system

Abstract

The system according to the embodiment aims to evaluate the degree of AI generation of content provided by a user. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives content from a user. The analysis unit analyzes the content received by the reception unit. The evaluation unit evaluates the AI ​​generation level based on the results of the analysis by the analysis unit. The provision unit provides the evaluation results obtained by the evaluation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies lack sufficient means to assess whether content provided by users was generated by AI, leaving room for improvement.

[0005] The system according to the embodiment aims to evaluate the degree of AI generation of content provided by a user. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives content from a user. The analysis unit analyzes the content received by the reception unit. The evaluation unit evaluates the AI ​​generation level based on the analysis results obtained by the analysis unit. The provision unit provides the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the degree of AI generation of content provided by a user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI-generatedness checking system according to an embodiment of the present invention evaluates the AI-generatedness of content entered by a user. This AI-generatedness checking system allows a user to input content such as text or images, and an AI analyzes the content to determine the extent to which it was generated by AI. For example, in the case of text, the AI-generatedness is evaluated based on grammar, vocabulary frequency, and stylistic consistency. In the case of images, the system analyzes pixel arrangement and color patterns. The analysis results are provided to the user, and an AI-generatedness score is displayed. This tool allows users to confirm the reliability of content and promotes the use of AI-generated content. For example, when a user uses this tool to check the reliability of a piece of writing, the AI-generatedness score can be used as a reference. It can also be used to evaluate the quality of AI-generated content. In this way, the AI-generatedness checking system can confirm the reliability of content entered by a user and promote the use of AI-generated content.

[0029] An AI creativity check system according to an embodiment includes a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives content from a user. The content from the user includes, but is not limited to, text, images, audio, and video. The reception unit receives, for example, text entered by a user. The reception unit can also receive images uploaded by a user. The reception unit can also receive audio and video content. For example, the reception unit receives audio files recorded by a user. The analysis unit analyzes the content received by the reception unit. The analysis unit includes algorithms for analyzing, for example, grammar, vocabulary frequency, and stylistic consistency. For example, the analysis unit analyzes the grammatical structure of the text and evaluates the AI ​​creativity. The analysis unit also includes algorithms for analyzing the pixel arrangement and color patterns of an image. For example, the analysis unit analyzes the pixel arrangement of an image and evaluates the AI ​​creativity. The evaluation unit evaluates the AI ​​creativity based on the results of the analysis by the analysis unit. The evaluation unit calculates a score based on, for example, grammatical accuracy and vocabulary diversity. The evaluation unit also includes a method for scoring the AI ​​creativity based on the image analysis results. For example, the evaluation unit calculates a score based on the color pattern of the image. The provision unit provides the evaluation results obtained by the evaluation unit. The provision unit includes, for example, an interface that displays the score and provides a detailed explanation of the analysis results. For example, the provision unit visually displays the score to the user and provides a detailed explanation of the analysis results. This enables the AI ​​creativity check system according to the embodiment to efficiently accept, analyze, evaluate, and provide user content.

[0030] The analysis unit may include algorithms that analyze grammar, vocabulary frequency, and stylistic consistency. The analysis unit, for example, analyzes the grammatical structure of a text using a grammar analysis algorithm. For example, the analysis unit evaluates grammatical accuracy and detects grammatical errors. The analysis unit also includes an algorithm that analyzes vocabulary frequency. For example, the analysis unit can evaluate the vocabulary diversity in a text and identify frequently occurring vocabulary. The analysis unit also includes an algorithm that analyzes stylistic consistency. For example, the analysis unit can analyze the stylistic features of a text and evaluate whether the style is consistent. This improves the accuracy of evaluating the AI ​​generation level by analyzing grammar, vocabulary frequency, stylistic consistency, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input text data to a generation AI and cause the generation AI to perform grammatical analysis and vocabulary analysis.

[0031] The evaluation unit may include a method for scoring the AI ​​generation level based on the analysis results. The evaluation unit may calculate a score based on, for example, grammatical accuracy or lexical diversity. For example, the evaluation unit may calculate a score based on the number and type of grammatical errors. The evaluation unit may also evaluate lexical diversity and calculate a score. For example, the evaluation unit may evaluate the lexical diversity in the text and calculate a score based on the proportion of frequently occurring vocabulary. The evaluation unit may also evaluate the consistency of writing style and calculate a score. For example, the evaluation unit may analyze stylistic features, evaluate whether the writing style is consistent, and calculate a score. This improves the accuracy of the evaluation by scoring the AI ​​generation level based on the analysis results. Some or all of the above-described processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit may input the analysis results to a generation AI and cause the generation AI to perform scoring.

[0032] The providing unit may include an interface that provides a score display method and a detailed explanation of the analysis results. The providing unit may include, for example, a method for visually displaying the score. For example, the providing unit may display the score as a graph or chart. The providing unit may also include a method for providing a detailed explanation of the analysis results. For example, the providing unit may provide a detailed explanation of the analysis results in text format. The providing unit may also provide the analysis results in diagram format. For example, the providing unit may display the analysis results as a table or graph, providing them in a format that is visually easy for the user to understand. This makes it easier for the user to understand the results by providing a score display method and a detailed explanation of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit may input the analysis results to a generation AI and cause the generation AI to execute the display method.

[0033] The analysis unit may include an algorithm that analyzes the pixel arrangement and color patterns of an image. The analysis unit may include, for example, an algorithm that analyzes the pixel arrangement of an image. For example, the analysis unit may analyze position information of pixels in an image and identify an arrangement pattern. The analysis unit may also include an algorithm that analyzes the color pattern of an image. For example, the analysis unit may analyze the color distribution and hue changes of an image and identify a color pattern. This improves the accuracy of evaluating the AI ​​generation degree of an image by analyzing the pixel arrangement and color patterns of an image. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input image data to a generation AI and cause the generation AI to analyze the pixel arrangement and color patterns.

