System

Abstract

An object of a system according to an embodiment is to analyze image data using AI and perform fair evaluation.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a setting unit, an evaluation unit, a calculation unit, and a creation unit. The collection unit collects image data. The analysis unit analyzes the image data collected by the collection unit. The setting unit sets an evaluation criterion based on the analysis result obtained by the analysis unit. The evaluation unit evaluates each element based on the evaluation criterion set by the setting unit. The calculation unit calculates a point addition point based on the evaluation result obtained by the evaluation unit. The creation part creates an advice sheet based on the added point calculated by the calculation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, evaluations of entertainment, sports, art, etc. depend on the judges' subjective judgment, which can lead to a lack of fairness.

[0005] The system according to the embodiment aims to analyze image data using AI and provide a fair evaluation. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a setting unit, an evaluation unit, a calculation unit, and a creation unit. The collection unit collects image data. The analysis unit analyzes the image data collected by the collection unit. The setting unit sets evaluation criteria based on the analysis results obtained by the analysis unit. The evaluation unit evaluates each element based on the evaluation criteria set by the setting unit. The calculation unit calculates added points based on the evaluation results obtained by the evaluation unit. The creation unit creates an advice sheet based on the added points calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze image data using AI and provide fair evaluation. [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 evaluation system according to an embodiment of the present invention uses AI to fairly evaluate entertainment, sports, art, and the like. This evaluation system collects and analyzes image data, sets evaluation criteria, evaluates each element, calculates bonus points, and creates an advice sheet. For example, the evaluation system can analyze the movements of athletes in a sports game and suggest areas for technical improvement. Furthermore, when evaluating an artwork, the evaluation system can analyze the composition and color balance of the artwork and provide specific advice. This allows the evaluation system to be fair and promote the growth of participants. For example, when evaluating a sports game, the evaluation system can analyze the movements of athletes and suggest areas for technical improvement. Furthermore, when evaluating an artwork, the evaluation system can analyze the composition and color balance of the artwork and provide specific advice.

[0029] The evaluation system according to the embodiment includes a collection unit, an analysis unit, a setting unit, an evaluation unit, a calculation unit, and a creation unit. The collection unit collects image data. The collection unit can collect, for example, image data of sports games or artworks. The collection unit can collect image data in real time, for example, using a camera. The collection unit can also collect image data from the Internet. The collection unit can also collect image data provided by users. The analysis unit analyzes the image data collected by the collection unit. The analysis unit can analyze the image data using, for example, an image recognition algorithm. The analysis unit can also analyze the image data using a feature extraction method. The analysis unit can also analyze the image data using AI. The setting unit sets evaluation criteria based on the analysis results obtained by the analysis unit. The setting unit can, for example, set evaluation items. The setting unit can also set a scoring method. The setting unit can also set evaluation criteria using AI. The evaluation unit evaluates each element based on the evaluation criteria set by the setting unit. The evaluation unit can evaluate, for example, technical elements. The evaluation unit can also evaluate design elements. Furthermore, the evaluation unit can evaluate each element using AI. The calculation unit calculates added points based on the evaluation results obtained by the evaluation unit. The calculation unit can calculate added points using, for example, a point allocation method based on the evaluation results. Furthermore, the calculation unit can calculate added points using AI. The creation unit creates an advice sheet based on the added points calculated by the calculation unit. The creation unit can, for example, set the feedback format. Furthermore, the creation unit can set the level of detail of the content. Furthermore, the creation unit can create the advice sheet using AI. As a result, the evaluation system according to the embodiment can achieve fair evaluation by consistently performing processes from collecting image data to analyzing, evaluating, and creating an advice sheet.

[0030] The collection unit can collect image data of sports games or artworks. For example, the collection unit can collect image data in real time using a camera during a sports game. The collection unit can also collect image data using a camera at an art exhibition. Furthermore, the collection unit can collect image data of sports games or artworks from the Internet. This allows for evaluation in a wide range of fields by collecting image data of sports games or artworks.

[0031] The analysis unit can analyze the collected image data and set evaluation criteria for each element. The analysis unit can analyze the collected image data using, for example, an image recognition algorithm. The analysis unit can also analyze the collected image data using a feature extraction method. Furthermore, the analysis unit can analyze the collected image data using AI. This allows for objective evaluation by setting evaluation criteria based on the analysis of the image data.

[0032] The evaluation unit can evaluate each element based on the set evaluation criteria. The evaluation unit can evaluate, for example, technical elements. The evaluation unit can also evaluate design elements. Furthermore, the evaluation unit can evaluate each element using AI. This enables fair evaluation by evaluating each element based on the set evaluation criteria.

[0033] The calculation unit can calculate the added points based on the evaluation results. For example, the calculation unit can calculate the added points using a point allocation method based on the evaluation results. The calculation unit can also calculate the added points using AI. This allows for specific evaluation by calculating the added points based on the evaluation results.

[0034] The creation unit can create an advice sheet based on the added points. The creation unit can, for example, set the format of the feedback. The creation unit can also set the level of detail of the content. Furthermore, the creation unit can create an advice sheet using AI. This makes it possible to provide specific feedback by creating an advice sheet based on the added points.

[0035] The collection unit can optimize the collection of image data based on specific events or situations. For example, the collection unit can collect image data when a goal or an important play occurs during a sports game. The collection unit can also collect image data when an important step in the creation of an artwork is completed. Furthermore, the collection unit can collect image data during a presentation or discussion at a workshop. In this way, by optimizing collection based on specific events or situations, data can be collected without missing important moments.

[0036] The collection unit can improve the accuracy of the analysis by collecting image data from different viewpoints or angles. For example, the collection unit can collect image data from different viewpoints using multiple cameras during a sports game. The collection unit can also collect image data by photographing artworks from various angles at an art exhibition. Furthermore, the collection unit can record activities in a workshop from multiple viewpoints to enable detailed analysis. This allows data collection from different viewpoints and angles to improve the accuracy of the analysis.

[0037] The collection unit can select the optimal collection method by referring to the user's past collection history. For example, the collection unit can analyze patterns of image data that the user has preferred to collect in the past and collect data in a similar manner. The collection unit can also optimize the collection method for specific events or situations based on the user's past collection history. Furthermore, the collection unit can select the most effective collection timing based on the user's past collection history. In this way, the optimal collection method can be selected by referring to the past collection history.

