System

Abstract

To provide a system that analyzes various types of input data from a user and automatically generates high-quality contents.SOLUTION: A system according to an embodiment comprises a receiving unit, an analysis unit, and a generation unit. The receiving unit receives input of a text, an image, and a voice from a user. The analysis unit analyzes the text, image, and voice received by the receiving unit. The generation unit generates contents on the basis of a result of the analysis performed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have not been sufficient in efficiently analyzing a variety of input data from users and automatically generating high-quality content, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze various input data from users and automatically generate high-quality content. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives input of text, images, and audio from a user. The analysis unit analyzes the text, images, and audio received by the reception unit. The generation unit generates content based on the results of the analysis by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can analyze various input data from users and automatically generate high-quality content. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The content generation system according to the embodiment of the present invention is a system that accepts input of text, images, and voice from a user, analyzes them, and automatically generates high-quality content. In this content generation system, a user inputs text, images, voice, etc., and AI analyzes them to automatically generate high-quality content. For example, text for a blog article, images for advertising copy, and voice for a video script are input. This input is accepted by the AI. Next, the AI ​​analyzes the input text, images, and voice. The AI ​​uses natural language processing technology, image recognition technology, and voice recognition technology to analyze the input data. For example, it understands the content of the text, extracts the features of the image, and converts the content of the voice into text. Then, the AI ​​generates content based on the analysis results. For example, it generates text for a blog article, creates images for advertising copy, and generates text for a video script. This allows users to automatically obtain high-quality content. This allows companies and marketers to realize efficient content production and save time and resources. For example, it is possible to significantly reduce the time required to create a blog article and reduce the cost of creating advertising copy. In addition, it is possible to provide high-quality content quickly and in the creation of video scripts. This allows the content generation system to realize efficient content creation by accepting user input, analyzing it, and generating content.

[0029] The content generation system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives input of text, images, and voice from a user. The input from the user includes, but is not limited to, for example, text for a blog article, images for advertising copy, and voice for a video script. The reception unit includes, for example, a text box for receiving text input, an image upload function for receiving image input, and a voice recording function for receiving voice input. The analysis unit analyzes the text, image, and voice received by the reception unit. The analysis unit analyzes the text, image, and voice using, for example, natural language processing technology. For example, the content of the text is understood and important keywords are extracted. The analysis unit also analyzes the image using image recognition technology. For example, the image features are extracted and objects in the image are recognized. The analysis unit also analyzes the voice using voice recognition technology. For example, the voice content is converted into text and the emotion of the voice is analyzed. The generation unit generates content based on the results analyzed by the analysis unit. The generation unit generates, for example, text for a blog article. For example, the blog article structure is created based on the analyzed keywords, and a sentence is generated. The generation unit also generates an image of the advertising copy. For example, the design of the advertising copy is created based on the characteristics of the analyzed image. The generation unit also generates text of a video script. For example, the structure of the video script is created and sentences are generated based on the contents of the analyzed audio. In this way, the content generation system according to the embodiment can realize efficient content production by accepting and analyzing user input and generating content.

[0030] The reception unit receives input of text, images, and voice from the user. The input from the user includes, but is not limited to, for example, text for a blog post, images for advertising copy, and voice for a video script. The reception unit includes, for example, a text box for receiving text input, an image upload function for receiving image input, and a voice recording function for receiving voice input. Specifically, the text box is designed to allow the user to easily input text, and the input text is immediately sent to the system. The image upload function allows the user to select and upload an image file from his / her own device. The uploaded image is converted into an appropriate format in the system and sent to the analysis unit. The voice recording function allows the user to record voice using a microphone and send the voice data to the system. The recorded voice is preprocessed, such as noise removal and sound quality improvement, and then sent to the analysis unit. In addition, the reception unit also includes a function that allows the user to check the input content through a user interface and correct it if necessary. For example, in the case of text input, the user can preview the input text and correct typos. In the case of image input, the user can preview the uploaded image and perform simple editing such as cropping and applying filters. In the case of voice input, the user can play back the recorded voice and re-record it if necessary. This allows the reception unit to efficiently receive various inputs from the user and smoothly proceed with the processing of the entire system.

