For example, it can generate a pop-style song for a brightly colored photograph. It can also generate a classical song for a mutedly colored photograph. It can even generate a song that reflects the emotion of an emotional photograph. This allows the generator to flexibly adapt to the user's situation by adjusting the level of detail in the song based on the characteristics of the photograph. Characteristics are defined based on, for example, color, shape, pattern, and type of object.

[0044] The generation unit can apply different generation algorithms depending on the category of the photograph when generating music. For example, for landscape photographs, the generation unit can generate music that incorporates natural sounds. For portrait photographs, the generation unit can also generate music that reflects the emotions of the person. For event photographs, the generation unit can generate music that reflects the atmosphere of the event. This allows the generation unit to efficiently generate music by applying different generation algorithms depending on the category of the photograph. Categories are defined based on, for example, landscapes, people, events, etc.

[0045] The generation unit can determine the priority of songs based on when the photos were taken during song generation. For example, the generation unit can prioritize songs based on recently taken photos. The generation unit can also prioritize songs based on photos taken during a specific event period. The generation unit can also prioritize songs based on photos taken in each season. This allows the generation unit to efficiently generate songs by prioritizing songs based on when the photos were taken. The shooting period can be defined, for example, based on the season or time of day.

[0046] The generation unit can adjust the order of songs based on the relevance of the photos during song generation. For example, the generation unit can group songs based on photos taken at the same event. The generation unit can also group songs based on photos related to the same theme. The generation unit can also prioritize the generation of songs based on photos related to the user's areas of interest. This allows the generation unit to efficiently generate songs by adjusting the order of songs based on the relevance of the photos. Relevance can be defined, for example, based on similarity of content or a common theme.

[0047] The service provider can select the optimal service delivery method by referring to the user's past music usage history when providing music. For example, the service provider may prioritize the service delivery method for music that the user has previously preferred. The service provider can also suggest the optimal service delivery method for a specific time period based on the user's past usage history. The service provider can also analyze the user's past usage history and select the most efficient service delivery method. In this way, the service provider can select the optimal service delivery method by referring to the user's past music usage history. Past music usage history is defined, for example, based on playback history and rating history.

[0048] The distribution unit can select the optimal distribution method when providing music, taking into account the user's device information. For example, if the user is using a smartphone, the distribution unit will select a distribution method optimized for smartphones. If the user is using a tablet, the distribution unit can also select a distribution method optimized for tablets. If the user is using a smartwatch, the distribution unit can also select a distribution method optimized for smartwatches. This allows the distribution unit to respond flexibly to the user's situation by selecting the optimal distribution method considering the user's device information. Device information is defined, for example, based on the device type, OS, and screen size.

[0049] The evaluation unit can select the optimal evaluation method when evaluating a song by referring to the user's past evaluation history. For example, the evaluation unit can suggest the optimal evaluation method based on the user's past evaluations. The evaluation unit can also prioritize providing specific evaluation items based on the user's past evaluation history. The evaluation unit can also analyze the user's past evaluation history and select the most efficient evaluation method. In this way, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history. Past evaluation history is defined, for example, based on evaluation scores and comments.

[0050] The evaluation unit can select the optimal evaluation method when evaluating music, taking into account the user's geographical location information. For example, if the user is in a specific region, the evaluation unit will provide evaluation items related to that region. If the user is traveling, the evaluation unit can also provide evaluation items related to the travel destination. If the user is participating in a specific event, the evaluation unit can also provide evaluation items related to that event. This allows the evaluation unit to respond flexibly to the user's situation by selecting the optimal evaluation method considering the user's geographical location information. Geographical location information is defined, for example, based on GPS data or location information services. The optimal evaluation method is selected, for example, based on the user's past behavior patterns or current situation.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception system can refer to the user's past photo upload history when accepting a user's photos and suggest the most suitable reception method. For example, if a user frequently uploaded photos during a specific time period in the past, the reception system can suggest the most suitable reception method for that time period. Furthermore, the reception system can analyze the types of photos the user has uploaded in the past and prioritize the acceptance of similar photos. This allows the reception system to provide the most suitable reception method based on the user's past behavioral patterns.

