System

Abstract

An object of a system according to an embodiment is to generate an effective sound effect at low cost.SOLUTION: A system includes a reception unit, a generation unit, and a reproduction unit. The reception unit inputs scene information. The generation unit analyzes the scene information input by the reception unit and generates a sound effect. The reproduction unit reproduces the sound effect generated by the generation unit in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that generating sound effects requires expert knowledge and is expensive, making it difficult to generate effective sound effects at low cost.

[0005] The system according to the embodiment aims to generate effective sound effects at low cost. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a playback unit. The reception unit inputs scene information. The generation unit analyzes the scene information input by the reception unit and generates sound effects. The playback unit plays the sound effects generated by the generation unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can generate effective sound effects at low cost. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A Sound Effector system according to an embodiment of the present invention is a system in which scene information is input, and a generation AI analyzes the input to generate appropriate sound effects and plays them in real time. In the Sound Effector system, a user inputs a scene for which they want to generate sound effects. The generation AI analyzes the scene information, generates the sound effects, and plays them in real time. For example, in the Sound Effector system, a user can input scene information using text, images, or videos. The generation AI then analyzes the input scene information and generates appropriate sound effects. The generation AI determines the type, volume, timing, etc. of sound effects based on the content and atmosphere of the scene. For example, intense sounds and explosions are generated in battle scenes, while cheers and applause are generated in exciting scenes. The generated sound effects are played in real time. This allows users to easily generate and use sound effects tailored to specific scenes. For example, a game developer can generate sound effects tailored to battle scenes and use them in the game. Furthermore, an event organizer can generate sound effects tailored to exciting scenes and use them in the event. This allows the Sound Effector system to significantly reduce the cost of generating sound effects. It eliminates the need for experts to create and record sound effects, and because the AI ​​automatically generates them, it is possible to provide high-quality sound effects at low cost.In addition, users can easily create and use sound effects that match the scene, adding even more color to the entertainment and event industries.

[0029] A sound effector system according to an embodiment includes a reception unit, a generation unit, and a playback unit. The reception unit inputs scene information for which a user wants to generate a sound effect. Scene information includes, but is not limited to, text, images, and videos. The reception unit supports, for example, both a user inputting a text description of a scene and a user inputting images and videos. The generation unit uses a generation AI to analyze the scene information input by the reception unit and generate appropriate sound effects. The generation unit, for example, analyzes a scene based on a pre-trained database and generates sound effects. The generation unit can also adjust sound effect parameters based on the atmosphere and tempo of the scene. For example, the generation AI determines the type, volume, timing, etc. of the sound effect based on the content and atmosphere of the scene. The playback unit plays the sound effects generated by the generation unit in real time. The playback unit supports, for example, both a method of streaming sound effects generated on the cloud and a method of downloading and playing the sound effects on a local device. This allows the sound effector system according to an embodiment to input and analyze scene information, generate appropriate sound effects, and play them in real time.

[0030] The reception unit can support both a method in which a user inputs a scene description in text and a method in which a user inputs an image or video. The reception unit supports, for example, a method in which a user inputs a scene description in text. For example, natural language processing technology can be used to analyze the text input by the user and recognize it as scene information. The reception unit can also support a method in which a user inputs an image or video. For example, image recognition technology or video analysis technology can be used to analyze the image or video input by the user and recognize it as scene information. This allows the user to input scene information in the form of text, image, or video. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the reception unit can input the text, image, or video input by the user to the generation AI and have the generation AI analyze the scene information.

[0031] The generation unit can analyze a scene based on a pre-trained database and generate sound effects. The generation unit, for example, analyzes a scene based on a pre-trained database. For example, the generation unit determines the type, volume, timing, etc. of sound effects based on the content and atmosphere of the scene. The generation unit can also adjust sound effect parameters based on the atmosphere and tempo of the scene. For example, the generation unit analyzes the atmosphere of the scene and generates appropriate sound effects. Furthermore, the generation unit can analyze the tempo of the scene and adjust the playback speed of the sound effects. In this way, the scene can be analyzed based on the pre-trained database and appropriate sound effects can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input a pre-trained database into the generation AI and cause the generation AI to analyze the scene and generate sound effects.

[0032] The playback unit can support both streaming sound effects generated on the cloud and downloading them to a local device for playback. The playback unit supports, for example, streaming sound effects generated on the cloud. For example, the playback unit can stream sound effects from a cloud server via the Internet. The playback unit can also support downloading them to a local device for playback. For example, the playback unit can download sound effects generated on the cloud to a local device and play them offline. This supports both streaming sound effects generated on the cloud and downloading them to a local device for playback. Some or all of the above-described processing in the playback unit may be performed using, or without, a generation AI. For example, the playback unit can input sound effects generated on the cloud to the generation AI and cause the generation AI to perform streaming playback or download playback.

[0033] The generation unit can adjust the parameters of the sound effects based on the atmosphere or tempo of a scene. The generation unit, for example, analyzes the atmosphere of a scene and adjusts the parameters of the sound effects. For example, the generation unit can adjust the volume and effects of the sound effects based on the atmosphere of the scene. The generation unit can also analyze the tempo of a scene and adjust the playback speed of the sound effects. For example, the generation unit can speed up or slow down the playback speed of the sound effects based on the tempo of the scene. This allows the parameters of the sound effects to be adjusted based on the atmosphere and tempo of the scene. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the atmosphere and tempo of a scene into the generation AI and cause the generation AI to adjust the parameters of the sound effects.