[0031] The analysis unit analyzes the text, image, and voice received by the reception unit. Natural language processing technology, image recognition technology, and voice recognition technology are used for the analysis. Specifically, the natural language processing technology is used to analyze the text, understand the content of the text, and extract important keywords. For example, morphological analysis is performed to divide words in the text and identify parts of speech. Furthermore, the subject and emotion of the text can be analyzed by considering the context. When analyzing an image using image recognition technology, image features are extracted and objects in the image are recognized. For example, a convolutional neural network (CNN) is used to extract features such as edges, textures, and colors of an image to identify specific objects or scenes. When analyzing voice using voice recognition technology, the content of the voice is converted into text and the emotion of the voice is analyzed. For example, a voice signal is converted into a spectrogram and a recurrent neural network (RNN) is used to recognize the content of the voice. Furthermore, the speaker's emotions and intentions can be inferred by analyzing features such as the pitch, intensity, and rhythm of the voice. The analysis unit combines these technologies to analyze the received data from multiple angles and accurately grasp the user's intentions and requests. In addition, the analysis unit stores the analysis results in a database and makes them accessible to the generation unit, allowing the analysis unit to analyze the received data quickly and accurately, improving the efficiency of the entire system.

[0032] The generation unit generates content based on the results of the analysis by the analysis unit. Specifically, when generating text for a blog article, the structure of the blog article is created based on the analyzed keywords, and sentences are generated. For example, a generation AI is used to collect information related to the keywords and generate natural sentences. The generation AI is trained with a large amount of text data in advance, and can generate high-quality sentences by taking grammar and context into consideration. When generating an image for an ad copy, the design of the ad copy is created based on the characteristics of the analyzed image. For example, an image generation AI is used to generate an image based on a specific theme or style. The image generation AI is trained with a variety of image data in advance, and can provide creative designs according to the user's requests. When generating text for a video script, the structure of the video script is created based on the analyzed audio content, and sentences are generated. For example, a voice recognition AI is used to convert the audio into text, and a video script is created based on the text. The voice recognition AI can generate natural dialogue and narration by taking into consideration the speaker's intentions and emotions. In addition, the generation unit also has a function of performing quality checks before providing the generated content to the user. For example, the grammar and spelling of the generated text are checked, and corrections are made if necessary. The resolution and color of the generated image are checked, and adjustments are made if necessary. The flow and content of the generated video script is checked and edited as necessary, allowing the generator to quickly generate high-quality content to meet user demands.

[0033] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit preferentially suggests an input method (text, image, voice, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. The reception unit can also suggest a related input method by referring to the contents entered by the user in the past. This makes it possible to select the optimal reception method by analyzing the user's past input history and to efficiently receive input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to select the optimal reception method.

[0034] The reception unit can perform filtering based on the user's current project or field of interest when receiving the input. For example, the reception unit preferentially receives only input related to a project currently in progress by the user. The reception unit can also filter related inputs based on the user's field of interest and preferentially receive them. The reception unit can also adjust the reception order of the inputs based on the project priority set by the user. This makes it possible to preferentially receive highly relevant inputs by filtering the inputs based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's project data and field of interest data to the generation AI and cause the generation AI to perform filtering.

[0035] The reception unit can preferentially receive inputs with high relevance based on the geographical location information of the user when receiving the input. For example, when the user is in a specific area, the reception unit preferentially receives inputs related to the area. The reception unit can also preferentially receive inputs related to a location close to the user's current location. The reception unit can also preferentially receive inputs related to a travel destination when the user is traveling. This allows more appropriate inputs to be received by preferentially receiving inputs with high relevance based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select inputs with high relevance.

[0036] The reception unit can analyze the user's social media activity and receive related inputs when receiving the input. The reception unit preferentially receives related inputs based on, for example, the content shared by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related inputs. The reception unit can also preferentially receive related inputs based on accounts the user follows on social media. In this way, by analyzing the user's social media activity, related inputs can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related inputs.

[0037] The analysis unit can adjust the level of detail of the analysis based on the importance of the input data during analysis. For example, the analysis unit performs a detailed analysis on input data with high importance. The analysis unit can also perform a concise analysis on input data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the input data. This makes it possible to provide a more appropriate analysis result by adjusting the level of detail of the analysis based on the importance of the input data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input importance data of the input data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0038] The analysis unit can apply different analysis algorithms depending on the category of the input data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. This makes it possible to provide more appropriate analysis results by applying different analysis algorithms depending on the category of the input data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the input data to the generation AI and cause the generation AI to select an analysis algorithm.

[0039] The analysis unit can determine the analysis priority based on the submission time of the input data during analysis. For example, the analysis unit preferentially analyzes the most recently submitted input data. The analysis unit can also postpone input data submitted earlier. The analysis unit can also adjust the analysis schedule based on the submission time. This enables more efficient analysis by determining the analysis priority based on the submission time of the input data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the input data to the generation AI and cause the generation AI to determine the analysis priority.