[0034] The evaluation unit may include a method for scoring the AI ​​creativity based on the image analysis results. The evaluation unit may calculate a score based on, for example, the pixel arrangement or color pattern of the image. For example, the evaluation unit may evaluate the regularity of the pixel arrangement or the consistency of the color pattern and calculate a score. The evaluation unit may also include a method for scoring the AI ​​creativity based on the image analysis results. For example, the evaluation unit may calculate a score based on the color distribution or hue changes of the image. This improves the accuracy of the evaluation by scoring the AI ​​creativity based on the image analysis results. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit may input the image analysis results to the generation AI and cause the generation AI to perform scoring.

[0035] The reception unit can analyze the user's past content submission history and select an appropriate reception method. The reception unit, for example, includes an algorithm that analyzes the user's past content submission history. For example, the reception unit can analyze the format and content of content previously submitted by the user and select the optimal reception method. The reception unit can also suggest an optimal reception time period based on the user's past submission history. For example, the reception unit can analyze time periods during which the user frequently submitted content in the past and accept content during those time periods. The reception unit can also analyze trends in content previously submitted by the user and preferentially accept related content. In this way, by analyzing the user's past submission history, the optimal reception method can be selected and content can be efficiently accepted. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's submission history data into a generation AI and have the generation AI select the optimal reception method.

[0036] The reception unit may filter content based on the user's current project or area of ​​interest when receiving the content. The reception unit may include, for example, an algorithm for identifying the user's current project or area of ​​interest. For example, the reception unit may preferentially receive highly relevant content based on the user's project progress status or area of ​​interest. The reception unit may also receive content at an appropriate time according to the user's project progress status. For example, the reception unit may preferentially receive content related to a project the user is currently working on. The reception unit may also filter and receive highly relevant content based on the user's area of ​​interest. In this way, by filtering based on the user's current project or area of ​​interest, highly relevant content can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's project data and area of ​​interest data to a generation AI and cause the generation AI to perform filtering.

[0037] When receiving content, the reception unit can prioritize receiving highly relevant content by taking into account the user's geographical location information. The reception unit, for example, includes an algorithm for acquiring the user's geographical location information. For example, the reception unit can acquire the user's geographical location information using GPS data or a location information service. The reception unit also includes a method for preferentially receiving highly relevant content based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving content related to that area. The reception unit can also prioritize receiving content related to local trends based on the user's location information. Furthermore, when the user is traveling, the reception unit can prioritize receiving content related to the travel destination. In this way, highly relevant content can be prioritized by taking the user's geographical location information into account. Some or all of the above-described 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 location information data to a generation AI and cause the generation AI to select highly relevant content.

[0038] The reception unit may analyze the user's social media activity and receive related content when receiving content. The reception unit may include, for example, an algorithm for analyzing the user's social media activity. For example, the reception unit may analyze the user's posts and the user's followers' responses to identify related content. The reception unit may also include a method for preferentially receiving related content based on the user's social media activity. For example, the reception unit may preferentially receive content related to content shared by the user on social media. The reception unit may also analyze the user's social media activity history and preferentially receive content that is likely to be of interest to the user. The reception unit may also receive related content based on the posts of accounts the user follows. This allows the user's social media activity to be analyzed and highly relevant content to be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's social media data into a generation AI and cause the generation AI to select related content.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the content during analysis. The analysis unit, for example, includes an algorithm for evaluating the importance of the content. For example, the analysis unit can evaluate the influence and relevance of the content and identify the importance. The analysis unit also includes a method for adjusting the level of detail of the analysis based on the importance of the content. For example, the analysis unit can perform a detailed analysis on content with high importance. The analysis unit can also perform a simplified analysis on content with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the content. Some or all of the above-described 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 content importance data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the content category during analysis. The analysis unit, for example, includes an algorithm for identifying the content category. For example, the analysis unit can identify categories such as text, image, audio, and video. The analysis unit also includes a method for applying different analysis algorithms depending on the content category. For example, the analysis unit can apply a grammar analysis algorithm to text content. The analysis unit can apply a pixel analysis algorithm to image content. The analysis unit can also apply a frame analysis algorithm to video content. This improves the accuracy of analysis by applying different analysis algorithms depending on the content category. Some or all of the above-described 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 content category data to a generation AI and cause the generation AI to select an analysis algorithm to apply.

[0041] During analysis, the analysis unit can determine the analysis priority based on the submission time of the content. The analysis unit, for example, includes an algorithm that identifies the submission time of the content. For example, the analysis unit can identify the submission date or submission time and determine the analysis priority based on the submission time. The analysis unit also includes a method for adjusting the analysis order based on the submission time. For example, the analysis unit can prioritize analysis of the most recent content. The analysis unit can also postpone content that was submitted earlier. The analysis unit can also adjust the analysis order based on the submission time. As a result, by determining the analysis priority based on the submission time of the content, the most recent content can be analyzed preferentially. 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 submission time data to a generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the content. The analysis unit, for example, includes an algorithm for evaluating the relevance of the content. For example, the analysis unit can evaluate the relevance based on common keywords or related topics. The analysis unit also includes a method for preferentially analyzing highly relevant content. For example, the analysis unit can prioritize analyzing highly relevant content. The analysis unit can also postpone less relevant content. The analysis unit can also adjust the order of analysis based on the relevance of the content. As a result, by adjusting the order of analysis based on the relevance of the content, highly relevant content can be analyzed preferentially. 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 relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0043] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between the analysis results during the evaluation. The evaluation unit, for example, includes an algorithm for evaluating the interrelationships between the analysis results. For example, the evaluation unit can evaluate the interrelationships between grammatical analysis results and lexical analysis results. The evaluation unit can also evaluate the interrelationships between pixel arrangements and color patterns in an image. Furthermore, the evaluation unit can evaluate the interrelationships between video frame analysis results and audio analysis results. This improves the accuracy of the evaluation by taking the interrelationships between the analysis results into account. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input analysis result data into a generation AI and cause the generation AI to evaluate the interrelationships.