[0053] The reception desk can prioritize receiving photos that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving photos related to that region. If the user is traveling, it can also prioritize receiving photos related to their travel destination. This allows the reception desk to respond flexibly to the user's situation by prioritizing highly relevant photos based on the user's geographical location.

[0054] The analysis unit can apply different analysis algorithms depending on the category of the photograph. For example, an algorithm that analyzes natural colors and atmosphere can be applied to landscape photographs. An algorithm that analyzes the facial expressions and emotions of the person can also be applied to portrait photographs. This allows the analysis unit to perform efficient analysis by applying different analysis algorithms depending on the category of the photograph.

[0055] The generation unit can prioritize songs based on when the photos were taken. For example, it can prioritize generating songs based on recently taken photos. It can also prioritize generating songs based on photos taken during a specific event period. This allows the generation unit to efficiently generate songs by prioritizing them based on when the photos were taken.

[0056] The service provider can select the optimal delivery method by considering the user's device information. For example, if the user is using a smartphone, it can select a delivery method optimized for smartphones. If the user is using a tablet, it can also select a delivery method optimized for tablets. This allows the service provider to respond flexibly to the user's situation by selecting the optimal delivery method considering the user's device information.

[0057] The evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history when evaluating a song. For example, it can suggest the optimal evaluation method based on the user's past evaluations. It can also prioritize providing specific evaluation items based on the user's past evaluation history. In this way, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives photos uploaded by users. The reception desk allows users to upload photos through the website or application, and can also check the format and size of the photos and convert them to the appropriate format. Step 2: The analysis unit detects the characteristics of the photograph received by the reception unit and analyzes its atmosphere, color tone, and emotions. The analysis unit can analyze the color distribution and shape of the photograph using image processing technology and estimate the emotions of the people in the photograph using face recognition technology. It can also analyze emotions from the color tone of the photograph based on color psychology. Step 3: The generation unit generates music based on the results analyzed by the analysis unit. The generation unit uses generation AI to create the genre, melody, harmony, and rhythm of the music, and can adjust the tempo and tone of the music to match the atmosphere of the photograph. It can also include an evaluation unit that evaluates the quality of the music and makes corrections as needed. Step 4: The distribution unit provides the music generated by the generation unit to the user. The distribution unit can provide the music in streaming or download format, and can provide the music in a format optimized for the user's device.

[0060] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes the atmosphere, color tones, and emotions of a photograph uploaded by a user and automatically generates original music that matches it. In this AI agent system, the user uploads a photograph, and the AI ​​detects the characteristics of the photograph to analyze its atmosphere, color tones, and emotions. Based on this analysis, the AI ​​uses a proprietary algorithm to create the genre, melody, harmony, and rhythm of the music. For example, a pop song is generated from a photograph with bright colors, and a classical song is generated from a photograph with subdued colors. This mechanism allows users to enjoy original music that perfectly matches their photographs. Thus, the AI ​​agent system can automatically generate and provide music based on the photographs uploaded by the user.

[0061] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives photos uploaded by the user. The reception unit enables users to upload photos, for example, through a website or application. The reception unit can also check the format and size of the photos and convert them to an appropriate format. The analysis unit detects the features of the photos received by the reception unit and analyzes their atmosphere, color tones, and emotions. The analysis unit analyzes the color distribution and shape of the photos, for example, using image processing technology. The analysis unit can also estimate the emotions of people in the photos using facial recognition technology. The analysis unit can also analyze emotions from the color tones of the photos based on color psychology. The generation unit generates music based on the results analyzed by the analysis unit. The generation unit creates the genre, melody, harmony, and rhythm of the music, for example, using generation AI. The generation unit can also adjust the tempo and timbre of the music to match the atmosphere of the photos. The generation unit may also include an evaluation unit that evaluates the quality of the music and makes corrections as necessary. The provision unit provides the music generated by the generation unit to the user. The service provider can, for example, provide music in streaming format. It can also provide music in download format. Furthermore, it can provide music in a format optimized for the user's device. This allows the AI ​​agent system to automatically generate and provide music based on photos uploaded by the user.