[0038] The collection unit can prioritize collecting highly relevant image data by taking into account the user's geographical location information. For example, if the user is at a particular sports venue, the collection unit can prioritize collecting image data of matches at that venue. Also, if the user is at an art museum, the collection unit can prioritize collecting image data of exhibits at the art museum. Furthermore, if the user is at a workshop venue, the collection unit can prioritize collecting image data of activities at the venue. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information.

[0039] The collection unit can analyze the user's social media activities and collect related image data. For example, the collection unit can collect image data of events shared by the user on social media. The collection unit can also analyze the content posted by the user on social media and collect related image data. Furthermore, the collection unit can also collect related image data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can adjust the collection method based on feedback provided by the user in the past. The collection unit can also preferentially adopt a specific collection method based on the user's past feedback. Furthermore, the collection unit can customize the collection timing and viewpoint by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the image data. For example, the analysis unit can perform a detailed analysis on image data of an important match scene. The analysis unit can also perform a detailed analysis on image data of an important work of art. Furthermore, the analysis unit can also perform a detailed analysis on image data of an important workshop activity scene. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the image data. For example, the analysis unit can apply a motion analysis algorithm to image data of a sports game. The analysis unit can also apply a color analysis algorithm to image data of a work of art. Furthermore, the analysis unit can apply a behavior analysis algorithm to image data of workshop activity scenes. This improves the accuracy of the analysis by applying an analysis algorithm according to the category.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can analyze similar patterns based on the user's past analysis results. The analysis unit can also preferentially adopt a specific analysis method based on the user's past analysis results. Furthermore, the analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results.

[0044] The analysis unit can determine the priority of analysis based on the time when the image data was collected. For example, the analysis unit can prioritize analysis of the most recently collected image data. The analysis unit can also prioritize analysis of image data of important events. Furthermore, the analysis unit can also prioritize analysis of image data from a time specified by the user. This allows for efficient analysis by determining the priority based on the time when the image data was collected.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the image data. For example, the analysis unit can prioritize analysis of highly relevant image data. The analysis unit can also postpone analysis of less relevant image data. Furthermore, the analysis unit can prioritize analysis of highly relevant image data specified by the user. This allows for efficient analysis by adjusting the order of analysis based on relevance.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can use appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, it is possible to provide analysis results that are suitable for the user.

[0047] The setting unit can optimize the current evaluation criteria by referring to past evaluation criterion data. The setting unit can optimize the current evaluation criteria, for example, based on past evaluation criterion data. The setting unit can also preferentially adopt specific evaluation criteria from the past evaluation criterion data. Furthermore, the setting unit can also adjust the evaluation criteria by referring to the past evaluation criterion data. In this way, the current evaluation criteria can be optimized by referring to the past evaluation criterion data.

[0048] The setting unit can apply different evaluation criteria to each category of image data. For example, the setting unit can apply a movement evaluation criterion to image data of a sports game. The setting unit can also apply a color evaluation criterion to image data of an artistic work. Furthermore, the setting unit can also apply a behavior evaluation criterion to image data of a workshop activity scene. In this way, applying different evaluation criteria to each category enables more appropriate evaluation.

[0049] The setting unit can set the evaluation criteria based on the user's attribute information. The setting unit can set the evaluation criteria according to the user's age and gender, for example. The setting unit can also set the evaluation criteria according to the user's level of expertise. Furthermore, the setting unit can also set the evaluation criteria according to the user's interests and concerns. In this way, by taking the attribute information into consideration, it is possible to set evaluation criteria that are suitable for the user.

[0050] The setting unit can analyze changes in the evaluation criteria based on the collection time of the image data. The setting unit can set the evaluation criteria based on the most recently collected image data, for example. The setting unit can also set the evaluation criteria based on image data of an important event. Furthermore, the setting unit can also set the evaluation criteria based on image data from a time specified by the user. In this way, by analyzing changes in the evaluation criteria based on the collection time, it is possible to set appropriate evaluation criteria.

[0051] The setting unit can set the evaluation criteria by referring to related market data. The setting unit can set the evaluation criteria based on, for example, current market trends. The setting unit can also set the evaluation criteria by referring to past market data. Furthermore, the setting unit can also set the evaluation criteria based on future market forecast data. In this way, by referring to market data, it is possible to set evaluation criteria based on trends.

[0052] The setting unit can set the evaluation criteria taking into consideration the technological maturity. For example, the setting unit can set strict evaluation criteria for a technologically mature field. The setting unit can also set flexible evaluation criteria for a technologically new field. Furthermore, the setting unit can adjust the evaluation criteria according to the technological maturity. In this way, appropriate evaluation criteria can be set by taking into consideration the technological maturity.

[0053] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the image data. For example, the evaluation unit can perform a detailed evaluation on image data of an important game scene. The evaluation unit can also perform a detailed evaluation on image data of an important work of art. Furthermore, the evaluation unit can also perform a detailed evaluation on image data of an important workshop activity scene. This allows for efficient evaluation by adjusting the level of detail of the evaluation based on the importance.

[0054] The evaluation unit can apply different evaluation algorithms depending on the category of the image data. For example, the evaluation unit can apply a movement evaluation algorithm to image data of a sports game. The evaluation unit can also apply a color evaluation algorithm to image data of an artwork. Furthermore, the evaluation unit can also apply a behavior evaluation algorithm to image data of a workshop activity scene. In this way, the application of an evaluation algorithm according to the category improves the accuracy of the evaluation.

[0055] The evaluation unit can improve the accuracy of the evaluation by referring to the user's past evaluation results. For example, the evaluation unit can evaluate similar patterns based on the user's past evaluation results. The evaluation unit can also preferentially adopt a specific evaluation method based on the user's past evaluation results. Furthermore, the evaluation unit can adjust the evaluation algorithm by referring to the user's past evaluation results. In this way, the accuracy of the evaluation can be improved by referring to the past evaluation results.

[0056] The evaluation unit can determine the priority of evaluation based on the time of collection of image data. For example, the evaluation unit can prioritize evaluation of image data collected most recently. The evaluation unit can also prioritize evaluation of image data of important events. Furthermore, the evaluation unit can also prioritize evaluation of image data collected at a time specified by the user. This allows for efficient evaluation by determining the priority based on the time of collection.