[0034] The reception unit can analyze the user's past scene information input history and suggest an input method. The reception unit, for example, analyzes the user's past scene information input history and suggests an optimal input method. For example, the reception unit can automatically display scene information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (text, image, video, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest scene information to be used in a specific time period from the user's past input history. This makes it possible to analyze the user's past scene information input history and suggest an optimal input method. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past scene information input history into the generation AI and have the generation AI suggest an input method.

[0035] The reception unit may filter scene information based on the user's current project or field of interest when the scene information is input. For example, the reception unit may preferentially display scene information related to a project the user is currently working on. The reception unit may also filter and display related scene information based on the user's field of interest. Furthermore, the reception unit may suggest related scene information by referring to the user's past project history. This allows the scene information to be filtered based on the user's current project or field of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input data on the user's current project or field of interest into the generation AI and cause the generation AI to filter the scene information.

[0036] The reception unit can select the optimal input means depending on the user's input method when inputting scene information. For example, when inputting scene information, the reception unit selects the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit can input the scene information using voice recognition technology. Furthermore, if the user selects image or video input, the reception unit can analyze the scene information using image recognition technology. Furthermore, if the user selects text input, the reception unit can analyze the scene information using natural language processing technology. This makes it possible to select the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input method to the generation AI and cause the generation AI to select the optimal input means.

[0037] The reception unit can prioritize inputting highly relevant scene information in consideration of the user's geographical location information when inputting scene information. For example, the reception unit can prioritize inputting highly relevant scene information in consideration of the user's geographical location information when inputting scene information. For example, the reception unit can prioritize displaying scene information related to the user's current location. The reception unit can also filter and display related scene information based on the user's geographical location information. Furthermore, the reception unit can suggest related scene information by referring to the user's past location information. This allows highly relevant scene information to be prioritized in consideration of the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to filter the scene information.

[0038] The reception unit can analyze the user's social media activity when scene information is input and input related scene information. For example, the reception unit can analyze the user's social media activity when scene information is input and input related scene information. For example, the reception unit can suggest scene information related to a place where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related scene information. Furthermore, the reception unit can suggest related scene information by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related scene information can be input. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to suggest scene information.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting scene information. The reception unit customizes the input method by reflecting the user's past feedback when inputting scene information, for example. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by referring to the user's past feedback. Furthermore, the reception unit can reflect the user's feedback in real time and improve the input method. This makes it possible to customize the input method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0040] The generation unit can adjust the level of detail of the sound effect based on the importance of the scene when generating the sound effect. For example, the generation unit can generate detailed and complex sound effects for important scenes. The generation unit can also generate simple sound effects for general scenes. The generation unit can also adjust the volume and effects of the sound effect according to the importance of the scene. This allows the level of detail of the sound effect to be adjusted based on the importance of the scene. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input scene importance data to the generation AI and cause the generation AI to adjust the level of detail of the sound effect.

[0041] The generation unit can apply different generation algorithms depending on the scene category when generating sound effects. For example, the generation unit can apply different generation algorithms depending on the scene category when generating sound effects. For example, the generation unit can apply an algorithm that generates intense sounds to a battle scene. The generation unit can also apply an algorithm that generates humorous sounds to a comedy scene. Furthermore, the generation unit can apply an algorithm that generates sounds that enhance emotions to a drama scene. This allows different generation algorithms to be applied depending on the scene category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input scene category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0042] The generation unit can improve the accuracy of sound effects generation by referring to the user's past generation results. For example, the generation unit can improve the accuracy of sound effects generation by referring to the user's past generation results. For example, the generation unit can suggest optimal sound effects based on sound effects generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation algorithm. Furthermore, the generation unit can improve the accuracy of generation based on user feedback. This improves the accuracy of generation by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] The generation unit can determine the priority of sound effects based on the submission time of the scenes when generating sound effects. The generation unit, for example, determines the priority of sound effects based on the submission time of the scenes when generating sound effects. For example, the generation unit can prioritize generating sound effects for scenes whose deadlines are approaching. The generation unit can also postpone generating sound effects for scenes whose submission time is further away. Furthermore, the generation unit can adjust the order in which sound effects are generated depending on the submission time. This allows the priority of sound effects to be determined based on the submission time of the scenes. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input scene submission time data into the generation AI and have the generation AI determine the priority of sound effects.

[0044] The generation unit can adjust the order of sound effects based on the relevance of scenes when generating sound effects. The generation unit, for example, adjusts the order of sound effects based on the relevance of scenes when generating sound effects. For example, the generation unit can prioritize generating sound effects for highly relevant scenes. The generation unit can also postpone generating sound effects for less relevant scenes. Furthermore, the generation unit can adjust the order of sound effects generation based on the relevance of scenes. This allows the order of sound effects to be adjusted based on the relevance of scenes. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input scene relevance data to the generation AI and cause the generation AI to adjust the order of sound effects.