[0062] The reception unit receives photos uploaded by users. For example, it enables users to upload photos through websites or applications. Specifically, users can select photos through a dedicated interface and send them to the system by pressing an upload button. The reception unit can also check the format and size of the photos and convert them to the appropriate format. For example, it supports common image formats such as JPEG, PNG, and TIFF, and optimizes image resolution and file size as needed. Furthermore, the reception unit can extract metadata from uploaded photos (such as date and time of shooting, location information, and camera settings) and provide it to the analysis unit. This allows the reception unit to provide an environment where users can easily and quickly upload photos, preparing them for smooth subsequent analysis processing.

[0063] The analysis unit detects the features of photographs received by the reception unit and analyzes their atmosphere, color tones, and emotions. For example, the analysis unit uses image processing technology to analyze the color distribution and shape of the photograph. Specifically, it generates an image histogram and analyzes the color distribution to evaluate the overall tone and contrast of the photograph. Furthermore, it uses edge detection algorithms to analyze the shape and structure of the photograph and identify major objects and backgrounds. The analysis unit can also estimate the emotions of people in a photograph using facial recognition technology. For example, it detects facial feature points and analyzes changes in facial expression to estimate emotions such as joy, sadness, and surprise. The analysis unit can also analyze emotions from the color tones of a photograph based on color psychology. For example, it analyzes that photographs with many warm colors indicate warmth and happiness, while photographs with many cool colors indicate calmness and sadness. In this way, the analysis unit can analyze the visual features and emotional elements of a photograph in detail and provide the generation unit with rich information for music generation.

[0064] The generation unit generates music based on the results analyzed by the analysis unit. For example, the generation unit uses a generation AI to create the genre, melody, harmony, and rhythm of the music. Specifically, the generation AI selects an appropriate music genre and generates a melody line based on the atmosphere and emotion of the photograph provided by the analysis unit. For example, if the photograph has a bright and cheerful atmosphere, a pop or up-tempo song will be generated. On the other hand, if the photograph has a calm atmosphere, a classical or jazz song will be generated. The generation unit can also adjust the tempo and tone of the music to match the atmosphere of the photograph. For example, if the photograph depicts a tranquil landscape, a slow-tempo song will be generated, and a soft tone will be used. The generation unit may also include an evaluation unit that assesses the quality of the music and makes corrections as needed. The evaluation unit evaluates the consistency of the melody and harmony, the accuracy of the rhythm, etc., of the generated music and makes corrections as necessary. This allows the generation unit to generate high-quality music optimized for the atmosphere and emotion of the photograph.

[0065] The service provider delivers the music generated by the production unit to the user. For example, the service provider can deliver music in streaming format. Specifically, users can play the music through websites or applications and enjoy it in real time. The service provider can also deliver music in download format. Users can save the generated music to their devices and play it offline. The service provider can also deliver music in a format optimized for the user's device. For example, it can select and provide the optimal sound quality and file format for different devices such as smartphones, tablets, and PCs. Furthermore, the service provider can collect user feedback to improve the quality and delivery method of the music. For example, it can collect user impressions and opinions while playing music and reflect them in future music generation and delivery. This allows the service provider to deliver high-quality music quickly and flexibly to users, improving the user experience.

[0066] The generation unit further includes an evaluation unit that assesses the quality of the generated music and makes corrections as needed. The generation unit can, for example, use a generation AI to assess the quality of the music. The generation unit can evaluate the music using evaluation criteria such as sound quality, melodic consistency, and emotional impact. The generation unit can also make corrections to the music based on the evaluation results. For example, the generation unit can modify the melody line to improve melodic consistency. The generation unit can also adjust sound effects to improve sound quality. The generation unit can also adjust the tempo and harmony of the music to enhance the emotional impact. In this way, the generation unit can improve the quality of the music by assessing the quality of the generated music and making corrections as needed.