[0040] The analysis unit can adjust the order of analysis based on the relevance of the input data during analysis. For example, the analysis unit preferentially analyzes highly relevant input data. The analysis unit can also postpone the analysis of less relevant input data. The analysis unit can also adjust the order of analysis based on the relevance of the input data. This makes it possible to provide a more appropriate analysis result by adjusting the order of analysis based on the relevance of the input data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input relevance data of the input data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] The generation unit can adjust the level of detail of the content to be generated based on the importance of the analysis result at the time of generation. For example, the generation unit generates detailed content based on an analysis result with high importance. The generation unit can also generate concise content based on an analysis result with low importance. The generation unit can also determine the priority of the content to be generated according to the importance of the analysis result. As a result, more appropriate content can be provided by adjusting the level of detail of the content to be generated based on the importance of the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance data of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the content.

[0042] The generation unit can apply different generation algorithms depending on the category of the analysis result at the time of generation. The generation unit applies a natural language generation algorithm based on, for example, text data. The generation unit can also apply an image generation algorithm based on image data. The generation unit can also apply a voice generation algorithm based on voice data. This makes it possible to provide more appropriate content by applying different generation algorithms depending on the category of the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the analysis result to the generation AI and cause the generation AI to select a generation algorithm.

[0043] The generation unit can determine the priority of the content to be generated based on the submission time of the analysis result at the time of generation. The generation unit generates the content with priority based on, for example, the most recent analysis result. The generation unit can also generate the content later based on the analysis result that was submitted earlier. The generation unit can also adjust the schedule of the content to be generated based on the submission time. This enables more efficient content generation by determining the priority of the content to be generated based on the submission time of the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time data of the analysis result to the generation AI and cause the generation AI to determine the priority of the content.

[0044] The generation unit can adjust the order of the contents to be generated based on the relevance of the analysis result at the time of generation. For example, the generation unit generates the contents preferentially based on the analysis result with high relevance. The generation unit can also generate the contents later based on the analysis result with low relevance. The generation unit can also adjust the order of the contents to be generated based on the relevance of the analysis result. In this way, by adjusting the order of the contents to be generated based on the relevance of the analysis result, more appropriate content can be provided. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance data of the analysis result to the generation AI and cause the generation AI to adjust the order of the contents.

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

[0046] The reception unit can obtain additional information from a related external database based on the user's input. For example, if the user inputs text for a blog article, the reception unit can obtain related news articles and academic papers from an external database to complement the user's input. If the user inputs an image for an advertising copy, the reception unit can obtain related market research data and competitor advertisements to enhance the user's input. Furthermore, if the user inputs audio for a video script, the reception unit can obtain related music and sound effects from an external database to enrich the user's input. This allows for more substantial content to be generated by obtaining related additional information based on the user's input.

[0047] The analysis unit can provide related trend information based on the user's input. For example, when a user inputs text for a blog post, the analysis unit extracts current trend keywords and topics and suggests them to the user. When a user inputs an image for an advertising copy, the analysis unit can analyze the latest design trends and color trends and provide them to the user. Furthermore, when a user inputs audio for a video script, the analysis unit can analyze popular music genres and sound effect trends and suggest them to the user. This allows content that is more in line with the times to be generated by providing related trend information based on the user's input.

[0048] The analysis unit can evaluate the reliability of the input data and give priority to analyzing highly reliable data. For example, text data from a highly reliable source is analyzed with priority, and data from a less reliable source is analyzed later. The analysis unit can also evaluate the reliability of data provided by a user and give priority to analyzing highly reliable data. Furthermore, the analysis unit can evaluate the reliability of data acquired from an external database and give priority to analyzing highly reliable data. This makes it possible to provide more accurate analysis results by determining the analysis priority based on the reliability of the input data.

[0049] The generation unit can reflect the user's past feedback in the content to be generated. For example, the generation unit can give priority to using styles and formats that the user has previously preferred. The generation unit can also generate content that provides greater satisfaction by reflecting improvements that the user has previously pointed out. Furthermore, the generation unit can analyze the characteristics of content that the user has previously given a high rating, and incorporate these into new content. In this way, more personalized content can be provided by reflecting the user's past feedback.

[0050] The generation unit can reflect the user's current project progress status in the generated content. For example, in the early stage of the project, content focusing on an outline and a plan can be generated. In addition, in the intermediate stage of the project, content focusing on the progress status and intermediate results can be generated. Furthermore, in the final stage of the project, content focusing on results and a conclusion can be generated. In this way, more appropriate content can be provided by reflecting the user's current project progress status.

[0051] The process flow of the first embodiment will be briefly described below.