[0045] The generation unit can adjust the use of technical terms for sound effects in accordance with the user's level of expertise when generating sound effects. For example, the generation unit can adjust the use of technical terms for sound effects in accordance with the user's level of expertise when generating sound effects. For example, the generation unit can generate sound effects using detailed technical terms for users with high levels of expertise. The generation unit can also generate sound effects using simple terms for users with low levels of expertise. Furthermore, the generation unit can adjust the description of the sound effects in accordance with the user's level of expertise. This allows the use of technical terms for sound effects to be adjusted in accordance with the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms for sound effects.

[0046] The playback unit can select the optimal playback method by referring to the user's past playback history when playing sound effects. For example, the playback unit can select the optimal playback method by referring to the user's past playback history when playing sound effects. For example, the playback unit can suggest the optimal playback method based on the user's favorite sound effects played in the past. The playback unit can also analyze the user's past playback history and improve the playback algorithm. Furthermore, the playback unit can improve playback accuracy based on user feedback. This allows the optimal playback method to be selected by referring to the user's past playback history. Some or all of the above-described processing in the playback unit can be performed using, or without, a generation AI. For example, the playback unit can input the user's past playback history data into the generation AI and have the generation AI select the optimal playback method.

[0047] The playback unit can customize the playback content according to the user's current task when playing back sound effects. For example, the playback unit customizes the playback content according to the user's current task when playing back sound effects. For example, when the user is working, the playback unit can play a sound effect that increases concentration. Furthermore, when the user is relaxing, the playback unit can play a sound effect that has a relaxing effect. Furthermore, when the user is exercising, the playback unit can play an energetic sound effect. This allows the playback content to be customized according to the user's current task. Some or all of the above-described processing in the playback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the playback unit can input the user's current task data into the generation AI and have the generation AI customize the playback content.

[0048] The playback unit can improve the playback method by reflecting user feedback when playing back sound effects. For example, the playback unit can improve the playback method by reflecting user feedback when playing back sound effects. For example, the playback unit can adjust the playback method based on feedback provided by the user. The playback unit can also reflect user feedback in real time to improve playback accuracy. Furthermore, the playback unit can improve the playback algorithm by referring to past user feedback. This allows the playback method to be improved by reflecting user feedback. Some or all of the above-mentioned processing in the playback unit can be performed using, or without, a generation AI. For example, the playback unit can input user feedback data into the generation AI and cause the generation AI to improve the playback method.

[0049] The playback unit can select the optimal playback method when playing sound effects by taking into account the user's device information. For example, when playing sound effects, the playback unit selects the optimal playback method by taking into account the user's device information. For example, if the user is using a smartphone, the playback unit can provide a playback method optimized for the device's speakers. Furthermore, if the user is using headphones, the playback unit can provide a playback method that emphasizes the stereo effect. Furthermore, if the user is using a home theater system, the playback unit can provide a playback method optimized for surround sound. This allows the optimal playback method to be selected by taking into account the user's device information. Some or all of the above-described processing in the playback unit may be performed using, or without, a generation AI. For example, the playback unit can input the user's device information into the generation AI and cause the generation AI to select the optimal playback method.

[0050] The playback unit can make the playback content multilingual in accordance with the user's language setting when playing back sound effects. For example, the playback unit can automatically set a description of the sound effect based on the language setting of the user's device. The playback unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the playback unit can provide a description of the sound effect in that language. This allows the playback content to be multilingual in accordance with the user's language setting. Some or all of the above-described processing in the playback unit can be performed using, or without, a generation AI. For example, the playback unit can input the user's language setting data into the generation AI and cause the generation AI to execute multilingual playback content.

[0051] The playback unit can adjust the playback volume taking into account the user's environmental sounds when playing back sound effects. For example, the playback unit can adjust the playback volume taking into account the user's environmental sounds when playing back sound effects. For example, the playback unit can set the volume of the sound effects low when the user is in a quiet environment. Furthermore, the playback unit can set the volume of the sound effects high when the user is in a noisy environment. Furthermore, the playback unit can detect the user's environmental sounds in real time and adjust the volume to an optimal level. This allows the playback volume to be adjusted taking into account the user's environmental sounds. Some or all of the above-described processing in the playback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the playback unit can input the user's environmental sound data into the generation AI and cause the generation AI to adjust the playback volume.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The reception unit can analyze the user's past scene information input history and suggest an input method. For example, the reception unit can automatically display scene information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (text, image, video, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest scene information to be used in a specific time period based on the user's past input history. This makes it possible to analyze the user's past scene information input history and suggest the optimal input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past scene information input history into the generation AI and have the generation AI suggest an input method.