[0044] The evaluation unit can perform evaluation taking into account attribute information of the submitter of the content when performing evaluation. The evaluation unit, for example, includes an algorithm for acquiring the submitter's attribute information. For example, the evaluation unit can acquire attribute information such as the submitter's age, gender, and occupation. The evaluation unit also includes a method for adjusting evaluation criteria based on the submitter's attribute information. For example, the evaluation unit can perform evaluation taking into account the submitter's level of expertise. The evaluation unit can also perform evaluation taking into account the submitter's past submission history. Furthermore, the evaluation unit can adjust the evaluation criteria based on the submitter's attribute information. This allows for a more appropriate evaluation by taking into account the submitter's attribute information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the submitter's attribute information data into a generation AI and cause the generation AI to adjust the evaluation criteria.

[0045] The evaluation unit can perform the evaluation taking into account the geographical distribution of the content. The evaluation unit, for example, includes an algorithm for evaluating the geographical distribution of the content. For example, the evaluation unit can evaluate the distribution by region or geographical characteristics. The evaluation unit also includes a method for adjusting the evaluation criteria based on the geographical distribution. For example, the evaluation unit can prioritize evaluation of content related to a specific region. The evaluation unit can also adjust the evaluation criteria based on the geographical distribution. Furthermore, the evaluation unit can perform the evaluation taking into account trends by region. In this way, by taking the geographical distribution of the content into account, it is possible to perform an evaluation that reflects the characteristics of each region. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input geographical distribution data to a generation AI and cause the generation AI to adjust the evaluation criteria.

[0046] The evaluation unit can improve the accuracy of the evaluation by referring to related literature of the content during evaluation. The evaluation unit, for example, includes an algorithm for identifying related literature. For example, the evaluation unit can identify cited literature and reference literature and complement the evaluation criteria. The evaluation unit also includes a method for adjusting the evaluation results by taking into account the content of the related literature. For example, the evaluation unit can improve the accuracy of the evaluation based on the citation frequency of the related literature. As a result, the accuracy of the evaluation is improved by referring to the related literature. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input related literature data into a generation AI and have the generation AI complement the evaluation criteria.

[0047] The providing unit can select an appropriate display method by referring to the user's past operation history when providing the display method. The providing unit, for example, includes an algorithm that analyzes the user's past operation history. For example, the providing unit can analyze the user's operation log and usage history to select an optimal display method. The providing unit also includes a method for suggesting a display method based on the user's past operation history. For example, the providing unit can prioritize providing a display method that the user has previously preferred. The providing unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the providing unit can provide a customized display method based on the display method that the user has previously used. This makes it possible to provide an optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to a generation AI and cause the generation AI to select a display method.

[0048] The providing unit can select the optimal display method by taking into account the user's device information when providing the display. The providing unit, for example, includes an algorithm for acquiring the user's device information. For example, the providing unit can acquire the device type and OS version. The providing unit also includes a method for selecting the optimal display method based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method including detailed information. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select a display method.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The reception unit can analyze the user's past content submission history and select an appropriate reception method. For example, the reception unit can analyze the format and content of content submitted by the user in the past and select the optimal reception method. The reception unit can also suggest an optimal reception time period based on the user's past submission history. For example, the reception unit can analyze time periods during which the user frequently submitted content in the past and accept content during those time periods. The reception unit can also analyze trends in content submitted by the user in the past and preferentially accept related content. In this way, by analyzing the user's past submission history, the optimal reception method can be selected and content can be accepted efficiently. Some or all of the above-described 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 submission history data into a generation AI and have the generation AI select the optimal reception method.

[0051] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the content. For example, the analysis unit can evaluate the impact and relevance of the content and identify the importance. The analysis unit also includes a method for adjusting the level of detail of the analysis based on the importance of the content. For example, the analysis unit can perform a detailed analysis on content with high importance. The analysis unit can also perform a simplified analysis on content with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the content. Some or all of the above-described 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 content importance data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0052] During evaluation, the evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between the analysis results. For example, the evaluation unit can evaluate the interrelationships between grammatical analysis results and lexical analysis results. The evaluation unit can also evaluate the interrelationships between image pixel arrangements and color patterns. Furthermore, the evaluation unit can evaluate the interrelationships between video frame analysis results and audio analysis results. This improves the accuracy of the evaluation by taking into account the interrelationships between the analysis results. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input analysis result data into a generation AI and have the generation AI evaluate the interrelationships.

[0053] The providing unit can select an appropriate display method by referring to the user's past operation history when providing the display method. For example, the providing unit can analyze the user's operation log and usage history to select the optimal display method. The providing unit also includes a method for suggesting a display method based on the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used preferably in the past. The providing unit can also suggest the optimal display method from the user's past operation history. Furthermore, the providing unit can provide a customized display method based on the display method that the user has used in the past. This makes it possible to provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to a generation AI and cause the generation AI to select a display method.