[0052] Step 1: The reception unit accepts text, image, and audio input from a user. User input may include text for a blog post, images for ad copy, audio for a video script, etc. The reception unit includes a text box for accepting text input, an image upload function for accepting image input, and a voice recording function for accepting voice input. Step 2: The analysis unit analyzes the text, images, and audio received by the reception unit. The analysis unit uses natural language processing technology to analyze the text and extract important keywords. It uses image recognition technology to analyze the image, extract image features, and recognize objects in the image. It uses voice recognition technology to analyze the audio, convert the audio content into text, and analyze the emotions in the audio. Step 3: The generation unit generates content based on the results of the analysis by the analysis unit. The generation unit creates a blog article structure based on the analyzed keywords and generates text. The generation unit creates an advertising copy design based on the analyzed image features. The generation unit creates a video script structure based on the analyzed audio content and generates text.

[0053] (Example 2) The content generation system according to the embodiment of the present invention is a system that accepts input of text, images, and voice from a user, analyzes them, and automatically generates high-quality content. In this content generation system, a user inputs text, images, voice, etc., and AI analyzes them to automatically generate high-quality content. For example, text for a blog article, images for advertising copy, and voice for a video script are input. This input is accepted by the AI. Next, the AI ​​analyzes the input text, images, and voice. The AI ​​uses natural language processing technology, image recognition technology, and voice recognition technology to analyze the input data. For example, it understands the content of the text, extracts the features of the image, and converts the content of the voice into text. Then, the AI ​​generates content based on the analysis results. For example, it generates text for a blog article, creates images for advertising copy, and generates text for a video script. This allows users to automatically obtain high-quality content. This allows companies and marketers to realize efficient content production and save time and resources. For example, it is possible to significantly reduce the time required to create a blog article and reduce the cost of creating advertising copy. In addition, it is possible to provide high-quality content quickly and in the creation of video scripts. This allows the content generation system to realize efficient content creation by accepting user input, analyzing it, and generating content.

[0054] The content generation system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives input of text, images, and voice from a user. The input from the user includes, but is not limited to, for example, text for a blog article, images for advertising copy, and voice for a video script. The reception unit includes, for example, a text box for receiving text input, an image upload function for receiving image input, and a voice recording function for receiving voice input. The analysis unit analyzes the text, image, and voice received by the reception unit. The analysis unit analyzes the text, image, and voice using, for example, natural language processing technology. For example, the content of the text is understood and important keywords are extracted. The analysis unit also analyzes the image using image recognition technology. For example, the image features are extracted and objects in the image are recognized. The analysis unit also analyzes the voice using voice recognition technology. For example, the voice content is converted into text and the emotion of the voice is analyzed. The generation unit generates content based on the results analyzed by the analysis unit. The generation unit generates, for example, text for a blog article. For example, the blog article structure is created based on the analyzed keywords, and a sentence is generated. The generation unit also generates an image of the advertising copy. For example, the design of the advertising copy is created based on the characteristics of the analyzed image. The generation unit also generates text of a video script. For example, the structure of the video script is created and sentences are generated based on the contents of the analyzed audio. In this way, the content generation system according to the embodiment can realize efficient content production by accepting and analyzing user input and generating content.

[0055] The reception unit receives input of text, images, and voice from the user. The input from the user includes, but is not limited to, for example, text for a blog post, images for advertising copy, and voice for a video script. The reception unit includes, for example, a text box for receiving text input, an image upload function for receiving image input, and a voice recording function for receiving voice input. Specifically, the text box is designed to allow the user to easily input text, and the input text is immediately sent to the system. The image upload function allows the user to select and upload an image file from his / her own device. The uploaded image is converted into an appropriate format in the system and sent to the analysis unit. The voice recording function allows the user to record voice using a microphone and send the voice data to the system. The recorded voice is preprocessed, such as noise removal and sound quality improvement, and then sent to the analysis unit. In addition, the reception unit also includes a function that allows the user to check the input content through a user interface and correct it if necessary. For example, in the case of text input, the user can preview the input text and correct typos. In the case of image input, the user can preview the uploaded image and perform simple editing such as cropping and applying filters. In the case of voice input, the user can play back the recorded voice and re-record it if necessary. This allows the reception unit to efficiently receive various inputs from the user and smoothly proceed with the processing of the entire system.