[0067] The reception system can estimate the user's emotions and adjust the photo submission timing based on the estimated emotions. For example, the reception system can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reception system can calculate an emotion score based on changes in facial expression. The reception system can also record the user's voice and estimate the emotion using voice analysis technology. The reception system can analyze the tone and speed of the voice and calculate an emotion score. The reception system adjusts the photo submission timing according to the user's emotions. For example, if the user is relaxed, the reception system can flexibly set the photo submission timing, allowing the user to upload photos at their preferred time. If the user is busy, the reception system can shorten the photo submission timing to allow for quick uploads. If the user is emotionally unstable, the reception system can adjust the photo submission timing so that the user can upload when they are calm. In this way, the reception system can flexibly respond to the user's situation by adjusting the photo submission timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0068] The reception desk can analyze a user's past photo upload history and select the optimal reception method. For example, it can analyze the time periods when a user frequently uploaded photos in the past and suggest the most suitable reception method for those times. The reception desk can also analyze the types of photos a user has uploaded in the past and prioritize the reception of similar photos. Based on the user's past upload history, the reception desk can also prioritize the reception of photos related to specific events or seasons. In this way, the reception desk can select the optimal reception method by analyzing a user's past photo upload history. The optimal reception method is selected, for example, based on the user's past behavior patterns and current situation.

[0069] The reception system can filter photos based on the user's current projects and areas of interest when they are submitted. For example, the reception system can prioritize photos related to a project the user is currently working on. The reception system can also filter and submit relevant photos based on the user's areas of interest. If the user is interested in a particular theme, the reception system can prioritize photos related to that theme. This allows the reception system to prioritize submitting highly relevant photos by filtering them based on the user's current projects and areas of interest. Current projects are defined, for example, based on the type and progress of the project. Areas of interest are defined, for example, based on the user's past behavior history or survey results.

[0070] The reception system can estimate the user's emotions and prioritize the photos to accept based on those emotions. For example, the reception system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception system can calculate an emotion score based on changes in facial expressions. The reception system can also record the user's voice and estimate their emotions using voice analysis technology. The reception system can analyze the tone and speed of the voice and calculate an emotion score. The reception system prioritizes the photos to accept according to the user's emotions. For example, if the user is excited, the reception system will prioritize emotional photos. If the user is calm, the reception system may prioritize photos with a calm atmosphere. If the user is sad, the reception system may prioritize comforting photos. This allows the reception system to respond flexibly to the user's situation by prioritizing photos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0071] The reception desk can prioritize accepting photos that are highly relevant to the user's geographical location when they are submitted. For example, if the user is in a specific region, the reception desk will prioritize accepting photos related to that region. If the user is traveling, the reception desk can also prioritize accepting photos related to their travel destination. If the user is participating in a specific event, the reception desk can also prioritize accepting photos related to that event. This allows the reception desk to respond flexibly to the user's situation by prioritizing highly relevant photos based on the user's geographical location. Geographical location information is defined, for example, based on GPS data or location services.

[0072] The reception desk can analyze a user's social media activity when receiving photos and accept relevant photos. For example, the reception desk can prioritize photos that the user has shared on social media. It can also prioritize photos that the user has shown interest in on social media. It can also prioritize photos related to accounts that the user follows on social media. In this way, the reception desk can prioritize the acceptance of relevant photos by analyzing the user's social media activity. Social media activity is defined based on, for example, the content of posts, the number of likes, and comments.

[0073] The analysis unit can estimate the user's emotions and adjust the photo analysis method based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit adjusts the photo analysis method according to the user's emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis, carefully analyzing the atmosphere and color tones of the photo. If the user is in a hurry, the analysis unit can perform a simplified analysis, analyzing only the main features. If the user is emotionally unstable, the analysis unit can focus on analyzing features related to emotions. This allows the analysis unit to flexibly respond to the user's situation by adjusting the photo analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0074] The analysis unit can adjust the level of detail in its analysis based on the importance of each photograph. For example, it can analyze photographs of important events in detail, carefully examining the atmosphere and color tones. It can also perform a simpler analysis of everyday photographs, focusing only on key features. For photographs related to a specific theme, the analysis unit can perform a detailed analysis tailored to that theme. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of each photograph. Importance can be defined, for example, based on user ratings or the content of the photograph.