[0054] When generating sound effects, the generation unit can apply different generation algorithms depending on the scene category. For example, the generation unit can apply an algorithm that generates intense sounds to a battle scene. The generation unit can also apply an algorithm that generates humorous sounds to a comedy scene. The generation unit can also apply an algorithm that generates emotionally appealing sounds to a dramatic scene. This allows different generation algorithms to be applied depending on the scene category. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input scene category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0055] When playing sound effects, the playback unit can select the optimal playback method by taking into account the user's device information. For example, if the user is using a smartphone, the playback unit can provide a playback method optimized for the device's speakers. Furthermore, if the user is using headphones, the playback unit can provide a playback method that emphasizes the stereo effect. Furthermore, if the user is using a home theater system, the playback unit can provide a playback method optimized for surround sound. This allows the optimal playback method to be selected by taking into account the user's device information. Some or all of the above-described processing in the playback unit may be performed using, or without, a generation AI. For example, the playback unit can input the user's device information into the generation AI and cause the generation AI to select the optimal playback method.

[0056] When generating sound effects, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. For example, the generation unit can suggest optimal sound effects based on sound effects generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation algorithm. Furthermore, the generation unit can improve the accuracy of the generation based on user feedback. This improves the accuracy of the generation by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0057] The playback unit can adjust the playback volume taking into account the user's environmental sounds when playing back sound effects. For example, the playback unit can set the volume of the sound effects low when the user is in a quiet environment. Also, the playback unit can set the volume of the sound effects high when the user is in a noisy environment. Furthermore, the playback unit can detect the user's environmental sounds in real time and adjust the volume to an optimal level. This allows the playback volume to be adjusted taking into account the user's environmental sounds. Some or all of the above-described processing in the playback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the playback unit can input the user's environmental sound data into the generation AI and have the generation AI adjust the playback volume.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives scene information for which the user wants to generate sound effects. The scene information includes text, images, videos, etc. The reception unit supports both a method in which the user inputs a description of the scene in text and a method in which the user inputs images or videos. Step 2: The generator uses the AI ​​to analyze the scene information input by the receiver and generate appropriate sound effects. The generator analyzes the scene based on a pre-trained database and can adjust the parameters of the sound effects based on the atmosphere and tempo of the scene. For example, it determines the type, volume, and timing of sound effects based on the content and atmosphere of the scene. Step 3: The playback unit plays the sound effects generated by the generation unit in real time. The playback unit supports both streaming the sound effects generated on the cloud and downloading them to a local device for playback.

[0060] (Example 2) A Sound Effector system according to an embodiment of the present invention is a system in which scene information is input, and a generation AI analyzes the input to generate appropriate sound effects and plays them in real time. In the Sound Effector system, a user inputs a scene for which they want to generate sound effects. The generation AI analyzes the scene information, generates the sound effects, and plays them in real time. For example, in the Sound Effector system, a user can input scene information using text, images, or videos. The generation AI then analyzes the input scene information and generates appropriate sound effects. The generation AI determines the type, volume, timing, etc. of sound effects based on the content and atmosphere of the scene. For example, intense sounds and explosions are generated in battle scenes, while cheers and applause are generated in exciting scenes. The generated sound effects are played in real time. This allows users to easily generate and use sound effects tailored to specific scenes. For example, a game developer can generate sound effects tailored to battle scenes and use them in the game. Furthermore, an event organizer can generate sound effects tailored to exciting scenes and use them in the event. This allows the Sound Effector system to significantly reduce the cost of generating sound effects. It eliminates the need for experts to create and record sound effects, and because the AI ​​automatically generates them, it is possible to provide high-quality sound effects at low cost.In addition, users can easily create and use sound effects that match the scene, adding even more color to the entertainment and event industries.

[0061] A sound effector system according to an embodiment includes a reception unit, a generation unit, and a playback unit. The reception unit inputs scene information for which a user wants to generate a sound effect. Scene information includes, but is not limited to, text, images, and videos. The reception unit supports, for example, both a user inputting a text description of a scene and a user inputting images and videos. The generation unit uses a generation AI to analyze the scene information input by the reception unit and generate appropriate sound effects. The generation unit, for example, analyzes a scene based on a pre-trained database and generates sound effects. The generation unit can also adjust sound effect parameters based on the atmosphere and tempo of the scene. For example, the generation AI determines the type, volume, timing, etc. of the sound effect based on the content and atmosphere of the scene. The playback unit plays the sound effects generated by the generation unit in real time. The playback unit supports, for example, both a method of streaming sound effects generated on the cloud and a method of downloading and playing the sound effects on a local device. This allows the sound effector system according to an embodiment to input and analyze scene information, generate appropriate sound effects, and play them in real time.

[0062] The reception unit can support both a method in which a user inputs a scene description in text and a method in which a user inputs an image or video. The reception unit supports, for example, a method in which a user inputs a scene description in text. For example, natural language processing technology can be used to analyze the text input by the user and recognize it as scene information. The reception unit can also support a method in which a user inputs an image or video. For example, image recognition technology or video analysis technology can be used to analyze the image or video input by the user and recognize it as scene information. This allows the user to input scene information in the form of text, image, or video. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the reception unit can input the text, image, or video input by the user to the generation AI and have the generation AI analyze the scene information.

[0063] The generation unit can analyze a scene based on a pre-trained database and generate sound effects. The generation unit, for example, analyzes a scene based on a pre-trained database. For example, the generation unit determines the type, volume, timing, etc. of sound effects based on the content and atmosphere of the scene. The generation unit can also adjust sound effect parameters based on the atmosphere and tempo of the scene. For example, the generation unit analyzes the atmosphere of the scene and generates appropriate sound effects. Furthermore, the generation unit can analyze the tempo of the scene and adjust the playback speed of the sound effects. In this way, the scene can be analyzed based on the pre-trained database and appropriate sound effects can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input a pre-trained database into the generation AI and cause the generation AI to analyze the scene and generate sound effects.