[0056] The analysis unit analyzes the text, image, and voice received by the reception unit. Natural language processing technology, image recognition technology, and voice recognition technology are used for the analysis. Specifically, the natural language processing technology is used to analyze the text, understand the content of the text, and extract important keywords. For example, morphological analysis is performed to divide words in the text and identify parts of speech. Furthermore, the subject and emotion of the text can be analyzed by considering the context. When analyzing an image using image recognition technology, image features are extracted and objects in the image are recognized. For example, a convolutional neural network (CNN) is used to extract features such as edges, textures, and colors of an image to identify specific objects or scenes. When analyzing voice using voice recognition technology, the content of the voice is converted into text and the emotion of the voice is analyzed. For example, a voice signal is converted into a spectrogram and a recurrent neural network (RNN) is used to recognize the content of the voice. Furthermore, the speaker's emotions and intentions can be inferred by analyzing features such as the pitch, intensity, and rhythm of the voice. The analysis unit combines these technologies to analyze the received data from multiple angles and accurately grasp the user's intentions and requests. In addition, the analysis unit stores the analysis results in a database and makes them accessible to the generation unit, allowing the analysis unit to analyze the received data quickly and accurately, improving the efficiency of the entire system.

[0057] The generation unit generates content based on the results of the analysis by the analysis unit. Specifically, when generating text for a blog article, the structure of the blog article is created based on the analyzed keywords, and sentences are generated. For example, a generation AI is used to collect information related to the keywords and generate natural sentences. The generation AI is trained with a large amount of text data in advance, and can generate high-quality sentences by taking grammar and context into consideration. When generating an image for an ad copy, the design of the ad copy is created based on the characteristics of the analyzed image. For example, an image generation AI is used to generate an image based on a specific theme or style. The image generation AI is trained with a variety of image data in advance, and can provide creative designs according to the user's requests. When generating text for a video script, the structure of the video script is created based on the analyzed audio content, and sentences are generated. For example, a voice recognition AI is used to convert the audio into text, and a video script is created based on the text. The voice recognition AI can generate natural dialogue and narration by taking into consideration the speaker's intentions and emotions. In addition, the generation unit also has a function of performing quality checks before providing the generated content to the user. For example, the grammar and spelling of the generated text are checked, and corrections are made if necessary. The resolution and color of the generated image are checked, and adjustments are made if necessary. The flow and content of the generated video script is checked and edited as necessary, allowing the generator to quickly generate high-quality content to meet user demands.

[0058] The reception unit can estimate the user's emotion and adjust the timing of receiving the input based on the estimated emotion. For example, when the user is feeling stressed, the reception unit delays the reception of the input to provide time for the user to relax. Also, when the user is relaxed, the reception unit can immediately accept the input to promote smooth operation. Also, when the user is in a hurry, the reception unit can quickly accept the input to support efficient operation. In this way, by adjusting the timing of receiving the input according to the user's emotion, the input can be accepted at a more appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0059] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit preferentially suggests an input method (text, image, voice, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. The reception unit can also suggest a related input method by referring to the contents entered by the user in the past. This makes it possible to select the optimal reception method by analyzing the user's past input history and to efficiently receive input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to select the optimal reception method.

[0060] The reception unit can perform filtering based on the user's current project or field of interest when receiving the input. For example, the reception unit preferentially receives only input related to a project currently in progress by the user. The reception unit can also filter related inputs based on the user's field of interest and preferentially receive them. The reception unit can also adjust the reception order of the inputs based on the project priority set by the user. This makes it possible to preferentially receive highly relevant inputs by filtering the inputs based on the user's current project or field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's project data and field of interest data to the generation AI and cause the generation AI to perform filtering.

[0061] The reception unit can estimate the user's emotion and determine the priority of the inputs to be received based on the estimated emotion. For example, when the user is stressed, the reception unit postpones inputs of low importance and prioritizes inputs of high importance. In addition, when the user is relaxed, the reception unit can equally receive all inputs. In addition, when the user is in a hurry, the reception unit can preferentially receive inputs of high urgency. In this way, by determining the priority of inputs according to the user's emotion, it is possible to receive inputs in a more appropriate order. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0062] The reception unit can preferentially receive inputs with high relevance based on the geographical location information of the user when receiving the input. For example, when the user is in a specific area, the reception unit preferentially receives inputs related to the area. The reception unit can also preferentially receive inputs related to a location close to the user's current location. The reception unit can also preferentially receive inputs related to a travel destination when the user is traveling. This allows more appropriate inputs to be received by preferentially receiving inputs with high relevance based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to select inputs with high relevance.

[0063] The reception unit can analyze the user's social media activity and receive related inputs when receiving the input. The reception unit preferentially receives related inputs based on, for example, the content shared by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related inputs. The reception unit can also preferentially receive related inputs based on accounts the user follows on social media. In this way, by analyzing the user's social media activity, related inputs can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related inputs.