[0075] The analysis unit can apply different analysis algorithms depending on the category of the photograph during analysis. For example, the analysis unit can apply an algorithm that analyzes the natural colors and atmosphere to landscape photographs. The analysis unit can also apply an algorithm that analyzes the facial expressions and emotions of people to portrait photographs. The analysis unit can also apply an algorithm that analyzes the atmosphere and colors of events to event photographs. This allows the analysis unit to perform efficient analysis by applying different analysis algorithms depending on the category of the photograph. Categories are defined based on, for example, landscapes, people, events, etc.

[0076] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit determines the priority of analysis according to the user's emotions. For example, if the user is excited, the analysis unit will prioritize analyzing emotionally charged photos. If the user is calm, the analysis unit may also prioritize analyzing photos with a calm atmosphere. If the user is sad, the analysis unit may also prioritize analyzing comforting photos. In this way, the analysis unit can flexibly respond to the user's situation by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0077] The analysis unit can determine the priority of the analysis based on when the photos were taken. For example, the analysis unit may prioritize the analysis of recently taken photos. It can also prioritize the analysis of photos taken during a specific event period. It can also prioritize the analysis of photos taken in a specific season. This allows the analysis unit to perform efficient analysis by determining the priority of the analysis based on when the photos were taken. The shooting period can be defined, for example, based on the season or time of day.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the photos during the analysis process. For example, the analysis unit can analyze photos taken at the same event together. It can also analyze photos related to the same theme together. It can also prioritize the analysis of photos related to the user's areas of interest. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the photos. Relevance is defined, for example, based on similarity of content or a common theme.

[0079] The generation unit can estimate the user's emotions and adjust the music generation method based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can calculate an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate their emotions using voice analysis technology. The generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit adjusts the music generation method according to the user's emotions. For example, if the user is relaxed, the generation unit can generate a song with a relaxed melody. If the user is excited, the generation unit can also generate an up-tempo song. If the user is sad, the generation unit can also generate a song with a calm melody. In this way, the generation unit can flexibly respond to the user's situation by adjusting the music generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0080] The music generator can adjust the level of detail in a song based on the characteristics of the photograph during music generation. For example, it can generate a pop-style song for a brightly colored photograph. It can also generate a classical song for a mutedly colored photograph. It can even generate a song that reflects the emotion of an emotional photograph. This allows the generator to flexibly adapt to the user's situation by adjusting the level of detail in the song based on the characteristics of the photograph. Characteristics are defined based on, for example, color, shape, pattern, and type of object.

[0081] The generation unit can apply different generation algorithms depending on the category of the photograph when generating music. For example, for landscape photographs, the generation unit can generate music that incorporates natural sounds. For portrait photographs, the generation unit can also generate music that reflects the emotions of the person. For event photographs, the generation unit can generate music that reflects the atmosphere of the event. This allows the generation unit to efficiently generate music by applying different generation algorithms depending on the category of the photograph. Categories are defined based on, for example, landscapes, people, events, etc.

[0082] The generation unit can estimate the user's emotions and adjust the length of the music based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can calculate an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate their emotions using voice analysis technology. The generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit adjusts the length of the music according to the user's emotions. For example, if the user is relaxed, the generation unit will generate a longer piece of music. If the user is in a hurry, the generation unit can generate a shorter piece of music. If the user is emotionally unstable, the generation unit can generate a piece of music of a length that matches their emotions. This allows the generation unit to flexibly respond to the user's situation by adjusting the length of the music according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The generation unit can determine the priority of songs based on when the photos were taken during song generation. For example, the generation unit can prioritize songs based on recently taken photos. The generation unit can also prioritize songs based on photos taken during a specific event period. The generation unit can also prioritize songs based on photos taken in each season. This allows the generation unit to efficiently generate songs by prioritizing songs based on when the photos were taken. The shooting period can be defined, for example, based on the season or time of day.

[0084] The generation unit can adjust the order of songs based on the relevance of the photos during song generation. For example, the generation unit can group songs based on photos taken at the same event. The generation unit can also group songs based on photos related to the same theme. The generation unit can also prioritize the generation of songs based on photos related to the user's areas of interest. This allows the generation unit to efficiently generate songs by adjusting the order of songs based on the relevance of the photos. Relevance can be defined, for example, based on similarity of content or a common theme.