[0057] The evaluation unit can adjust the order of evaluation based on the relevance of the image data. For example, the evaluation unit can prioritize evaluation of highly relevant image data. The evaluation unit can also postpone evaluation of less relevant image data. Furthermore, the evaluation unit can prioritize evaluation of highly relevant image data specified by the user. This allows for efficient evaluation by adjusting the order of evaluation based on relevance.

[0058] The evaluation unit can adjust the use of technical terminology in the evaluation depending on the user's level of expertise. For example, if the user is an expert, the evaluation unit can provide an evaluation result that uses a lot of technical terminology. Also, if the user is a beginner, the evaluation unit can provide an evaluation result that avoids technical terminology. Furthermore, the evaluation unit can use appropriate technical terminology depending on the user's level of expertise. In this way, by adjusting the use of technical terminology depending on the level of expertise, it is possible to provide an evaluation result that is suitable for the user.

[0059] The calculation unit can adjust the level of detail of the added points based on the importance of the evaluation result. For example, the calculation unit can calculate detailed added points for the evaluation result of an important match scene. The calculation unit can also calculate detailed added points for the evaluation result of an important artwork. Furthermore, the calculation unit can calculate detailed added points for the evaluation result of an important workshop activity scene. In this way, by adjusting the level of detail based on the importance, efficient calculation of added points is possible.

[0060] The calculation unit can apply different scoring point calculation algorithms depending on the category of the evaluation result. For example, the calculation unit can calculate scoring points by applying a motion evaluation algorithm to the evaluation result of a sports game. The calculation unit can also calculate scoring points by applying a color evaluation algorithm to the evaluation result of a work of art. Furthermore, the calculation unit can calculate scoring points by applying a behavior evaluation algorithm to the evaluation result of a workshop activity scene. This improves the accuracy of scoring points by applying a calculation algorithm according to the category.

[0061] The calculation unit can improve the accuracy of the calculation by referring to the user's past calculation results of the added points. For example, the calculation unit can calculate a similar pattern based on the user's past calculation results of the added points. The calculation unit can also preferentially adopt a specific calculation method based on the user's past calculation results of the added points. Furthermore, the calculation unit can adjust the calculation algorithm by referring to the user's past calculation results of the added points. In this way, the accuracy of the added points can be improved by referring to the past calculation results.

[0062] The calculation unit can determine the priority of the added points based on the collection time of the evaluation results. The calculation unit can calculate the added points based on the most recently collected evaluation results, for example. The calculation unit can also calculate the added points based on the evaluation results of important events. Furthermore, the calculation unit can calculate the added points based on the evaluation results at a time specified by the user. In this way, by determining the priority based on the collection time, it is possible to efficiently calculate the added points.

[0063] The calculation unit can adjust the order of added points based on the relevance of the evaluation results. The calculation unit can, for example, calculate added points based on highly relevant evaluation results. The calculation unit can also calculate added points by putting less relevant evaluation results on the back burner. Furthermore, the calculation unit can calculate added points based on highly relevant evaluation results specified by the user. This allows for efficient calculation of added points by adjusting the order based on relevance.

[0064] The calculation unit can adjust the level of detail of the added points according to the user's level of expertise. For example, if the user is an expert, the calculation unit can calculate detailed added points. Also, if the user is a beginner, the calculation unit can calculate simple added points. Furthermore, the calculation unit can calculate appropriate added points according to the user's level of expertise. In this way, by adjusting the level of detail according to the level of expertise, it is possible to calculate added points appropriate for the user.

[0065] The creation unit can adjust the level of detail of the advice sheet based on the importance of the added points. For example, the creation unit can provide detailed advice for important added points. The creation unit can also provide concise advice for less important added points. Furthermore, the creation unit can adjust the level of detail of the advice according to the importance of the added points. This allows for efficient creation of advice sheets by adjusting the level of detail based on the importance.

[0066] The creation unit can apply different advice sheet creation algorithms depending on the category of added points. For example, the creation unit can apply an algorithm that provides technical advice to added points for a sports game. The creation unit can also apply an algorithm that provides creative advice to added points for an artistic work. Furthermore, the creation unit can apply an algorithm that provides practical advice to added points for a workshop activity scene. In this way, the accuracy of the advice sheet is improved by applying a creation algorithm according to the category.

[0067] The creation unit can improve the accuracy of creation by referring to the user's past advice sheet creation results. For example, the creation unit can create a similar pattern based on the user's past advice sheet creation results. The creation unit can also preferentially adopt a specific creation method based on the user's past advice sheet creation results. Furthermore, the creation unit can also adjust the creation algorithm by referring to the user's past advice sheet creation results. In this way, the accuracy of the advice sheet is improved by referring to the past creation results.

[0068] The creation unit can determine the priority of advice sheets based on the time when the added points were collected. The creation unit can provide advice based on the most recently collected added points, for example. The creation unit can also provide advice based on the added points of important events. Furthermore, the creation unit can also provide advice based on the added points at a time specified by the user. This allows for efficient creation of advice sheets by determining the priority based on the time when the points were collected.

[0069] The creation unit can adjust the order of the advice sheets based on the relevance of the added points. For example, the creation unit can provide advice based on highly relevant added points. The creation unit can also provide advice by putting less relevant added points on hold. Furthermore, the creation unit can also provide advice based on highly relevant added points specified by the user. This allows for efficient creation of advice sheets by adjusting the order based on relevance.

[0070] The creation unit can adjust the level of detail of the advice sheet according to the user's level of expertise. For example, if the user is an expert, the creation unit can provide detailed advice. Also, if the user is a beginner, the creation unit can provide concise advice. Furthermore, the creation unit can provide advice with an appropriate level of detail according to the user's level of expertise. In this way, by adjusting the level of detail according to the level of expertise, an advice sheet suitable for the user can be provided.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The rating system can also dynamically adjust the rating criteria by referring to the user's past rating history. For example, it can set rating criteria that emphasize elements that the user previously rated highly. It can also set rating criteria that improve elements that the user previously rated poorly. It can also adjust the weighting of the rating criteria based on the user's past rating history. This promotes user growth and enables more personalized ratings.

[0073] The rating system can also combine the opinions of different evaluators to produce an overall rating. For example, multiple evaluators can evaluate the same sports game and combine their evaluation results to calculate a final rating. It can also have different evaluators evaluate a work of art and combine their evaluation results to provide comprehensive advice. Furthermore, the weighting of the evaluation results can be adjusted based on the evaluator's expertise and experience. This allows for a more multifaceted and fair evaluation.