[0054] When receiving content, the reception unit can prioritize receiving highly relevant content by taking into account the user's geographical location information. For example, the reception unit can acquire the user's geographical location information using GPS data or a location information service. The reception unit also includes a method for preferentially receiving highly relevant content based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving content related to that area. The reception unit can also prioritize receiving content related to local trends based on the user's location information. Furthermore, when the user is traveling, the reception unit can prioritize receiving content related to the travel destination. In this way, highly relevant content can be prioritized by taking the user's geographical location information into account. Some or all of the above-described 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 location information data to a generation AI and cause the generation AI to select highly relevant content.

[0055] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, the providing unit can acquire the device type and OS version. The providing unit also includes a method for selecting the optimal display method based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. If the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method including detailed information. This makes it possible to provide the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select a display method.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit receives content from a user. The content from a user includes text, images, audio, video, etc. For example, the reception unit receives text entered by the user, images uploaded by the user, and recorded audio files. Step 2: The analysis unit analyzes the content received by the reception unit. The analysis unit is equipped with algorithms that analyze grammar, vocabulary frequency, stylistic consistency, image pixel layout, color patterns, etc. For example, the analysis unit analyzes the grammatical structure of text and the pixel layout of images to evaluate the degree of AI generation. Step 3: The evaluation unit evaluates the AI ​​generation based on the results of the analysis by the analysis unit, calculating a score based on grammatical accuracy, vocabulary diversity, image color patterns, etc. Step 4: The providing unit provides the evaluation results obtained by the evaluation unit. The providing unit has an interface that provides a method for displaying the score and a detailed explanation of the analysis results. For example, the providing unit visually displays the score to the user and provides a detailed explanation of the analysis results.

[0058] (Example 2) An AI-generatedness checking system according to an embodiment of the present invention evaluates the AI-generatedness of content entered by a user. This AI-generatedness checking system allows users to input content such as text or images, and then analyzes the content using an AI to determine the extent to which it was generated by AI. For example, in the case of text, the AI-generatedness is evaluated based on grammar, vocabulary frequency, and stylistic consistency. In the case of images, the system analyzes pixel arrangement and color patterns. The analysis results are provided to the user, and an AI-generatedness score is displayed. This tool allows users to verify the reliability of content and promotes the use of AI-generated content. For example, when a user uses this tool to verify the reliability of a piece of writing, the AI-generatedness score can be used as a reference. It can also be used to evaluate the quality of AI-generated content. In this way, the AI-generatedness checking system can verify the reliability of content entered by users and promote the use of AI-generated content.

[0059] An AI creativity check system according to an embodiment includes a reception unit, an analysis unit, an evaluation unit, and a provision unit. The reception unit receives content from a user. The content from the user includes, but is not limited to, text, images, audio, and video. The reception unit receives, for example, text entered by a user. The reception unit can also receive images uploaded by a user. The reception unit can also receive audio and video content. For example, the reception unit receives audio files recorded by a user. The analysis unit analyzes the content received by the reception unit. The analysis unit includes algorithms for analyzing, for example, grammar, vocabulary frequency, and stylistic consistency. For example, the analysis unit analyzes the grammatical structure of the text and evaluates the AI ​​creativity. The analysis unit also includes algorithms for analyzing the pixel arrangement and color patterns of an image. For example, the analysis unit analyzes the pixel arrangement of an image and evaluates the AI ​​creativity. The evaluation unit evaluates the AI ​​creativity based on the results of the analysis by the analysis unit. The evaluation unit calculates a score based on, for example, grammatical accuracy and vocabulary diversity. The evaluation unit also includes a method for scoring the AI ​​creativity based on the image analysis results. For example, the evaluation unit calculates a score based on the color pattern of the image. The provision unit provides the evaluation results obtained by the evaluation unit. The provision unit includes, for example, an interface that displays the score and provides a detailed explanation of the analysis results. For example, the provision unit visually displays the score to the user and provides a detailed explanation of the analysis results. This enables the AI ​​creativity check system according to the embodiment to efficiently accept, analyze, evaluate, and provide user content.

[0060] The analysis unit may include algorithms that analyze grammar, vocabulary frequency, and stylistic consistency. The analysis unit, for example, analyzes the grammatical structure of a text using a grammar analysis algorithm. For example, the analysis unit evaluates grammatical accuracy and detects grammatical errors. The analysis unit also includes an algorithm that analyzes vocabulary frequency. For example, the analysis unit can evaluate the vocabulary diversity in a text and identify frequently occurring vocabulary. The analysis unit also includes an algorithm that analyzes stylistic consistency. For example, the analysis unit can analyze the stylistic features of a text and evaluate whether the style is consistent. This improves the accuracy of evaluating the AI ​​generation level by analyzing grammar, vocabulary frequency, stylistic consistency, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input text data to a generation AI and cause the generation AI to perform grammatical analysis and vocabulary analysis.

[0061] The evaluation unit may include a method for scoring the AI ​​generation level based on the analysis results. The evaluation unit may calculate a score based on, for example, grammatical accuracy or lexical diversity. For example, the evaluation unit may calculate a score based on the number and type of grammatical errors. The evaluation unit may also evaluate lexical diversity and calculate a score. For example, the evaluation unit may evaluate the lexical diversity in the text and calculate a score based on the proportion of frequently occurring vocabulary. The evaluation unit may also evaluate the consistency of writing style and calculate a score. For example, the evaluation unit may analyze stylistic features, evaluate whether the writing style is consistent, and calculate a score. This improves the accuracy of the evaluation by scoring the AI ​​generation level based on the analysis results. Some or all of the above-described processing in the evaluation unit may be performed using AI, or may be performed without using AI. For example, the evaluation unit may input the analysis results to a generation AI and cause the generation AI to perform scoring.