[0085] The service provider can estimate the user's emotions and adjust the way music is delivered based on those estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can calculate an emotion score based on changes in facial expressions. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. The service provider can analyze the tone and speed of the voice and calculate an emotion score. The service provider adjusts the way music is delivered according to the user's emotions. For example, if the user is relaxed, the service provider will deliver music at a relaxed pace. If the user is in a hurry, the service provider can deliver music quickly. If the user is emotionally unstable, the service provider can deliver music in a way that matches their emotions. This allows the service provider to flexibly respond to the user's situation by adjusting the way music is delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The service provider can select the optimal service delivery method by referring to the user's past music usage history when providing music. For example, the service provider may prioritize the service delivery method for music that the user has previously preferred. The service provider can also suggest the optimal service delivery method for a specific time period based on the user's past usage history. The service provider can also analyze the user's past usage history and select the most efficient service delivery method. In this way, the service provider can select the optimal service delivery method by referring to the user's past music usage history. Past music usage history is defined, for example, based on playback history and rating history.

[0087] The service provider can estimate the user's emotions and adjust the timing of music delivery based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can calculate an emotion score based on changes in facial expressions. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. The service provider can analyze the tone and speed of the voice and calculate an emotion score. The service provider adjusts the timing of music delivery according to the user's emotions. For example, if the user is relaxed, the service provider can flexibly set the timing of music delivery. If the user is in a hurry, the service provider can deliver music quickly. If the user is emotionally unstable, the service provider can deliver music at a timing that matches their emotions. In this way, the service provider can flexibly respond to the user's situation by adjusting the timing of music delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The distribution unit can select the optimal distribution method when providing music, taking into account the user's device information. For example, if the user is using a smartphone, the distribution unit will select a distribution method optimized for smartphones. If the user is using a tablet, the distribution unit can also select a distribution method optimized for tablets. If the user is using a smartwatch, the distribution unit can also select a distribution method optimized for smartwatches. This allows the distribution unit to respond flexibly to the user's situation by selecting the optimal distribution method considering the user's device information. Device information is defined, for example, based on the device type, OS, and screen size.

[0089] The evaluation unit can estimate the user's emotions and adjust the music evaluation method based on the estimated emotions. For example, the evaluation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The evaluation unit can calculate an emotion score based on changes in facial expressions. The evaluation unit can also record the user's voice and estimate their emotions using voice analysis technology. The evaluation unit can analyze the tone and speed of the voice and calculate an emotion score. The evaluation unit adjusts the music evaluation method according to the user's emotions. For example, if the user is relaxed, the evaluation unit can provide detailed evaluation items. If the user is in a hurry, the evaluation unit can provide simplified evaluation items. If the user is emotionally unstable, the evaluation unit can provide evaluation items tailored to their emotions. This allows the evaluation unit to flexibly respond to the user's situation by adjusting the music evaluation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The evaluation unit can select the optimal evaluation method when evaluating a song by referring to the user's past evaluation history. For example, the evaluation unit can suggest the optimal evaluation method based on the user's past evaluations. The evaluation unit can also prioritize providing specific evaluation items based on the user's past evaluation history. The evaluation unit can also analyze the user's past evaluation history and select the most efficient evaluation method. In this way, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history. Past evaluation history is defined, for example, based on evaluation scores and comments.