[0074] The evaluation system can also have a function to share the evaluation results with other users. For example, the evaluation results of a sports game can be shared with team members so that everyone can identify areas for improvement. The evaluation results of an artwork can also be shared within the community to receive feedback from other artists. Furthermore, the evaluation results can be shared on social media to gather a wide range of opinions. This allows feedback from a wider range of perspectives to be obtained by sharing the evaluation results.

[0075] The evaluation system can also provide training plans based on the evaluation results. For example, it can suggest training menus to athletes based on areas for improvement in their techniques. It can also provide practice assignments to artists based on areas for improvement in their work. It can also suggest practice methods to workshop participants based on areas for improvement in their presentations. In this way, specific training plans based on the evaluation results can be provided, supporting the growth of users.

[0076] The evaluation system can further introduce a reward system based on the evaluation results. For example, if an athlete receives a high evaluation, they can be offered specific training opportunities or resources. If an artist receives a high evaluation, they can be offered the opportunity to participate in an exhibition. Furthermore, if a workshop participant receives a high evaluation, they can be offered priority participation in the next workshop. In this way, a reward system based on the evaluation results can increase user motivation.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The collection unit collects image data. For example, the collection unit can collect image data of sports games or artworks. The collection unit can collect image data in real time using a camera. It can also collect image data from the Internet. It can also collect image data provided by users. Step 2: The analysis unit analyzes the image data collected by the collection unit. The analysis unit can analyze the image data using image recognition algorithms, feature extraction methods, and AI. Step 3: The setting unit sets evaluation criteria based on the analysis results obtained by the analysis unit. The setting unit can set evaluation items and scoring methods, and can also set evaluation criteria using AI. Step 4: The evaluation unit evaluates each element based on the evaluation criteria set by the setting unit. The evaluation unit can evaluate technical elements and design elements, and can also evaluate each element using AI. Step 5: The calculation unit calculates the points based on the evaluation results obtained by the evaluation unit. The calculation unit can calculate the points using a point allocation method based on the evaluation results, or can calculate the points using AI. Step 6: The creation unit creates an advice sheet based on the added points calculated by the calculation unit. The creation unit can set the feedback format and level of detail, and can also create the advice sheet using AI.

[0079] (Example 2) An evaluation system according to an embodiment of the present invention uses AI to fairly evaluate entertainment, sports, art, and the like. This evaluation system collects and analyzes image data, sets evaluation criteria, evaluates each element, calculates bonus points, and creates an advice sheet. For example, the evaluation system can analyze the movements of athletes in a sports game and suggest areas for technical improvement. Furthermore, when evaluating an artwork, the evaluation system can analyze the composition and color balance of the artwork and provide specific advice. This allows the evaluation system to be fair and promote the growth of participants. For example, when evaluating a sports game, the evaluation system can analyze the movements of athletes and suggest areas for technical improvement. Furthermore, when evaluating an artwork, the evaluation system can analyze the composition and color balance of the artwork and provide specific advice.

[0080] The evaluation system according to the embodiment includes a collection unit, an analysis unit, a setting unit, an evaluation unit, a calculation unit, and a creation unit. The collection unit collects image data. The collection unit can collect, for example, image data of sports games or artworks. The collection unit can collect image data in real time, for example, using a camera. The collection unit can also collect image data from the Internet. The collection unit can also collect image data provided by users. The analysis unit analyzes the image data collected by the collection unit. The analysis unit can analyze the image data using, for example, an image recognition algorithm. The analysis unit can also analyze the image data using a feature extraction method. The analysis unit can also analyze the image data using AI. The setting unit sets evaluation criteria based on the analysis results obtained by the analysis unit. The setting unit can, for example, set evaluation items. The setting unit can also set a scoring method. The setting unit can also set evaluation criteria using AI. The evaluation unit evaluates each element based on the evaluation criteria set by the setting unit. The evaluation unit can evaluate, for example, technical elements. The evaluation unit can also evaluate design elements. Furthermore, the evaluation unit can evaluate each element using AI. The calculation unit calculates added points based on the evaluation results obtained by the evaluation unit. The calculation unit can calculate added points using, for example, a point allocation method based on the evaluation results. Furthermore, the calculation unit can calculate added points using AI. The creation unit creates an advice sheet based on the added points calculated by the calculation unit. The creation unit can, for example, set the feedback format. Furthermore, the creation unit can set the level of detail of the content. Furthermore, the creation unit can create the advice sheet using AI. As a result, the evaluation system according to the embodiment can achieve fair evaluation by consistently performing processes from collecting image data to analyzing, evaluating, and creating an advice sheet.

[0081] The collection unit can collect image data of sports games or artworks. For example, the collection unit can collect image data in real time using a camera during a sports game. The collection unit can also collect image data using a camera at an art exhibition. Furthermore, the collection unit can collect image data of sports games or artworks from the Internet. This allows for evaluation in a wide range of fields by collecting image data of sports games or artworks.

[0082] The analysis unit can analyze the collected image data and set evaluation criteria for each element. The analysis unit can analyze the collected image data using, for example, an image recognition algorithm. The analysis unit can also analyze the collected image data using a feature extraction method. Furthermore, the analysis unit can analyze the collected image data using AI. This allows for objective evaluation by setting evaluation criteria based on the analysis of the image data.

[0083] The evaluation unit can evaluate each element based on the set evaluation criteria. The evaluation unit can evaluate, for example, technical elements. The evaluation unit can also evaluate design elements. Furthermore, the evaluation unit can evaluate each element using AI. This enables fair evaluation by evaluating each element based on the set evaluation criteria.

[0084] The calculation unit can calculate the added points based on the evaluation results. For example, the calculation unit can calculate the added points using a point allocation method based on the evaluation results. The calculation unit can also calculate the added points using AI. This allows for specific evaluation by calculating the added points based on the evaluation results.

[0085] The creation unit can create an advice sheet based on the added points. The creation unit can, for example, set the format of the feedback. The creation unit can also set the level of detail of the content. Furthermore, the creation unit can create an advice sheet using AI. This makes it possible to provide specific feedback by creating an advice sheet based on the added points.