[0064] The playback unit can support both streaming sound effects generated on the cloud and downloading them to a local device for playback. The playback unit supports, for example, streaming sound effects generated on the cloud. For example, the playback unit can stream sound effects from a cloud server via the Internet. The playback unit can also support downloading them to a local device for playback. For example, the playback unit can download sound effects generated on the cloud to a local device and play them offline. This supports both streaming sound effects generated on the cloud and downloading them to a local device for playback. Some or all of the above-described processing in the playback unit may be performed using, or without, a generation AI. For example, the playback unit can input sound effects generated on the cloud to the generation AI and cause the generation AI to perform streaming playback or download playback.

[0065] The generation unit can adjust the parameters of the sound effects based on the atmosphere or tempo of a scene. The generation unit, for example, analyzes the atmosphere of a scene and adjusts the parameters of the sound effects. For example, the generation unit can adjust the volume and effects of the sound effects based on the atmosphere of the scene. The generation unit can also analyze the tempo of a scene and adjust the playback speed of the sound effects. For example, the generation unit can speed up or slow down the playback speed of the sound effects based on the tempo of the scene. This allows the parameters of the sound effects to be adjusted based on the atmosphere and tempo of the scene. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the atmosphere and tempo of a scene into the generation AI and cause the generation AI to adjust the parameters of the sound effects.

[0066] The reception unit can estimate the user's emotion and specifically adjust the scene information input method based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the scene information input method based on the estimated emotion. For example, if the user is excited, the reception unit can provide a simple and intuitive interface to allow the user to quickly input scene information. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable scene information input method. Furthermore, if the user is stressed, the reception unit can minimize input steps and provide a simple interface. This allows the scene information input method to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the scene information input method.

[0067] The reception unit can analyze the user's past scene information input history and suggest an input method. The reception unit, for example, analyzes the user's past scene information input history and suggests an optimal input method. For example, the reception unit can automatically display scene information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (text, image, video, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest scene information to be used in a specific time period from the user's past input history. This makes it possible to analyze the user's past scene information input history and suggest an optimal input method. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past scene information input history into the generation AI and have the generation AI suggest an input method.

[0068] The reception unit may filter scene information based on the user's current project or field of interest when the scene information is input. For example, the reception unit may preferentially display scene information related to a project the user is currently working on. The reception unit may also filter and display related scene information based on the user's field of interest. Furthermore, the reception unit may suggest related scene information by referring to the user's past project history. This allows the scene information to be filtered based on the user's current project or field of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input data on the user's current project or field of interest into the generation AI and cause the generation AI to filter the scene information.

[0069] The reception unit can select the optimal input means depending on the user's input method when inputting scene information. For example, when inputting scene information, the reception unit selects the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit can input the scene information using voice recognition technology. Furthermore, if the user selects image or video input, the reception unit can analyze the scene information using image recognition technology. Furthermore, if the user selects text input, the reception unit can analyze the scene information using natural language processing technology. This makes it possible to select the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input method to the generation AI and cause the generation AI to select the optimal input means.

[0070] The reception unit can estimate the user's emotion and specifically determine the priority of the input scene information based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the input scene information based on the estimated emotion. For example, if the user is excited, the reception unit can prioritize processing scene information with high importance. Also, if the user is relaxed, the reception unit can prioritize processing detailed scene information. Furthermore, if the user is stressed, the reception unit can prioritize processing simple scene information. This allows the priority of scene information to be determined based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the scene information.

[0071] The reception unit can prioritize inputting highly relevant scene information in consideration of the user's geographical location information when inputting scene information. For example, the reception unit can prioritize inputting highly relevant scene information in consideration of the user's geographical location information when inputting scene information. For example, the reception unit can prioritize displaying scene information related to the user's current location. The reception unit can also filter and display related scene information based on the user's geographical location information. Furthermore, the reception unit can suggest related scene information by referring to the user's past location information. This allows highly relevant scene information to be prioritized in consideration of the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to filter the scene information.

[0072] The reception unit can analyze the user's social media activity when scene information is input and input related scene information. For example, the reception unit can analyze the user's social media activity when scene information is input and input related scene information. For example, the reception unit can suggest scene information related to a place where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related scene information. Furthermore, the reception unit can suggest related scene information by referring to the activity of the user's friends on social media. In this way, the user's social media activity can be analyzed and related scene information can be input. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to suggest scene information.

[0073] The reception unit can customize the input method by reflecting the user's past feedback when inputting scene information. The reception unit customizes the input method by reflecting the user's past feedback when inputting scene information, for example. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface by referring to the user's past feedback. Furthermore, the reception unit can reflect the user's feedback in real time and improve the input method. This makes it possible to customize the input method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0074] The generation unit can estimate the user's emotion and specifically adjust the sound effect generation method based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the sound effect generation method based on the estimated emotion. For example, if the user is relaxed, the generation unit can generate a gentle sound effect. If the user is excited, the generation unit can generate an intense sound effect. If the user is sad, the generation unit can generate a melancholic sound effect. This allows the sound effect generation method to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the sound effect generation method.