[0062] The providing unit may include an interface that provides a score display method and a detailed explanation of the analysis results. The providing unit may include, for example, a method for visually displaying the score. For example, the providing unit may display the score as a graph or chart. The providing unit may also include a method for providing a detailed explanation of the analysis results. For example, the providing unit may provide a detailed explanation of the analysis results in text format. The providing unit may also provide the analysis results in diagram format. For example, the providing unit may display the analysis results as a table or graph, providing them in a format that is visually easy for the user to understand. This makes it easier for the user to understand the results by providing a score display method and a detailed explanation of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit may input the analysis results to a generation AI and cause the generation AI to execute the display method.

[0063] The analysis unit may include an algorithm that analyzes the pixel arrangement and color patterns of an image. The analysis unit may include, for example, an algorithm that analyzes the pixel arrangement of an image. For example, the analysis unit may analyze position information of pixels in an image and identify an arrangement pattern. The analysis unit may also include an algorithm that analyzes the color pattern of an image. For example, the analysis unit may analyze the color distribution and hue changes of an image and identify a color pattern. This improves the accuracy of evaluating the AI ​​generation degree of an image by analyzing the pixel arrangement and color patterns of an image. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input image data to a generation AI and cause the generation AI to analyze the pixel arrangement and color patterns.

[0064] The evaluation unit may include a method for scoring the AI ​​creativity based on the image analysis results. The evaluation unit may calculate a score based on, for example, the pixel arrangement or color pattern of the image. For example, the evaluation unit may evaluate the regularity of the pixel arrangement or the consistency of the color pattern and calculate a score. The evaluation unit may also include a method for scoring the AI ​​creativity based on the image analysis results. For example, the evaluation unit may calculate a score based on the color distribution or hue changes of the image. This improves the accuracy of the evaluation by scoring the AI ​​creativity based on the image analysis results. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit may input the image analysis results to the generation AI and cause the generation AI to perform scoring.

[0065] The reception unit can estimate the user's emotions and adjust the timing of content reception based on the estimated user emotions. The reception unit, for example, includes an algorithm for estimating the user's emotions. For example, the reception unit can analyze the user's facial expressions and voice to estimate the user's emotions. The reception unit also includes a method for adjusting the timing of content reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of content reception to provide the user with time to relax. If the user is relaxed, the reception unit can immediately accept content and start analyzing it. Furthermore, if the user is in a hurry, the reception unit can prioritize accepting content and quickly analyze it. This allows the user's stress to be reduced by adjusting the timing of content reception based on the user's emotions, and the content can be received at an appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0066] The reception unit can analyze the user's past content submission history and select an appropriate reception method. The reception unit, for example, includes an algorithm that analyzes the user's past content submission history. For example, the reception unit can analyze the format and content of content previously submitted by the user and select the optimal reception method. The reception unit can also suggest an optimal reception time period based on the user's past submission history. For example, the reception unit can analyze time periods during which the user frequently submitted content in the past and accept content during those time periods. The reception unit can also analyze trends in content previously submitted by the user and preferentially accept related content. In this way, by analyzing the user's past submission history, the optimal reception method can be selected and content can be efficiently accepted. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's submission history data into a generation AI and have the generation AI select the optimal reception method.

[0067] The reception unit may filter content based on the user's current project or area of ​​interest when receiving the content. The reception unit may include, for example, an algorithm for identifying the user's current project or area of ​​interest. For example, the reception unit may preferentially receive highly relevant content based on the user's project progress status or area of ​​interest. The reception unit may also receive content at an appropriate time according to the user's project progress status. For example, the reception unit may preferentially receive content related to a project the user is currently working on. The reception unit may also filter and receive highly relevant content based on the user's area of ​​interest. In this way, by filtering based on the user's current project or area of ​​interest, highly relevant content can be preferentially received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's project data and area of ​​interest data to a generation AI and cause the generation AI to perform filtering.

[0068] The reception unit can estimate the user's emotions and determine the priority of the content to be received based on the estimated user emotions. The reception unit, for example, includes an algorithm for estimating the user's emotions. For example, the reception unit can analyze the user's facial expressions and voice to estimate the emotions. The reception unit also includes a method for determining the priority of the content to be received based on the estimated user emotions. For example, the reception unit can postpone content of low importance when the user is stressed. The reception unit can prioritize content of high importance when the user is relaxed. Furthermore, the reception unit can prioritize content of high urgency when the user is in a hurry. In this way, by determining the priority of the content to be received based on the user's emotions, it is possible to receive content in a priority order that meets the user's needs. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0069] When receiving content, the reception unit can prioritize receiving highly relevant content by taking into account the user's geographical location information. The reception unit, for example, includes an algorithm for acquiring the user's geographical location information. For example, the reception unit can acquire the user's geographical location information using GPS data or a location information service. The reception unit also includes a method for preferentially receiving highly relevant content based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving content related to that area. The reception unit can also prioritize receiving content related to local trends based on the user's location information. Furthermore, when the user is traveling, the reception unit can prioritize receiving content related to the travel destination. In this way, highly relevant content can be prioritized by taking the user's geographical location information into account. Some or all of the above-described 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 location information data to a generation AI and cause the generation AI to select highly relevant content.