[0086] The collection unit can estimate the user's emotions and adjust the timing of image data collection based on the estimated user emotions. For example, when the user is excited, the collection unit can collect image data in real time and immediately analyze it. Furthermore, when the user is relaxed, the collection unit can periodically collect image data and analyze it later. Furthermore, when the user is concentrating, the collection unit can collect image data more frequently so as not to miss important moments. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The collection unit can optimize the collection of image data based on specific events or situations. For example, the collection unit can collect image data when a goal or an important play occurs during a sports game. The collection unit can also collect image data when an important step in the creation of an artwork is completed. Furthermore, the collection unit can collect image data during a presentation or discussion at a workshop. In this way, by optimizing collection based on specific events or situations, data can be collected without missing important moments.

[0088] The collection unit can improve the accuracy of the analysis by collecting image data from different viewpoints or angles. For example, the collection unit can collect image data from different viewpoints using multiple cameras during a sports game. The collection unit can also collect image data by photographing artworks from various angles at an art exhibition. Furthermore, the collection unit can record activities in a workshop from multiple viewpoints to enable detailed analysis. This allows data collection from different viewpoints and angles to improve the accuracy of the analysis.

[0089] The collection unit can select the optimal collection method by referring to the user's past collection history. For example, the collection unit can analyze patterns of image data that the user has preferred to collect in the past and collect data in a similar manner. The collection unit can also optimize the collection method for specific events or situations based on the user's past collection history. Furthermore, the collection unit can select the most effective collection timing based on the user's past collection history. In this way, the optimal collection method can be selected by referring to the past collection history.

[0090] The collection unit can estimate the user's emotions and determine the priority of image data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting important moments. Also, if the user is relaxed, the collection unit can collect data by focusing on the overall flow. Furthermore, if the user is focused, the collection unit can collect data by focusing on details. In this way, by determining priorities based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0091] The collection unit can prioritize collecting highly relevant image data by taking into account the user's geographical location information. For example, if the user is at a particular sports venue, the collection unit can prioritize collecting image data of matches at that venue. Also, if the user is at an art museum, the collection unit can prioritize collecting image data of exhibits at the art museum. Furthermore, if the user is at a workshop venue, the collection unit can prioritize collecting image data of activities at the venue. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information.

[0092] The collection unit can analyze the user's social media activities and collect related image data. For example, the collection unit can collect image data of events shared by the user on social media. The collection unit can also analyze the content posted by the user on social media and collect related image data. Furthermore, the collection unit can also collect related image data by referring to the activities of the user's friends on social media. In this way, related data can be efficiently collected by analyzing social media activities.

[0093] The collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can adjust the collection method based on feedback provided by the user in the past. The collection unit can also preferentially adopt a specific collection method based on the user's past feedback. Furthermore, the collection unit can customize the collection timing and viewpoint by referring to the user's past feedback. In this way, the collection method can be optimized by reflecting the past feedback.

[0094] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can also provide visually appealing analysis results. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the image data. For example, the analysis unit can perform a detailed analysis on image data of an important match scene. The analysis unit can also perform a detailed analysis on image data of an important work of art. Furthermore, the analysis unit can also perform a detailed analysis on image data of an important workshop activity scene. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance.

[0096] The analysis unit can apply different analysis algorithms depending on the category of the image data. For example, the analysis unit can apply a motion analysis algorithm to image data of a sports game. The analysis unit can also apply a color analysis algorithm to image data of a work of art. Furthermore, the analysis unit can apply a behavior analysis algorithm to image data of workshop activity scenes. This improves the accuracy of the analysis by applying an analysis algorithm according to the category.

[0097] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can analyze similar patterns based on the user's past analysis results. The analysis unit can also preferentially adopt a specific analysis method based on the user's past analysis results. Furthermore, the analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results.

[0098] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can perform a short and to-the-point analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can also perform a visually appealing analysis. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The analysis unit can determine the priority of analysis based on the time when the image data was collected. For example, the analysis unit can prioritize analysis of the most recently collected image data. The analysis unit can also prioritize analysis of image data of important events. Furthermore, the analysis unit can also prioritize analysis of image data from a time specified by the user. This allows for efficient analysis by determining the priority based on the time when the image data was collected.

[0100] The analysis unit can adjust the order of analysis based on the relevance of the image data. For example, the analysis unit can prioritize analysis of highly relevant image data. The analysis unit can also postpone analysis of less relevant image data. Furthermore, the analysis unit can prioritize analysis of highly relevant image data specified by the user. This allows for efficient analysis by adjusting the order of analysis based on relevance.

[0101] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can use appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, it is possible to provide analysis results that are suitable for the user.

[0102] The setting unit can estimate the user's emotions and adjust the method for setting the evaluation criteria based on the estimated user's emotions. For example, if the user is relaxed, the setting unit can set detailed evaluation criteria. Furthermore, if the user is in a hurry, the setting unit can set simple evaluation criteria. Furthermore, if the user is excited, the setting unit can set visually appealing evaluation criteria. In this way, by adjusting the method for setting the evaluation criteria based on the user's emotions, more appropriate evaluation criteria can be set. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The setting unit can optimize the current evaluation criteria by referring to past evaluation criterion data. The setting unit can optimize the current evaluation criteria, for example, based on past evaluation criterion data. The setting unit can also preferentially adopt specific evaluation criteria from the past evaluation criterion data. Furthermore, the setting unit can also adjust the evaluation criteria by referring to the past evaluation criterion data. In this way, the current evaluation criteria can be optimized by referring to the past evaluation criterion data.

[0104] The setting unit can apply different evaluation criteria to each category of image data. For example, the setting unit can apply a movement evaluation criterion to image data of a sports game. The setting unit can also apply a color evaluation criterion to image data of an artistic work. Furthermore, the setting unit can also apply a behavior evaluation criterion to image data of a workshop activity scene. In this way, applying different evaluation criteria to each category enables more appropriate evaluation.

[0105] The setting unit can set the evaluation criteria based on the user's attribute information. The setting unit can set the evaluation criteria according to the user's age and gender, for example. The setting unit can also set the evaluation criteria according to the user's level of expertise. Furthermore, the setting unit can also set the evaluation criteria according to the user's interests and concerns. In this way, by taking the attribute information into consideration, it is possible to set evaluation criteria that are suitable for the user.

[0106] The setting unit can estimate the user's emotions and determine the priority of the evaluation criteria based on the estimated user's emotions. For example, when the user is excited, the setting unit can prioritize important evaluation criteria. Furthermore, when the user is relaxed, the setting unit can also set overall evaluation criteria evenly. Furthermore, when the user is focused, the setting unit can also prioritize evaluation criteria that focus on details. In this way, by determining the priority based on the user's emotions, important evaluation criteria can be prioritized. Emotion estimation is realized using an emotion estimation function using, 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.