[0075] The generation unit can adjust the level of detail of the sound effect based on the importance of the scene when generating the sound effect. For example, the generation unit can generate detailed and complex sound effects for important scenes. The generation unit can also generate simple sound effects for general scenes. The generation unit can also adjust the volume and effects of the sound effect according to the importance of the scene. This allows the level of detail of the sound effect to be adjusted based on the importance of the scene. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input scene importance data to the generation AI and cause the generation AI to adjust the level of detail of the sound effect.

[0076] The generation unit can apply different generation algorithms depending on the scene category when generating sound effects. For example, the generation unit can apply different generation algorithms depending on the scene category when generating sound effects. For example, the generation unit can apply an algorithm that generates intense sounds to a battle scene. The generation unit can also apply an algorithm that generates humorous sounds to a comedy scene. Furthermore, the generation unit can apply an algorithm that generates sounds that enhance emotions to a drama scene. This allows different generation algorithms to be applied depending on the scene category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input scene category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0077] The generation unit can improve the accuracy of sound effects generation by referring to the user's past generation results. For example, the generation unit can improve the accuracy of sound effects generation by referring to the user's past generation results. For example, the generation unit can suggest optimal sound effects based on sound effects generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation algorithm. Furthermore, the generation unit can improve the accuracy of generation based on user feedback. This improves the accuracy of generation by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0078] The generation unit can estimate the user's emotion and specifically adjust the length of the sound effect based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the sound effect based on the estimated emotion. For example, if the user is relaxed, the generation unit can generate a longer sound effect. If the user is in a hurry, the generation unit can generate a shorter sound effect. Furthermore, if the user is excited, the generation unit can generate a variety of sound effects. This allows the length of the sound effect to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the length of the sound effect.

[0079] The generation unit can determine the priority of sound effects based on the submission time of the scenes when generating sound effects. The generation unit, for example, determines the priority of sound effects based on the submission time of the scenes when generating sound effects. For example, the generation unit can prioritize generating sound effects for scenes whose deadlines are approaching. The generation unit can also postpone generating sound effects for scenes whose submission time is further away. Furthermore, the generation unit can adjust the order in which sound effects are generated depending on the submission time. This allows the priority of sound effects to be determined based on the submission time of the scenes. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input scene submission time data into the generation AI and have the generation AI determine the priority of sound effects.

[0080] The generation unit can adjust the order of sound effects based on the relevance of scenes when generating sound effects. The generation unit, for example, adjusts the order of sound effects based on the relevance of scenes when generating sound effects. For example, the generation unit can prioritize generating sound effects for highly relevant scenes. The generation unit can also postpone generating sound effects for less relevant scenes. Furthermore, the generation unit can adjust the order of sound effects generation based on the relevance of scenes. This allows the order of sound effects to be adjusted based on the relevance of scenes. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input scene relevance data to the generation AI and cause the generation AI to adjust the order of sound effects.

[0081] The generation unit can adjust the use of technical terms for sound effects in accordance with the user's level of expertise when generating sound effects. For example, the generation unit can adjust the use of technical terms for sound effects in accordance with the user's level of expertise when generating sound effects. For example, the generation unit can generate sound effects using detailed technical terms for users with high levels of expertise. The generation unit can also generate sound effects using simple terms for users with low levels of expertise. Furthermore, the generation unit can adjust the description of the sound effects in accordance with the user's level of expertise. This allows the use of technical terms for sound effects to be adjusted in accordance with the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms for sound effects.

[0082] The playback unit can estimate the user's emotion and specifically adjust the playback method of sound effects based on the estimated user emotion. The playback unit, for example, estimates the user's emotion and adjusts the playback method of sound effects based on the estimated emotion. For example, if the user is relaxed, the playback unit can play sound effects at a gentle volume. If the user is excited, the playback unit can play sound effects at a loud volume. If the user is sad, the playback unit can play sound effects at a quiet volume. This allows the playback method of sound effects to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the playback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the playback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the playback method of sound effects.

[0083] The playback unit can select the optimal playback method by referring to the user's past playback history when playing sound effects. For example, the playback unit can select the optimal playback method by referring to the user's past playback history when playing sound effects. For example, the playback unit can suggest the optimal playback method based on the user's favorite sound effects played in the past. The playback unit can also analyze the user's past playback history and improve the playback algorithm. Furthermore, the playback unit can improve playback accuracy based on user feedback. This allows the optimal playback method to be selected by referring to the user's past playback history. Some or all of the above-described processing in the playback unit can be performed using, or without, a generation AI. For example, the playback unit can input the user's past playback history data into the generation AI and have the generation AI select the optimal playback method.

[0084] The playback unit can customize the playback content according to the user's current task when playing back sound effects. For example, the playback unit customizes the playback content according to the user's current task when playing back sound effects. For example, when the user is working, the playback unit can play a sound effect that increases concentration. Furthermore, when the user is relaxing, the playback unit can play a sound effect that has a relaxing effect. Furthermore, when the user is exercising, the playback unit can play an energetic sound effect. This allows the playback content to be customized according to the user's current task. Some or all of the above-described processing in the playback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the playback unit can input the user's current task data into the generation AI and have the generation AI customize the playback content.