[0070] The reception unit may analyze the user's social media activity and receive related content when receiving content. The reception unit may include, for example, an algorithm for analyzing the user's social media activity. For example, the reception unit may analyze the user's posts and the user's followers' responses to identify related content. The reception unit may also include a method for preferentially receiving related content based on the user's social media activity. For example, the reception unit may preferentially receive content related to content shared by the user on social media. The reception unit may also analyze the user's social media activity history and preferentially receive content that is likely to be of interest to the user. The reception unit may also receive related content based on the posts of accounts the user follows. This allows the user's social media activity to be analyzed and highly relevant content to be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's social media data into a generation AI and cause the generation AI to select related content.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, includes an algorithm for estimating the user's emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate emotions. The analysis unit also includes a method for adjusting the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide simple, highly visible analysis results when the user is nervous. The analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. This allows the analysis results to be easily understood by adjusting the presentation method of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the 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 into the generation AI and have the generation AI estimate the emotion.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the content during analysis. The analysis unit, for example, includes an algorithm for evaluating the importance of the content. For example, the analysis unit can evaluate the influence and relevance of the content and identify the importance. The analysis unit also includes a method for adjusting the level of detail of the analysis based on the importance of the content. For example, the analysis unit can perform a detailed analysis on content with high importance. The analysis unit can also perform a simplified analysis on content with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the content. Some or all of the above-described 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 content importance data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the content category during analysis. The analysis unit, for example, includes an algorithm for identifying the content category. For example, the analysis unit can identify categories such as text, image, audio, and video. The analysis unit also includes a method for applying different analysis algorithms depending on the content category. For example, the analysis unit can apply a grammar analysis algorithm to text content. The analysis unit can apply a pixel analysis algorithm to image content. The analysis unit can also apply a frame analysis algorithm to video content. This improves the accuracy of analysis by applying different analysis algorithms depending on the content category. Some or all of the above-described 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 content category data to a generation AI and cause the generation AI to select an analysis algorithm to apply.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, includes an algorithm for estimating the user's emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate emotions. The analysis unit also includes a method for adjusting the length of the analysis based on the estimated user emotions. For example, the analysis unit can perform a short and concise analysis when the user is in a hurry. The analysis unit can perform a detailed analysis when the user is relaxed. Furthermore, the analysis unit can perform a visually stimulating analysis when the user is excited. This allows the analysis results to be provided according to the user's needs by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the 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 into the generation AI and have the generation AI estimate the emotion.

[0075] During analysis, the analysis unit can determine the analysis priority based on the submission time of the content. The analysis unit, for example, includes an algorithm that identifies the submission time of the content. For example, the analysis unit can identify the submission date or submission time and determine the analysis priority based on the submission time. The analysis unit also includes a method for adjusting the analysis order based on the submission time. For example, the analysis unit can prioritize analysis of the most recent content. The analysis unit can also postpone content that was submitted earlier. The analysis unit can also adjust the analysis order based on the submission time. As a result, by determining the analysis priority based on the submission time of the content, the most recent content can be analyzed preferentially. 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 submission time data to a generation AI and have the generation AI determine the analysis priority.

[0076] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the content. The analysis unit, for example, includes an algorithm for evaluating the relevance of the content. For example, the analysis unit can evaluate the relevance based on common keywords or related topics. The analysis unit also includes a method for preferentially analyzing highly relevant content. For example, the analysis unit can prioritize analyzing highly relevant content. The analysis unit can also postpone less relevant content. The analysis unit can also adjust the order of analysis based on the relevance of the content. As a result, by adjusting the order of analysis based on the relevance of the content, highly relevant content can be analyzed preferentially. 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 relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0077] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. The evaluation unit includes, for example, an algorithm for estimating the user's emotions. For example, the evaluation unit can analyze the user's facial expressions and voice to estimate the emotions. The evaluation unit also includes a method for adjusting the evaluation criteria based on the estimated user emotions. For example, the evaluation unit can relax strict evaluation criteria when the user is nervous. The evaluation unit can apply detailed evaluation criteria when the user is relaxed. Furthermore, the evaluation unit can apply simplified evaluation criteria when the user is in a hurry. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0078] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between the analysis results during the evaluation. The evaluation unit, for example, includes an algorithm for evaluating the interrelationships between the analysis results. For example, the evaluation unit can evaluate the interrelationships between grammatical analysis results and lexical analysis results. The evaluation unit can also evaluate the interrelationships between pixel arrangements and color patterns in an image. Furthermore, the evaluation unit can evaluate the interrelationships between video frame analysis results and audio analysis results. This improves the accuracy of the evaluation by taking the interrelationships between the analysis results into account. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input analysis result data into a generation AI and cause the generation AI to evaluate the interrelationships.

[0079] The evaluation unit can perform evaluation taking into account attribute information of the submitter of the content when performing evaluation. The evaluation unit, for example, includes an algorithm for acquiring the submitter's attribute information. For example, the evaluation unit can acquire attribute information such as the submitter's age, gender, and occupation. The evaluation unit also includes a method for adjusting evaluation criteria based on the submitter's attribute information. For example, the evaluation unit can perform evaluation taking into account the submitter's level of expertise. The evaluation unit can also perform evaluation taking into account the submitter's past submission history. Furthermore, the evaluation unit can adjust the evaluation criteria based on the submitter's attribute information. This allows for a more appropriate evaluation by taking into account the submitter's attribute information. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the submitter's attribute information data into a generation AI and cause the generation AI to adjust the evaluation criteria.