[0107] The setting unit can analyze changes in the evaluation criteria based on the collection time of the image data. The setting unit can set the evaluation criteria based on the most recently collected image data, for example. The setting unit can also set the evaluation criteria based on image data of an important event. Furthermore, the setting unit can also set the evaluation criteria based on image data from a time specified by the user. In this way, by analyzing changes in the evaluation criteria based on the collection time, it is possible to set appropriate evaluation criteria.

[0108] The setting unit can set the evaluation criteria by referring to related market data. The setting unit can set the evaluation criteria based on, for example, current market trends. The setting unit can also set the evaluation criteria by referring to past market data. Furthermore, the setting unit can also set the evaluation criteria based on future market forecast data. In this way, by referring to market data, it is possible to set evaluation criteria based on trends.

[0109] The setting unit can set the evaluation criteria taking into consideration the technological maturity. For example, the setting unit can set strict evaluation criteria for a technologically mature field. The setting unit can also set flexible evaluation criteria for a technologically new field. Furthermore, the setting unit can adjust the evaluation criteria according to the technological maturity. In this way, appropriate evaluation criteria can be set by taking into consideration the technological maturity.

[0110] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user emotions. For example, the evaluation unit can provide a detailed evaluation method when the user is relaxed. Furthermore, the evaluation unit can provide a concise evaluation method when the user is in a hurry. Furthermore, the evaluation unit can provide a visually appealing evaluation method when the user is excited. This allows for more appropriate evaluation by adjusting the evaluation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0111] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the image data. For example, the evaluation unit can perform a detailed evaluation on image data of an important game scene. The evaluation unit can also perform a detailed evaluation on image data of an important work of art. Furthermore, the evaluation unit can also perform a detailed evaluation on image data of an important workshop activity scene. This allows for efficient evaluation by adjusting the level of detail of the evaluation based on the importance.

[0112] The evaluation unit can apply different evaluation algorithms depending on the category of the image data. For example, the evaluation unit can apply a movement evaluation algorithm to image data of a sports game. The evaluation unit can also apply a color evaluation algorithm to image data of an artwork. Furthermore, the evaluation unit can also apply a behavior evaluation algorithm to image data of a workshop activity scene. In this way, the application of an evaluation algorithm according to the category improves the accuracy of the evaluation.

[0113] The evaluation unit can improve the accuracy of the evaluation by referring to the user's past evaluation results. For example, the evaluation unit can evaluate similar patterns based on the user's past evaluation results. The evaluation unit can also preferentially adopt a specific evaluation method based on the user's past evaluation results. Furthermore, the evaluation unit can adjust the evaluation algorithm by referring to the user's past evaluation results. In this way, the accuracy of the evaluation can be improved by referring to the past evaluation results.

[0114] The evaluation unit can estimate the user's emotions and adjust the length of the evaluation based on the estimated user emotions. For example, if the user is in a hurry, the evaluation unit can provide a short and to-the-point evaluation. Also, if the user is relaxed, the evaluation unit can provide a detailed evaluation. Furthermore, if the user is excited, the evaluation unit can provide a visually appealing evaluation. This allows for more appropriate evaluation by adjusting the length of the evaluation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0115] The evaluation unit can determine the priority of evaluation based on the time of collection of image data. For example, the evaluation unit can prioritize evaluation of image data collected most recently. The evaluation unit can also prioritize evaluation of image data of important events. Furthermore, the evaluation unit can also prioritize evaluation of image data collected at a time specified by the user. This allows for efficient evaluation by determining the priority based on the time of collection.

[0116] The evaluation unit can adjust the order of evaluation based on the relevance of the image data. For example, the evaluation unit can prioritize evaluation of highly relevant image data. The evaluation unit can also postpone evaluation of less relevant image data. Furthermore, the evaluation unit can prioritize evaluation of highly relevant image data specified by the user. This allows for efficient evaluation by adjusting the order of evaluation based on relevance.

[0117] The evaluation unit can adjust the use of technical terminology in the evaluation depending on the user's level of expertise. For example, if the user is an expert, the evaluation unit can provide an evaluation result that uses a lot of technical terminology. Also, if the user is a beginner, the evaluation unit can provide an evaluation result that avoids technical terminology. Furthermore, the evaluation unit can use appropriate technical terminology depending on the user's level of expertise. In this way, by adjusting the use of technical terminology depending on the level of expertise, it is possible to provide an evaluation result that is suitable for the user.

[0118] The calculation unit can estimate the user's emotions and adjust the calculation method of the added points based on the estimated user's emotions. For example, if the user is relaxed, the calculation unit can calculate detailed added points. Furthermore, if the user is in a hurry, the calculation unit can calculate concise added points. Furthermore, if the user is excited, the calculation unit can calculate visually appealing added points. Thus, by adjusting the calculation method based on the user's emotions, more appropriate added points can be calculated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0119] The calculation unit can adjust the level of detail of the added points based on the importance of the evaluation result. For example, the calculation unit can calculate detailed added points for the evaluation result of an important match scene. The calculation unit can also calculate detailed added points for the evaluation result of an important artwork. Furthermore, the calculation unit can calculate detailed added points for the evaluation result of an important workshop activity scene. In this way, by adjusting the level of detail based on the importance, efficient calculation of added points is possible.

[0120] The calculation unit can apply different scoring point calculation algorithms depending on the category of the evaluation result. For example, the calculation unit can calculate scoring points by applying a motion evaluation algorithm to the evaluation result of a sports game. The calculation unit can also calculate scoring points by applying a color evaluation algorithm to the evaluation result of a work of art. Furthermore, the calculation unit can calculate scoring points by applying a behavior evaluation algorithm to the evaluation result of a workshop activity scene. This improves the accuracy of scoring points by applying a calculation algorithm according to the category.

[0121] The calculation unit can improve the accuracy of the calculation by referring to the user's past calculation results of the added points. For example, the calculation unit can calculate a similar pattern based on the user's past calculation results of the added points. The calculation unit can also preferentially adopt a specific calculation method based on the user's past calculation results of the added points. Furthermore, the calculation unit can adjust the calculation algorithm by referring to the user's past calculation results of the added points. In this way, the accuracy of the added points can be improved by referring to the past calculation results.