[0091] The evaluation unit can estimate the user's emotions and adjust the song evaluation criteria based on the estimated user emotions. For example, the evaluation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The evaluation unit can calculate an emotion score based on changes in facial expressions. The evaluation unit can also record the user's voice and estimate their emotions using voice analysis technology. The evaluation unit can analyze the tone and speed of the voice and calculate an emotion score. The evaluation unit adjusts the song evaluation criteria according to the user's emotions. For example, if the user is relaxed, the evaluation unit can provide detailed evaluation criteria. If the user is in a hurry, the evaluation unit can provide simplified evaluation criteria. If the user is emotionally unstable, the evaluation unit can provide evaluation criteria tailored to their emotions. This allows the evaluation unit to flexibly respond to the user's situation by adjusting the song evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The evaluation unit can select the optimal evaluation method when evaluating music, taking into account the user's geographical location information. For example, if the user is in a specific region, the evaluation unit will provide evaluation items related to that region. If the user is traveling, the evaluation unit can also provide evaluation items related to the travel destination. If the user is participating in a specific event, the evaluation unit can also provide evaluation items related to that event. This allows the evaluation unit to respond flexibly to the user's situation by selecting the optimal evaluation method considering the user's geographical location information. Geographical location information is defined, for example, based on GPS data or location information services. The optimal evaluation method is selected, for example, based on the user's past behavior patterns or current situation.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The reception system can refer to the user's past photo upload history when accepting a user's photos and suggest the most suitable reception method. For example, if a user frequently uploaded photos during a specific time period in the past, the reception system can suggest the most suitable reception method for that time period. Furthermore, the reception system can analyze the types of photos the user has uploaded in the past and prioritize the acceptance of similar photos. This allows the reception system to provide the most suitable reception method based on the user's past behavioral patterns.

[0095] The generation unit can estimate the user's emotions when evaluating the quality of the generated music and adjust the evaluation criteria based on those emotions. For example, if the user is relaxed, it can provide detailed evaluation criteria, and if the user is in a hurry, it can provide simplified criteria. Furthermore, if the user is emotionally unstable, it can provide evaluation criteria tailored to those emotions. This allows the generation unit to flexibly respond to the user's situation by adjusting the music evaluation criteria according to the user's emotions.

[0096] The reception desk can estimate the user's emotions and adjust the timing of photo submission based on those estimates. For example, if the user is relaxed, the reception desk can flexibly set the photo submission timing, allowing the user to upload photos at their preferred time. If the user is busy, the reception desk can shorten the photo submission timing to allow for quicker uploads. This allows the reception desk to respond flexibly to the user's situation by adjusting the photo submission timing according to the user's emotions.

[0097] The analysis unit can estimate the user's emotions when analyzing a photograph and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis, carefully examining the atmosphere and color tones of the photograph. If the user is in a hurry, it can perform a simplified analysis, focusing only on the main features. This allows the analysis unit to adjust the photo analysis method according to the user's emotions, enabling a flexible response tailored to the user's situation.

[0098] The generation unit can estimate the user's emotions during music generation and adjust the music generation method based on the estimated emotions. For example, if the user is relaxed, it can generate a song with a relaxed melody. If the user is excited, it can also generate an up-tempo song. In this way, the generation unit can flexibly respond to the user's situation by adjusting the music generation method according to the user's emotions.

[0099] The reception desk can prioritize receiving photos that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving photos related to that region. If the user is traveling, it can also prioritize receiving photos related to their travel destination. This allows the reception desk to respond flexibly to the user's situation by prioritizing highly relevant photos based on the user's geographical location.

[0100] The analysis unit can apply different analysis algorithms depending on the category of the photograph. For example, an algorithm that analyzes natural colors and atmosphere can be applied to landscape photographs. An algorithm that analyzes the facial expressions and emotions of the person can also be applied to portrait photographs. This allows the analysis unit to perform efficient analysis by applying different analysis algorithms depending on the category of the photograph.

[0101] The generation unit can prioritize songs based on when the photos were taken. For example, it can prioritize generating songs based on recently taken photos. It can also prioritize generating songs based on photos taken during a specific event period. This allows the generation unit to efficiently generate songs by prioritizing them based on when the photos were taken.

[0102] The service provider can select the optimal delivery method by considering the user's device information. For example, if the user is using a smartphone, it can select a delivery method optimized for smartphones. If the user is using a tablet, it can also select a delivery method optimized for tablets. This allows the service provider to respond flexibly to the user's situation by selecting the optimal delivery method considering the user's device information.