[0080] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated user emotions. The evaluation unit, for example, includes an algorithm for estimating the user's emotions. For example, the evaluation unit can analyze the user's facial expressions and voice to estimate emotions. The evaluation unit also includes a method for adjusting the order in which the evaluation results are displayed based on the estimated user emotions. For example, the evaluation unit can display important evaluation results first if the user is nervous. The evaluation unit can sequentially display detailed evaluation results if the user is relaxed. Furthermore, the evaluation unit can display summary evaluation results first if the user is in a hurry. This allows the order in which the evaluation results are displayed to be adjusted according to the user's emotions, thereby providing evaluation results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0081] The evaluation unit can perform the evaluation taking into account the geographical distribution of the content. The evaluation unit, for example, includes an algorithm for evaluating the geographical distribution of the content. For example, the evaluation unit can evaluate the distribution by region or geographical characteristics. The evaluation unit also includes a method for adjusting the evaluation criteria based on the geographical distribution. For example, the evaluation unit can prioritize evaluation of content related to a specific region. The evaluation unit can also adjust the evaluation criteria based on the geographical distribution. Furthermore, the evaluation unit can perform the evaluation taking into account trends by region. In this way, by taking the geographical distribution of the content into account, it is possible to perform an evaluation that reflects the characteristics of each region. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input geographical distribution data to a generation AI and cause the generation AI to adjust the evaluation criteria.

[0082] The evaluation unit can improve the accuracy of the evaluation by referring to related literature of the content during evaluation. The evaluation unit, for example, includes an algorithm for identifying related literature. For example, the evaluation unit can identify cited literature and reference literature and complement the evaluation criteria. The evaluation unit also includes a method for adjusting the evaluation results by taking into account the content of the related literature. For example, the evaluation unit can improve the accuracy of the evaluation based on the citation frequency of the related literature. As a result, the accuracy of the evaluation is improved by referring to the related literature. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input related literature data into a generation AI and have the generation AI complement the evaluation criteria.

[0083] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. The providing unit, for example, includes an algorithm for estimating the user's emotion. For example, the providing unit can analyze the user's facial expressions and voice to estimate the emotion. The providing unit also includes a method for adjusting the display method of the information to be provided based on the estimated user's emotion. For example, the providing unit can provide a simple, highly visible display method when the user is nervous. The providing unit can provide a display method including detailed information when the user is relaxed. Furthermore, the providing unit can provide a display method that focuses on the main points when the user is in a hurry. This allows the information to be provided in an easy-to-understand manner by adjusting the display method of the information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generating AI and cause the generating AI to estimate emotions.

[0084] The providing unit can select an appropriate display method by referring to the user's past operation history when providing the display method. The providing unit, for example, includes an algorithm that analyzes the user's past operation history. For example, the providing unit can analyze the user's operation log and usage history to select an optimal display method. The providing unit also includes a method for suggesting a display method based on the user's past operation history. For example, the providing unit can prioritize providing a display method that the user has previously preferred. The providing unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the providing unit can provide a customized display method based on the display method that the user has previously used. This makes it possible to provide an optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to a generation AI and cause the generation AI to select a display method.

[0085] The providing unit can estimate the user's emotions and adjust the operation procedures for the information to be provided based on the estimated user emotions. The providing unit, for example, includes an algorithm for estimating the user's emotions. For example, the providing unit can analyze the user's facial expressions and voice to estimate the emotions. The providing unit also includes a method for adjusting the operation procedures for the information to be provided based on the estimated user emotions. For example, the providing unit can simplify the operation procedures when the user is nervous. The providing unit can provide detailed operation procedures when the user is relaxed. Furthermore, the providing unit can provide procedures that allow quick operation when the user is in a hurry. This allows the operation procedures to be adjusted according to the user's emotions, thereby providing easy-to-use operation procedures for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generating AI and cause the generating AI to estimate emotions.

[0086] The providing unit can select the optimal display method by taking into account the user's device information when providing the display. The providing unit, for example, includes an algorithm for acquiring the user's device information. For example, the providing unit can acquire the device type and OS version. The providing unit also includes a method for selecting the optimal display method based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method including detailed information. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select a display method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, evaluation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text entered or images uploaded by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes grammar, vocabulary frequency, stylistic consistency, pixel layout of images, etc. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the AI ​​generation level and calculates a score based on the analysis results. The provision unit is realized, for example, by the output device 40 of the smart device 14 and visually displays the score and detailed explanation of the analysis results to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, evaluation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives text entered by the user and images uploaded by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes grammar, vocabulary frequency, stylistic consistency, pixel layout of images, etc. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the AI ​​generation level and calculates a score based on the analysis results. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the user with a detailed explanation of the score and analysis results by voice. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, evaluation unit, and provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives text entered by the user and images uploaded by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes grammar, vocabulary frequency, stylistic consistency, pixel layout of images, etc. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the AI ​​generation level based on the analysis results and calculates a score. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and visually displays the score and detailed explanation of the analysis results to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, evaluation unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives text entered by the user and images uploaded by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes grammar, vocabulary frequency, stylistic consistency, pixel layout of images, etc. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the AI ​​generation level based on the analysis results and calculates a score. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the user with a detailed explanation of the score and analysis results by voice.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The reception unit can analyze the user's past content submission history and select an appropriate reception method. For example, the reception unit can analyze the format and content of content submitted by the user in the past and select the optimal reception method. The reception unit can also suggest an optimal reception time period based on the user's past submission history. For example, the reception unit can analyze time periods during which the user frequently submitted content in the past and accept content during those time periods. The reception unit can also analyze trends in content submitted by the user in the past and preferentially accept related content. In this way, by analyzing the user's past submission history, the optimal reception method can be selected and content can be accepted efficiently. Some or all of the above-described 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 submission history data into a generation AI and have the generation AI select the optimal reception method.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the content. For example, the analysis unit can evaluate the impact and relevance of the content and identify the importance. The analysis unit also includes a method for adjusting the level of detail of the analysis based on the importance of the content. For example, the analysis unit can perform a detailed analysis on content with high importance. The analysis unit can also perform a simplified analysis on content with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the content. Some or all of the above-described 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 content importance data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0090] During evaluation, the evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between the analysis results. For example, the evaluation unit can evaluate the interrelationships between grammatical analysis results and lexical analysis results. The evaluation unit can also evaluate the interrelationships between image pixel arrangements and color patterns. Furthermore, the evaluation unit can evaluate the interrelationships between video frame analysis results and audio analysis results. This improves the accuracy of the evaluation by taking into account the interrelationships between the analysis results. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input analysis result data into a generation AI and have the generation AI evaluate the interrelationships.