[0064] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated emotion. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. In addition, when the user is in a hurry, the analysis unit can also provide a concise analysis result that covers the main points. In addition, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, by adjusting the expression method of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the input data during analysis. For example, the analysis unit performs a detailed analysis on input data with high importance. The analysis unit can also perform a concise analysis on input data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the input data. This makes it possible to provide a more appropriate analysis result by adjusting the level of detail of the analysis based on the importance of the input data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input importance data of the input data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0066] The analysis unit can apply different analysis algorithms depending on the category of the input data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. This makes it possible to provide more appropriate analysis results by applying different analysis algorithms depending on the category of the input data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the input data to the generation AI and cause the generation AI to select an analysis algorithm.

[0067] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. In addition, the analysis unit can also provide a visually stimulating analysis result when the user is excited. In this way, by adjusting the length of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0068] The analysis unit can determine the analysis priority based on the submission time of the input data during analysis. For example, the analysis unit preferentially analyzes the most recently submitted input data. The analysis unit can also postpone input data submitted earlier. The analysis unit can also adjust the analysis schedule based on the submission time. This enables more efficient analysis by determining the analysis priority based on the submission time of the input data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the input data to the generation AI and cause the generation AI to determine the analysis priority.

[0069] The analysis unit can adjust the order of analysis based on the relevance of the input data during analysis. For example, the analysis unit preferentially analyzes highly relevant input data. The analysis unit can also postpone the analysis of less relevant input data. The analysis unit can also adjust the order of analysis based on the relevance of the input data. This makes it possible to provide a more appropriate analysis result by adjusting the order of analysis based on the relevance of the input data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input relevance data of the input data to the generation AI and cause the generation AI to adjust the order of analysis.

[0070] The generation unit can estimate the user's emotion and adjust the expression method of the generated content based on the estimated emotion. For example, when the user is relaxed, the generation unit uses a detailed and polite expression method. In addition, when the user is in a hurry, the generation unit can also use a concise and to the point expression method. In addition, when the user is excited, the generation unit can also use a visually stimulating expression method. In this way, by adjusting the expression method of the generated content according to the user's emotion, more appropriate content can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0071] The generation unit can adjust the level of detail of the content to be generated based on the importance of the analysis result at the time of generation. For example, the generation unit generates detailed content based on an analysis result with high importance. The generation unit can also generate concise content based on an analysis result with low importance. The generation unit can also determine the priority of the content to be generated according to the importance of the analysis result. As a result, more appropriate content can be provided by adjusting the level of detail of the content to be generated based on the importance of the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance data of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the content.

[0072] The generation unit can apply different generation algorithms depending on the category of the analysis result at the time of generation. The generation unit applies a natural language generation algorithm based on, for example, text data. The generation unit can also apply an image generation algorithm based on image data. The generation unit can also apply a voice generation algorithm based on voice data. This makes it possible to provide more appropriate content by applying different generation algorithms depending on the category of the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the analysis result to the generation AI and cause the generation AI to select a generation algorithm.

[0073] The generation unit can estimate the user's emotion and adjust the length of the content to be generated based on the estimated emotion. For example, when the user is in a hurry, the generation unit generates short, to-the-point content. In addition, when the user is relaxed, the generation unit can also generate longer content including detailed explanations. In addition, when the user is excited, the generation unit can also generate content with visually stimulating effects. In this way, by adjusting the length of the content to be generated according to the user's emotion, more appropriate content can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0074] The generation unit can determine the priority of the content to be generated based on the submission time of the analysis result at the time of generation. The generation unit generates the content with priority based on, for example, the most recent analysis result. The generation unit can also generate the content later based on the analysis result that was submitted earlier. The generation unit can also adjust the schedule of the content to be generated based on the submission time. This enables more efficient content generation by determining the priority of the content to be generated based on the submission time of the analysis result. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time data of the analysis result to the generation AI and cause the generation AI to determine the priority of the content.

[0075] The generation unit can adjust the order of the contents to be generated based on the relevance of the analysis result at the time of generation. For example, the generation unit generates the contents preferentially based on the analysis result with high relevance. The generation unit can also generate the contents later based on the analysis result with low relevance. The generation unit can also adjust the order of the contents to be generated based on the relevance of the analysis result. In this way, by adjusting the order of the contents to be generated based on the relevance of the analysis result, more appropriate content can be provided. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance data of the analysis result to the generation AI and cause the generation AI to adjust the order of the contents.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The reception unit can obtain additional information from a related external database based on the user's input. For example, if the user inputs text for a blog article, the reception unit can obtain related news articles and academic papers from an external database to complement the user's input. If the user inputs an image for an advertising copy, the reception unit can obtain related market research data and competitor advertisements to enhance the user's input. Furthermore, if the user inputs audio for a video script, the reception unit can obtain related music and sound effects from an external database to enrich the user's input. This allows for more substantial content to be generated by obtaining related additional information based on the user's input.