[0085] The playback unit can improve the playback method by reflecting user feedback when playing back sound effects. For example, the playback unit can improve the playback method by reflecting user feedback when playing back sound effects. For example, the playback unit can adjust the playback method based on feedback provided by the user. The playback unit can also reflect user feedback in real time to improve playback accuracy. Furthermore, the playback unit can improve the playback algorithm by referring to past user feedback. This allows the playback method to be improved by reflecting user feedback. Some or all of the above-mentioned processing in the playback unit can be performed using, or without, a generation AI. For example, the playback unit can input user feedback data into the generation AI and cause the generation AI to improve the playback method.

[0086] The playback unit can estimate the user's emotion and specifically adjust the playback order of sound effects based on the estimated user emotion. The playback unit, for example, estimates the user's emotion and adjusts the playback order of sound effects based on the estimated emotion. For example, if the user is relaxed, the playback unit can play calm sound effects first. Alternatively, if the user is excited, the playback unit can play intense sound effects first. Furthermore, if the user is sad, the playback unit can play melancholic sound effects first. This allows the playback order of sound effects to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the playback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the playback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the playback order of sound effects.

[0087] The playback unit can select the optimal playback method when playing sound effects by taking into account the user's device information. For example, when playing sound effects, the playback unit selects the optimal playback method by taking into account the user's device information. For example, if the user is using a smartphone, the playback unit can provide a playback method optimized for the device's speakers. Furthermore, if the user is using headphones, the playback unit can provide a playback method that emphasizes the stereo effect. Furthermore, if the user is using a home theater system, the playback unit can provide a playback method optimized for surround sound. This allows the optimal playback method to be selected by taking into account the user's device information. Some or all of the above-described processing in the playback unit may be performed using, or without, a generation AI. For example, the playback unit can input the user's device information into the generation AI and cause the generation AI to select the optimal playback method.

[0088] The playback unit can make the playback content multilingual in accordance with the user's language setting when playing back sound effects. For example, the playback unit can automatically set a description of the sound effect based on the language setting of the user's device. The playback unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the playback unit can provide a description of the sound effect in that language. This allows the playback content to be multilingual in accordance with the user's language setting. Some or all of the above-described processing in the playback unit can be performed using, or without, a generation AI. For example, the playback unit can input the user's language setting data into the generation AI and cause the generation AI to execute multilingual playback content.

[0089] The playback unit can adjust the playback volume taking into account the user's environmental sounds when playing back sound effects. For example, the playback unit can adjust the playback volume taking into account the user's environmental sounds when playing back sound effects. For example, the playback unit can set the volume of the sound effects low when the user is in a quiet environment. Furthermore, the playback unit can set the volume of the sound effects high when the user is in a noisy environment. Furthermore, the playback unit can detect the user's environmental sounds in real time and adjust the volume to an optimal level. This allows the playback volume to be adjusted taking into account the user's environmental sounds. Some or all of the above-described processing in the playback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the playback unit can input the user's environmental sound data into the generation AI and cause the generation AI to adjust the playback volume. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, and playback unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing a user to input scene information in the form of text, images, or video. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzing the scene information using a generation AI and generating appropriate sound effects. The playback unit is implemented, for example, by the output device 40 of the smart device 14, and playing back the generated sound effects in real time. For example, the playback unit supports both streaming sound effects generated on the cloud and downloading them to a local device for playback. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, generation unit, and playback unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input scene information in the form of text, images, or videos. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzing the scene information using a generation AI and generating appropriate sound effects. The playback unit is implemented, for example, by the speaker 240 of the smart glasses 214, and playing back the generated sound effects in real time. For example, the playback unit supports both streaming sound effects generated on the cloud and downloading them to a local device for playback. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and playback unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the user to input scene information in the form of text, images, or videos. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the scene information using a generation AI to generate appropriate sound effects. The playback unit is realized, for example, by the speaker 240 of the headset-type terminal 314, and plays the generated sound effects in real time. The playback unit supports, for example, both a method of streaming sound effects generated on the cloud and a method of downloading them to a local device and playing them back. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and playback unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, allowing a user to input scene information in the form of text, images, or videos. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the scene information using a generation AI and generates appropriate sound effects. The playback unit is realized, for example, by the speaker 240 of the robot 414, and plays the generated sound effects in real time. The playback unit supports both a method of streaming sound effects generated on the cloud and a method of downloading sound effects to a local device for playback, for example.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can analyze the user's past scene information input history and suggest an input method. For example, the reception unit can automatically display scene information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (text, image, video, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest scene information to be used in a specific time period based on the user's past input history. This makes it possible to analyze the user's past scene information input history and suggest the optimal input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past scene information input history into the generation AI and have the generation AI suggest an input method.