[0091] The providing unit can select an appropriate display method by referring to the user's past operation history when providing the display method. For example, the providing unit can analyze the user's operation log and usage history to select the optimal display method. The providing unit also includes a method for suggesting a display method based on the user's past operation history. For example, the providing unit can preferentially provide a display method that the user has used preferably in the past. The providing unit can also suggest the optimal display method from the user's past operation history. Furthermore, the providing unit can provide a customized display method based on the display method that the user has used in the past. This makes it possible to provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to a generation AI and cause the generation AI to select a display method.

[0092] When receiving content, the reception unit can prioritize receiving highly relevant content by taking into account the user's geographical location information. For example, the reception unit can acquire the user's geographical location information using GPS data or a location information service. The reception unit also includes a method for preferentially receiving highly relevant content based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving content related to that area. The reception unit can also prioritize receiving content related to local trends based on the user's location information. Furthermore, when the user is traveling, the reception unit can prioritize receiving content related to the travel destination. In this way, highly relevant content can be prioritized by taking the user's geographical location information into account. Some or all of the above-described 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 location information data to a generation AI and cause the generation AI to select highly relevant content.

[0093] The reception unit can estimate the user's emotions and adjust the timing of content reception based on the estimated user emotions. For example, the reception unit can analyze the user's facial expressions and voice to estimate emotions. The reception unit also includes a method for adjusting the timing of content reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of content reception to provide the user with time to relax. If the user is relaxed, the reception unit can immediately accept content and start analyzing it. Furthermore, if the user is in a hurry, the reception unit can prioritize accepting content and quickly analyze it. This allows the user to reduce stress and receive content at an appropriate time by adjusting the timing of content reception based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0094] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate emotions. The analysis unit also includes a method for adjusting the presentation method of the analysis based on the estimated user emotions. For example, the analysis unit can provide simple, highly visible analysis results when the user is nervous. The analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described 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 into the generation AI and have the generation AI estimate the emotion.

[0095] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, the evaluation unit can analyze the user's facial expressions and voice to estimate emotions. The evaluation unit also includes a method for adjusting the evaluation criteria based on the estimated user emotions. For example, the evaluation unit can relax strict evaluation criteria when the user is nervous. The evaluation unit can apply detailed evaluation criteria when the user is relaxed. Furthermore, the evaluation unit can apply simplified evaluation criteria when the user is in a hurry. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0096] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user emotions. For example, the providing unit can analyze the user's facial expressions and voice to estimate emotions. The providing unit also includes a method for adjusting the display method of the information to be provided based on the estimated user emotions. For example, the providing unit can provide a simple, highly visible display method when the user is nervous. The providing unit can provide a display method including detailed information when the user is relaxed. Furthermore, the providing unit can provide a display method that focuses on the main points when the user is in a hurry. This allows the information to be provided in an easily understandable manner by adjusting the display method of the information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0097] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, the providing unit can acquire the device type and OS version. The providing unit also includes a method for selecting the optimal display method based on the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. If the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a display method including detailed information. This makes it possible to provide the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select a display method.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit receives content from a user. The content from a user includes text, images, audio, video, etc. For example, the reception unit receives text entered by the user, images uploaded by the user, and recorded audio files. Step 2: The analysis unit analyzes the content received by the reception unit. The analysis unit is equipped with algorithms that analyze grammar, vocabulary frequency, stylistic consistency, image pixel layout, color patterns, etc. For example, the analysis unit analyzes the grammatical structure of text and the pixel layout of images to evaluate the degree of AI generation. Step 3: The evaluation unit evaluates the AI ​​generation based on the results of the analysis by the analysis unit, calculating a score based on grammatical accuracy, vocabulary diversity, image color patterns, etc. Step 4: The providing unit provides the evaluation results obtained by the evaluation unit. The providing unit has an interface that provides a method for displaying the score and a detailed explanation of the analysis results. For example, the providing unit visually displays the score to the user and provides a detailed explanation of the analysis results.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] 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.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] 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.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0171] [Explanation of symbols]

[0172] 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 content from a user; an analysis unit that analyzes the content received by the reception unit; an evaluation unit that evaluates the AI ​​generation degree based on the analysis result by the analysis unit; a providing unit that provides the evaluation result obtained by the evaluation unit. A system characterized by:

2. The analysis unit It has algorithms that analyze grammar, vocabulary frequency, and stylistic consistency.

2. The system of claim 1.

3. The evaluation unit Equipped with a method for scoring the AI ​​generation degree based on the analysis results 2. The system of claim 1.

4. The providing unit It has an interface that provides detailed explanations of how scores are displayed and analysis results.

2. The system of claim 1.

5. The analysis unit It has algorithms that analyze pixel arrangement and color patterns in images.

2. The system of claim 1.

6. The evaluation unit Equipped with a method for scoring the AI ​​generation degree based on the image analysis results 2. The system of claim 1.

7. The reception unit Estimates user emotions and adjusts content reception timing based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past content submission history and select the appropriate reception method 2. The system of claim 1.

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