[0103] The evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history when evaluating a song. For example, it can suggest the optimal evaluation method based on the user's past evaluations. It can also prioritize providing specific evaluation items based on the user's past evaluation history. In this way, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The reception desk receives photos uploaded by users. The reception desk allows users to upload photos through the website or application, and can also check the format and size of the photos and convert them to the appropriate format. Step 2: The analysis unit detects the characteristics of the photograph received by the reception unit and analyzes its atmosphere, color tone, and emotions. The analysis unit can analyze the color distribution and shape of the photograph using image processing technology and estimate the emotions of the people in the photograph using face recognition technology. It can also analyze emotions from the color tone of the photograph based on color psychology. Step 3: The generation unit generates music based on the results analyzed by the analysis unit. The generation unit uses generation AI to create the genre, melody, harmony, and rhythm of the music, and can adjust the tempo and tone of the music to match the atmosphere of the photograph. It can also include an evaluation unit that evaluates the quality of the music and makes corrections as needed. Step 4: The distribution unit provides the music generated by the generation unit to the user. The distribution unit can provide the music in streaming or download format, and can provide the music in a format optimized for the user's device.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, enabling the user to upload photos through a website or application. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, detecting the features of the photo and analyzing its atmosphere, color, and emotion. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, generating music based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14, providing the generated music to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, enabling the user to upload photos through a website or application. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, detecting the features of the photo and analyzing its atmosphere, color tone, and emotion. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, generating music based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214, providing the generated music to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, enabling the user to upload photos through a website or application. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, detecting the features of the photo and analyzing its atmosphere, color tone, and emotion. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, generating music based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314, providing the generated music to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0150] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, enabling users to upload photos through a website or application. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, detecting features of the photo and analyzing its atmosphere, color tone, and emotion. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, generating music based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the robot 414, providing the generated music to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0159] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0161] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0162] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0163] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0167] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0168] 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.

[0169] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0170] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0171] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0172] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0174] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0175] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0176] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0177] (Note 1) A reception area that accepts photos uploaded by users, An analysis unit detects the characteristics of the photograph received by the reception unit and analyzes the atmosphere, color tone, and emotion, A generation unit that generates music based on the results of analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides music generated by the generation unit. A system characterized by the following features. (Note 2) It also includes an evaluation unit that assesses the quality of the generated music and makes corrections as needed. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of photo submissions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system analyzes the user's past photo upload history and selects the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When receiving photos, the system filters them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the photos to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving photos, the system prioritizes accepting photos that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving photos, the system analyzes the user's social media activity and accepts relevant photos. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the photo analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the photographs. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the photograph. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the photos were taken. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the photos. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the music generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating music, adjust the level of detail in the music based on the characteristics of the photograph. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating music, different generation algorithms are applied depending on the category of the photo. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the length of the song based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating music, the priority of songs is determined based on when the photos were taken. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating music, the order of the songs is adjusted based on the relevance of the photos. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way music is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing music, the system selects the most suitable method of provision by referring to the user's past music usage history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of song delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing music, the optimal delivery method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, It estimates the user's emotions and adjusts the song evaluation method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The evaluation unit, When evaluating a song, the system selects the optimal evaluation method by referring to the user's past evaluation history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The evaluation unit, It estimates the user's emotions and adjusts the song evaluation criteria based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The evaluation unit, When evaluating music, the system selects the optimal evaluation method by taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts photos uploaded by users, An analysis unit detects the characteristics of the photograph received by the reception unit and analyzes the atmosphere, color tone, and emotion, A generation unit that generates music based on the results of analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides music generated by the generation unit. A system characterized by the following features.

2. It also includes an evaluation unit that assesses the quality of the generated music and makes corrections as needed. The system according to feature 1.

3. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of photo submissions based on those emotions. The system according to feature 1.

4. The aforementioned reception unit is The system analyzes the user's past photo upload history and selects the most suitable submission method. The system according to feature 1.

5. The aforementioned reception unit is When receiving photos, the system filters them based on the user's current projects and areas of interest. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the photos to be accepted based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is When receiving photos, the system prioritizes accepting photos that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

8. The aforementioned reception unit is When receiving photos, the system analyzes the user's social media activity and accepts relevant photos. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the photo analysis method based on those estimated emotions. The system according to feature 1.

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