[0092] When generating sound effects, the generation unit can apply different generation algorithms depending on the scene category. For example, the generation unit can apply an algorithm that generates intense sounds to a battle scene. The generation unit can also apply an algorithm that generates humorous sounds to a comedy scene. The generation unit can also apply an algorithm that generates emotionally appealing sounds to a dramatic scene. This allows different generation algorithms to be applied depending on the scene category. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input scene category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0093] When playing sound effects, the playback unit can select the optimal playback method by taking into account the user's device information. For example, if the user is using a smartphone, the playback unit can provide a playback method optimized for the device's speakers. Furthermore, if the user is using headphones, the playback unit can provide a playback method that emphasizes the stereo effect. Furthermore, if the user is using a home theater system, the playback unit can provide a playback method optimized for surround sound. This allows the optimal playback method to be selected by taking into account the user's device information. Some or all of the above-described processing in the playback unit may be performed using, or without, a generation AI. For example, the playback unit can input the user's device information into the generation AI and cause the generation AI to select the optimal playback method.

[0094] When generating sound effects, the generation unit can improve the accuracy of the generation by referring to the user's past generation results. For example, the generation unit can suggest optimal sound effects based on sound effects generated by the user in the past. The generation unit can also analyze the user's past generation results and improve the generation algorithm. Furthermore, the generation unit can improve the accuracy of the generation based on user feedback. This improves the accuracy of the generation by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0095] The playback unit can adjust the playback volume taking into account the user's environmental sounds when playing back sound effects. For example, the playback unit can set the volume of the sound effects low when the user is in a quiet environment. Also, the playback unit can set the volume of the sound effects high when the user is in a noisy environment. Furthermore, the playback unit can detect the user's environmental sounds in real time and adjust the volume to an optimal level. This allows the playback volume to be adjusted taking into account the user's environmental sounds. Some or all of the above-described processing in the playback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the playback unit can input the user's environmental sound data into the generation AI and have the generation AI adjust the playback volume.

[0096] The reception unit can estimate the user's emotions and specifically adjust the scene information input method based on the estimated user emotions. For example, if the user is excited, the reception unit can provide a simple and intuitive interface to allow the user to quickly input scene information. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable scene information input method. Furthermore, if the user is stressed, the reception unit can minimize the input steps and provide a simple interface. This allows the scene information input method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the scene information input method.

[0097] The generation unit can estimate the user's emotion and specifically adjust the sound effect generation method based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate a gentle sound effect. If the user is excited, the generation unit can generate a violent sound effect. If the user is sad, the generation unit can generate a melancholic sound effect. This allows the sound effect generation method to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the sound effect generation method.

[0098] The playback unit can estimate the user's emotion and specifically adjust the playback method of sound effects based on the estimated user's emotion. For example, if the user is relaxed, the playback unit can play sound effects at a gentle volume. Furthermore, if the user is excited, the playback unit can play sound effects at a loud volume. Furthermore, if the user is sad, the playback unit can play sound effects at a quiet volume. This allows the playback method of sound effects to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the playback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the playback unit can input the user's emotion data into the generation AI and have the generation AI adjust the playback method of sound effects.

[0099] The reception unit can estimate the user's emotions and specifically determine the priority of input scene information based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize scene information with high importance. Furthermore, if the user is relaxed, the reception unit can prioritize detailed scene information. Furthermore, if the user is stressed, the reception unit can prioritize simple scene information. This allows the priority of scene information to be determined based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the scene information.

[0100] The generation unit can estimate the user's emotion and specifically adjust the length of the sound effect based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate a longer sound effect. Furthermore, if the user is in a hurry, the generation unit can generate a shorter sound effect. Furthermore, if the user is excited, the generation unit can generate a variety of sound effects. This allows the length of the sound effect to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the sound effect.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives scene information for which the user wants to generate sound effects. The scene information includes text, images, videos, etc. The reception unit supports both a method in which the user inputs a description of the scene in text and a method in which the user inputs images or videos. Step 2: The generator uses the AI ​​to analyze the scene information input by the receiver and generate appropriate sound effects. The generator analyzes the scene based on a pre-trained database and can adjust the parameters of the sound effects based on the atmosphere and tempo of the scene. For example, it determines the type, volume, and timing of sound effects based on the content and atmosphere of the scene. Step 3: The playback unit plays the sound effects generated by the generation unit in real time. The playback unit supports both streaming the sound effects generated on the cloud and downloading them to a local device for playback.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting scene information; a generation unit that analyzes the scene information input by the reception unit and generates sound effects; a playback unit that plays back the sound effects generated by the generation unit in real time. A system characterized by:

2. The reception unit Support both a text description of the scene and an image or video input by the user.

2. The system of claim 1.

3. The generation unit Analyzes the scene based on a pre-trained database and generates sound effects 2. The system of claim 1.

4. The playback unit Supports both streaming sound effects generated on the cloud and downloading them to a local device for playback.

2. The system of claim 1.

5. The generation unit Adjust sound effect parameters based on the mood or tempo of a scene 2. The system of claim 1.

6. The reception unit The system estimates the user's emotions and adjusts the scene information input method based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyzes the user's past scene information input history and suggests input methods 2. The system of claim 1.

8. The reception unit Filter scene information based on your current project or area of ​​interest as you enter it 2. The system of claim 1.

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