[0122] The calculation unit can estimate the user's emotions and determine the priority of added points based on the estimated user's emotions. For example, when the user is excited, the calculation unit can prioritize calculating important added points. Furthermore, when the user is relaxed, the calculation unit can calculate overall added points evenly. Furthermore, when the user is focused, the calculation unit can prioritize calculating added points that focus on details. In this way, by determining the priority based on the user's emotions, important added points can be calculated preferentially. Emotion estimation is realized using an emotion estimation function using, 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.

[0123] The calculation unit can determine the priority of the added points based on the collection time of the evaluation results. The calculation unit can calculate the added points based on the most recently collected evaluation results, for example. The calculation unit can also calculate the added points based on the evaluation results of important events. Furthermore, the calculation unit can calculate the added points based on the evaluation results at a time specified by the user. In this way, by determining the priority based on the collection time, it is possible to efficiently calculate the added points.

[0124] The calculation unit can adjust the order of added points based on the relevance of the evaluation results. The calculation unit can, for example, calculate added points based on highly relevant evaluation results. The calculation unit can also calculate added points by putting less relevant evaluation results on the back burner. Furthermore, the calculation unit can calculate added points based on highly relevant evaluation results specified by the user. This allows for efficient calculation of added points by adjusting the order based on relevance.

[0125] The calculation unit can adjust the level of detail of the added points according to the user's level of expertise. For example, if the user is an expert, the calculation unit can calculate detailed added points. Also, if the user is a beginner, the calculation unit can calculate simple added points. Furthermore, the calculation unit can calculate appropriate added points according to the user's level of expertise. In this way, by adjusting the level of detail according to the level of expertise, it is possible to calculate added points appropriate for the user.

[0126] The creation unit can estimate the user's emotions and adjust the creation method of the advice sheet based on the estimated user's emotions. For example, the creation unit can create a detailed advice sheet when the user is relaxed. Furthermore, the creation unit can create a concise advice sheet when the user is in a hurry. Furthermore, the creation unit can create a visually appealing advice sheet when the user is excited. In this way, by adjusting the creation method based on the user's emotions, a more appropriate advice sheet can be created. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0127] The creation unit can adjust the level of detail of the advice sheet based on the importance of the added points. For example, the creation unit can provide detailed advice for important added points. The creation unit can also provide concise advice for less important added points. Furthermore, the creation unit can adjust the level of detail of the advice according to the importance of the added points. This allows for efficient creation of advice sheets by adjusting the level of detail based on the importance.

[0128] The creation unit can apply different advice sheet creation algorithms depending on the category of added points. For example, the creation unit can apply an algorithm that provides technical advice to added points for a sports game. The creation unit can also apply an algorithm that provides creative advice to added points for an artistic work. Furthermore, the creation unit can apply an algorithm that provides practical advice to added points for a workshop activity scene. In this way, the accuracy of the advice sheet is improved by applying a creation algorithm according to the category.

[0129] The creation unit can improve the accuracy of creation by referring to the user's past advice sheet creation results. For example, the creation unit can create a similar pattern based on the user's past advice sheet creation results. The creation unit can also preferentially adopt a specific creation method based on the user's past advice sheet creation results. Furthermore, the creation unit can also adjust the creation algorithm by referring to the user's past advice sheet creation results. In this way, the accuracy of the advice sheet is improved by referring to the past creation results.

[0130] The creation unit can estimate the user's emotions and determine the priority of advice sheets based on the estimated user emotions. For example, if the user is excited, the creation unit can prioritize providing important advice. Furthermore, if the user is relaxed, the creation unit can also provide overall advice evenly. Furthermore, if the user is focused, the creation unit can prioritize providing advice that focuses on details. In this way, by determining the priority based on the user's emotions, important advice can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0131] The creation unit can determine the priority of advice sheets based on the time when the added points were collected. The creation unit can provide advice based on the most recently collected added points, for example. The creation unit can also provide advice based on the added points of important events. Furthermore, the creation unit can also provide advice based on the added points at a time specified by the user. This allows for efficient creation of advice sheets by determining the priority based on the time when the points were collected.

[0132] The creation unit can adjust the order of the advice sheets based on the relevance of the added points. For example, the creation unit can provide advice based on highly relevant added points. The creation unit can also provide advice by putting less relevant added points on hold. Furthermore, the creation unit can also provide advice based on highly relevant added points specified by the user. This allows for efficient creation of advice sheets by adjusting the order based on relevance.

[0133] The creation unit can adjust the level of detail of the advice sheet according to the user's level of expertise. For example, if the user is an expert, the creation unit can provide detailed advice. Also, if the user is a beginner, the creation unit can provide concise advice. Furthermore, the creation unit can provide advice with an appropriate level of detail according to the user's level of expertise. In this way, by adjusting the level of detail according to the level of expertise, an advice sheet suitable for the user can be provided. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, setting unit, evaluation unit, calculation unit, and creation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect image data in real time using the camera 42 of the smart device 14. The collection unit can also collect image data from the Internet via the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the image data using an image recognition algorithm. The setting unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, sets evaluation criteria based on the analysis results. The evaluation unit, implemented, for example, by the control unit 46A of the smart device 14, evaluates each element based on the set evaluation criteria. The calculation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, calculates points based on the evaluation results. The creation unit, implemented, for example, by the control unit 46A of the smart device 14, creates an advice sheet based on the calculated points. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, setting unit, evaluation unit, calculation unit, and creation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect image data in real time using the camera 42 of the smart glasses 214. The collection unit can also collect image data from the Internet via the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the image data using an image recognition algorithm. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets evaluation criteria based on the analysis results. The evaluation unit is realized, for example, by the control unit 46A of the smart glasses 214 and evaluates each element based on the set evaluation criteria. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates points based on the evaluation results. The creation unit is realized, for example, by the control unit 46A of the smart glasses 214 and creates an advice sheet based on the calculated points. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, setting unit, evaluation unit, calculation unit, and creation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect image data in real time using the camera 42 of the headset-type terminal 314. The collection unit can also collect image data from the Internet via the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the image data using an image recognition algorithm. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets evaluation criteria based on the analysis results. The evaluation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and evaluates each element based on the set evaluation criteria. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates additional points based on the evaluation results. The creation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and creates an advice sheet based on the calculated additional points. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, setting unit, evaluation unit, calculation unit, and creation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect image data in real time using the camera 42 of the robot 414. The collection unit can also collect image data from the Internet via the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the image data using an image recognition algorithm. The setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets evaluation criteria based on the analysis results. The evaluation unit is realized, for example, by the control unit 46A of the robot 414 and evaluates each element based on the set evaluation criteria. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates points based on the evaluation results. The creation unit is realized, for example, by the control unit 46A of the robot 414 and creates an advice sheet based on the calculated points.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The rating system can also dynamically adjust the rating criteria by referring to the user's past rating history. For example, it can set rating criteria that emphasize elements that the user previously rated highly. It can also set rating criteria that improve elements that the user previously rated poorly. It can also adjust the weighting of the rating criteria based on the user's past rating history. This promotes user growth and enables more personalized ratings.