[0078] The reception unit can estimate the user's emotion and customize the input reception method based on the estimated emotion. For example, when the user is stressed, the reception unit provides a simple and intuitive interface to reduce the burden on the user. Also, when the user is relaxed, the reception unit can provide detailed options to allow the user to perform more customization. Furthermore, when the user is excited, the reception unit can provide a visually attractive interface to maintain the user's excitement. In this way, a more comfortable user experience can be provided by customizing the input reception method according to the user's emotion.

[0079] The analysis unit can provide related trend information based on the user's input. For example, when a user inputs text for a blog post, the analysis unit extracts current trend keywords and topics and suggests them to the user. When a user inputs an image for an advertising copy, the analysis unit can analyze the latest design trends and color trends and provide them to the user. Furthermore, when a user inputs audio for a video script, the analysis unit can analyze popular music genres and sound effect trends and suggest them to the user. This allows content that is more in line with the times to be generated by providing related trend information based on the user's input.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis feedback method based on the estimated emotions. For example, if the user is stressed, the analysis unit can emphasize positive feedback to increase the user's motivation. Also, if the user is relaxed, the analysis unit can provide detailed feedback to allow the user to have a deeper understanding. Furthermore, if the user is excited, the analysis unit can provide visually attractive feedback to maintain the user's excitement. This makes it possible to provide more effective feedback by adjusting the analysis feedback method according to the user's emotions.

[0081] The analysis unit can evaluate the reliability of the input data and give priority to analyzing highly reliable data. For example, text data from a highly reliable source is analyzed with priority, and data from a less reliable source is analyzed later. The analysis unit can also evaluate the reliability of data provided by a user and give priority to analyzing highly reliable data. Furthermore, the analysis unit can evaluate the reliability of data acquired from an external database and give priority to analyzing highly reliable data. This makes it possible to provide more accurate analysis results by determining the analysis priority based on the reliability of the input data.

[0082] The generation unit can estimate the user's emotion and adjust the style of the generated content based on the estimated emotion. For example, when the user is relaxed, the generation unit generates content in a calm and soothing style. When the user is excited, the generation unit can also generate content in an energetic and dynamic style. Furthermore, when the user is stressed, the generation unit can generate content in a simple and intuitive style. In this way, by adjusting the style of the generated content according to the user's emotion, more appropriate content can be provided.

[0083] The generation unit can reflect the user's past feedback in the content to be generated. For example, the generation unit can give priority to using styles and formats that the user has previously preferred. The generation unit can also generate content that provides greater satisfaction by reflecting improvements that the user has previously pointed out. Furthermore, the generation unit can analyze the characteristics of content that the user has previously given a high rating, and incorporate these into new content. In this way, more personalized content can be provided by reflecting the user's past feedback.

[0084] The generation unit can estimate the user's emotion and adjust the tone of the generated content based on the estimated emotion. For example, when the user is relaxed, the generation unit generates content with a calm and soothing tone. When the user is excited, the generation unit can also generate content with an energetic and lively tone. Furthermore, when the user is stressed, the generation unit can generate content with a simple and reassuring tone. In this way, by adjusting the tone of the generated content according to the user's emotion, more appropriate content can be provided.

[0085] The generation unit can reflect the user's current project progress status in the generated content. For example, in the early stage of the project, content focusing on an outline and a plan can be generated. In addition, in the intermediate stage of the project, content focusing on the progress status and intermediate results can be generated. Furthermore, in the final stage of the project, content focusing on results and a conclusion can be generated. In this way, more appropriate content can be provided by reflecting the user's current project progress status.

[0086] The generation unit can estimate the user's emotion and adjust the interactivity of the generated content based on the estimated emotion. For example, when the user is relaxed, the generation unit generates content including many interactive elements. Also, when the user is in a hurry, the generation unit can generate content that reduces interactive elements and provides information quickly. Furthermore, when the user is excited, the generation unit can generate content that includes interactive elements such as game elements and quizzes. In this way, by adjusting the interactivity of the generated content according to the user's emotion, more appropriate content can be provided.

[0087] The process flow of the second embodiment will be briefly described below.

[0088] Step 1: The reception unit accepts text, image, and audio input from a user. User input may include text for a blog post, images for ad copy, audio for a video script, etc. The reception unit includes a text box for accepting text input, an image upload function for accepting image input, and a voice recording function for accepting voice input. Step 2: The analysis unit analyzes the text, images, and audio received by the reception unit. The analysis unit uses natural language processing technology to analyze the text and extract important keywords. It uses image recognition technology to analyze the image, extract image features, and recognize objects in the image. It uses voice recognition technology to analyze the audio, convert the audio content into text, and analyze the emotions in the audio. Step 3: The generation unit generates content based on the results of the analysis by the analysis unit. The generation unit creates a blog article structure based on the analyzed keywords and generates text. The generation unit creates an advertising copy design based on the analyzed image features. The generation unit creates a video script structure based on the analyzed audio content and generates text.