[0136] The rating system can also collect real-time user feedback and adjust the rating criteria based on that feedback. For example, if a user expresses dissatisfaction with a particular element during the rating process, the rating criteria for that element can be relaxed. Alternatively, if a user expresses high interest in a particular element, the rating criteria for that element can be tightened. Furthermore, the priority of rating criteria can be dynamically changed based on user feedback. This allows for flexible rating that meets the needs of users.

[0137] The rating system can also combine the opinions of different evaluators to produce an overall rating. For example, multiple evaluators can evaluate the same sports game and combine their evaluation results to calculate a final rating. It can also have different evaluators evaluate a work of art and combine their evaluation results to provide comprehensive advice. Furthermore, the weighting of the evaluation results can be adjusted based on the evaluator's expertise and experience. This allows for a more multifaceted and fair evaluation.

[0138] The evaluation system can further estimate the user's emotions and adjust the feedback method of the evaluation results based on the estimated emotions. For example, if the user is depressed, it can emphasize positive feedback. If the user is confident, it can explain in detail the areas for improvement. Furthermore, if the user is excited, it can provide visually appealing feedback. This enables appropriate feedback according to the user's emotions.

[0139] The evaluation system can also have a function to share the evaluation results with other users. For example, the evaluation results of a sports game can be shared with team members so that everyone can identify areas for improvement. The evaluation results of an artwork can also be shared within the community to receive feedback from other artists. Furthermore, the evaluation results can be shared on social media to gather a wide range of opinions. This allows feedback from a wider range of perspectives to be obtained by sharing the evaluation results.

[0140] The rating system can further estimate the user's emotions and adjust the timing of the rating based on the estimated emotions. For example, if the user is relaxed, a detailed rating can be made. If the user is in a hurry, a brief rating can be made. Furthermore, if the user is excited, a visually appealing rating can be made. This allows for appropriate timing of rating according to the user's emotions.

[0141] The evaluation system can also provide training plans based on the evaluation results. For example, it can suggest training menus to athletes based on areas for improvement in their techniques. It can also provide practice assignments to artists based on areas for improvement in their work. It can also suggest practice methods to workshop participants based on areas for improvement in their presentations. In this way, specific training plans based on the evaluation results can be provided, supporting the growth of users.

[0142] The evaluation system can further estimate the user's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if the user is relaxed, detailed evaluation results can be displayed. If the user is in a hurry, concise evaluation results that focus on the main points can be displayed. Furthermore, if the user is excited, visually appealing evaluation results can be displayed. This makes it possible to display appropriate evaluation results according to the user's emotions.

[0143] The evaluation system can further introduce a reward system based on the evaluation results. For example, if an athlete receives a high evaluation, they can be offered specific training opportunities or resources. If an artist receives a high evaluation, they can be offered the opportunity to participate in an exhibition. Furthermore, if a workshop participant receives a high evaluation, they can be offered priority participation in the next workshop. In this way, a reward system based on the evaluation results can increase user motivation.

[0144] The evaluation system can further estimate the user's emotions and adjust the notification method of the evaluation result based on the estimated emotions. For example, if the user is relaxed, a detailed notification can be sent. If the user is in a hurry, a concise notification can be sent. Furthermore, if the user is excited, a visually appealing notification can be sent. This makes it possible to notify the user of the evaluation result appropriately according to the user's emotions.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The collection unit collects image data. For example, the collection unit can collect image data of sports games or artworks. The collection unit can collect image data in real time using a camera. It can also collect image data from the Internet. It can also collect image data provided by users. Step 2: The analysis unit analyzes the image data collected by the collection unit. The analysis unit can analyze the image data using image recognition algorithms, feature extraction methods, and AI. Step 3: The setting unit sets evaluation criteria based on the analysis results obtained by the analysis unit. The setting unit can set evaluation items and scoring methods, and can also set evaluation criteria using AI. Step 4: The evaluation unit evaluates each element based on the evaluation criteria set by the setting unit. The evaluation unit can evaluate technical elements and design elements, and can also evaluate each element using AI. Step 5: The calculation unit calculates the points based on the evaluation results obtained by the evaluation unit. The calculation unit can calculate the points using a point allocation method based on the evaluation results, or can calculate the points using AI. Step 6: The creation unit creates an advice sheet based on the added points calculated by the calculation unit. The creation unit can set the feedback format and level of detail, and can also create the advice sheet using AI.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Explanation of symbols]

[0219] 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 collection unit that collects image data; an analysis unit that analyzes the image data collected by the collection unit; a setting unit that sets evaluation criteria based on the analysis results obtained by the analysis unit; an evaluation unit that evaluates each element based on the evaluation criteria set by the setting unit; a calculation unit that calculates points to be added based on the evaluation result obtained by the evaluation unit; a creation unit that creates an advice sheet based on the added points calculated by the calculation unit; Equipped with A system characterized by:

2. The collecting unit Collecting images of sports games or artwork 2. The system of claim 1.

3. The analysis unit Analyze the collected image data and set evaluation criteria for each element 2. The system of claim 1.

4. The evaluation unit Evaluate each element based on the set evaluation criteria 2. The system of claim 1.

5. The calculation unit Calculate additional points based on the evaluation results 2. The system of claim 1.

6. The creation unit Create an advice sheet based on the points added 2. The system of claim 1.

7. The collecting unit The user's emotions are estimated, and the timing of collecting image data is adjusted based on the estimated user's emotions.

2. The system of claim 1.

8. The collecting unit Optimizing image data collection based on specific events or situations 2. The system of claim 1.

9. The collecting unit Collecting image data from different perspectives or angles improves the accuracy of your analysis 2. The system of claim 1.

10. The collecting unit Select the optimal collection method by referring to the user's past collection history 2. The system of claim 1.

Images