[0089] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0090] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0091] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0092] For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives input of text, image, and voice from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the received text, image, and voice. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates content based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0096] 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.

[0097] 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 the voice according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0099] 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 performed in a secure state.

[0100] Fig. 4 shows an example of 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.

[0101] 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.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0104] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0105] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0106] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0107] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0108] For example, the reception unit is realized by the microphone 238 or the camera 42 of the smart glasses 214, and receives input of text, image, and voice from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the received text, image, and voice. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates content based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0112] 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.

[0113] 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 the voice according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0115] 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 performed in a secure state.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0119] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0120] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0121] 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 voice indicating a user input for 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.

[0122] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0123] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] For example, the reception unit is realized by the microphone 238 or the camera 42 of the headset type terminal 314, and receives input of text, image, and voice from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the received text, image, and voice. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates content based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0136] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] For example, the reception unit is realized by the microphone 238 or the camera 42 of the robot 414, and receives input of text, image, and voice from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the received text, image, and voice. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates content based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0142] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0143] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0145] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0146] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0147] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0149] 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.

[0150] 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 Universal Serial Bus (USB) 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.

[0151] In addition, 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 upon request from the data processing device 12.

[0152] 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.

[0153] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0154] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0155] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0156] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0158] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0160] (Appendix 1) a reception unit that receives input of text, images, and voice from a user; an analysis unit that analyzes the text, image, and sound received by the reception unit; A generation unit that generates content based on the result of the analysis by the analysis unit. A system characterized by: (Appendix 2) The reception unit is Estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. 2. The system of claim 1, (Appendix 3) The reception unit is Analyze the user's past input history and select the appropriate reception method 2. The system of claim 1, (Appendix 4) The reception unit is As input is received, it is filtered based on the user's current projects and interests. 2. The system of claim 1, (Appendix 5) The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated emotions. 2. The system of claim 1, (Appendix 6) The reception unit is When accepting input, prioritize input that is more relevant based on the user's geographic location information. 2. The system of claim 1, (Appendix 7) The reception unit is When receiving input, analyze the user's social media activity and receive relevant input 2. The system of claim 1, (Appendix 8) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred emotions 2. The system of claim 1, (Appendix 9) The analysis unit is During analysis, adjust the level of detail based on the importance of the input data 2. The system of claim 1, (Appendix 10) The analysis unit is During parsing, apply different parsing algorithms depending on the category of input data 2. The system of claim 1, (Appendix 11) The analysis unit is Infer user emotion and adjust the length of the analysis based on the inferred emotion 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, prioritize analysis based on when input data was submitted 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the order of analysis based on the relevance of the input data 2. The system of claim 1, (Appendix 14) The generation unit is Estimate the user's emotions and adjust the way content is presented based on the estimated emotions. 2. The system of claim 1, (Appendix 15) The generation unit is At generation time, adjust the level of detail of the generated content based on the importance of the analysis results. 2. The system of claim 1, (Appendix 16) The generation unit is During generation, different generation algorithms are applied depending on the category of analysis results. 2. The system of claim 1, (Appendix 17) The generation unit is Estimate the user's emotions and adjust the length of the generated content based on the estimated emotions. 2. The system of claim 1, (Appendix 18) The generation unit is At generation time, prioritize the content to generate based on when analysis results are submitted 2. The system of claim 1, (Appendix 19) The generation unit is At generation time, the order of generated content is adjusted based on the relevance of the analysis results 2. The system of claim 1, [Explanation of symbols]

[0161] 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 that receives input of text, images, and voice from a user; an analysis unit that analyzes the text, image, and sound received by the reception unit; A generation unit that generates content based on the result of the analysis by the analysis unit. A system characterized by:

2. The reception unit is Estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions.

2. The system of claim 1 .

3. The reception unit is Analyze the user's past input history and select the appropriate reception method 2. The system of claim 1 .

4. The reception unit is As input is received, it is filtered based on the user's current projects and interests.

2. The system of claim 1 .

5. The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated emotions.

2. The system of claim 1 .

6. The reception unit is When accepting input, prioritize input that is more relevant based on the user's geographic location information.

2. The system of claim 1 .

7. The reception unit is When receiving input, analyze the user's social media activity and receive relevant input 2. The system of claim 1 .

8. The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred emotions 2. The system of claim 1 .

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