System

Abstract

To provide a system that provides advice appropriate for a user's goals.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an image or data of a user. The analysis unit analyzes the data received by the reception unit. The generation unit generates advice based on analysis results obtained by the analysis unit. The provision unit provides the advice generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the disadvantage that it is difficult for users to obtain appropriate advice for achieving their goals.

[0005] The system according to the embodiment aims to provide appropriate advice for a user's goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an image or data of a user. The analysis unit analyzes the data received by the reception unit. The generation unit generates advice based on the analysis result obtained by the analysis unit. The provision unit provides the advice generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can provide appropriate advice for the user's goals. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The advice providing system according to the embodiment of the present invention is a system in which the user inputs an image or data of the person the user wants to be and the user's current image or data, and the generation AI provides advice such as makeup, hairstyle, and body shape improvement that suits the user. In this advice providing system, the user inputs an image or data of the person the user wants to be and the user's current image or data, and the generation AI analyzes these data and provides the user with advice on makeup, hairstyle, and body shape improvement that is optimal for the user. This advice becomes more accurate by improving the accuracy of the generation AI, and the user's "desire to be" is realized in the shortest and most optimal way. For example, the user inputs a face photo and body type data of the person the user wants to be, and a face photo and body type data of the user. The generation AI analyzes these data and generates makeup, hairstyle, and body shape improvement advice that is optimal for the user. The generated advice is provided to the user. For example, the generation AI proposes a makeup method, a hairstyle style, and an exercise plan for body shape improvement, etc., and the like. This allows the user to know a specific method for realizing his or her "desire to be". This allows the advice providing system to analyze the user's image and data and provide optimal advice.

[0029] The advice providing system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an image or data of a user. The image or data of the user includes, for example, a photo, a video, text data, and the like, but is not limited to such examples. The reception unit receives, for example, image data or text data input by the user. The analysis unit analyzes the data received by the reception unit. The analysis unit analyzes the image or data of the user using, for example, an image analysis algorithm or a data mining technique, and compares it with the image or data of the person the user wants to become. The generation unit generates advice based on the analysis result obtained by the analysis unit. The generation unit generates, for example, advice on makeup, hairstyle, and body shape improvement that is optimal for the user. The generation unit generates, for example, advice on makeup, hairstyle, and body shape improvement that is optimal for the user using a generation AI based on the analysis result. The provision unit provides the advice generated by the generation unit. The provision unit provides, for example, the generated advice to the user. As a result, the advice providing system according to the embodiment can analyze the image or data of the user and provide optimal advice.

[0030] The reception unit receives images and data of the user. Examples of the images and data of the user include, but are not limited to, photos, videos, and text data. The reception unit receives, for example, image data and text data input by the user. Specifically, the user can upload images and data through a dedicated application or website using a smartphone or a personal computer. The reception unit has a function of safely receiving these data and storing them in a database. Furthermore, the reception unit checks the format and quality of the data and performs preprocessing so that the analysis unit can process the data efficiently. For example, the reception unit adjusts the resolution of the image data and standardizes the format of the text data. Furthermore, the reception unit can encrypt and anonymize the data to protect the privacy of the user. This allows the user to provide his or her data with peace of mind, improving the reliability of the entire system.

[0031] The analysis unit analyzes the data received by the reception unit. The analysis unit, for example, uses an image analysis algorithm or data mining technology to analyze the user's image and data, and compares them with the image and data of the person the user wants to be. Specifically, the image analysis algorithm is used to analyze the user's facial features, hairstyle, body type, etc. in detail. For example, face recognition technology is used to identify the position and shape of the eyes, nose, and mouth, and the outline and color of the hairstyle are analyzed. In addition, data mining technology is used to extract preferences and goals from the user's text data and compare them with the data of the person the user wants to be. Furthermore, the analysis unit can utilize past data and statistical information to analyze the user's features and trends, and provide more accurate analysis results. For example, the analysis unit refers to data of users who have similar goals in the past, and generates advice based on success and failure examples. In addition, the analysis unit uses AI to detect patterns and trends in the data, and provides basic information for providing the user with optimal advice. As a result, the analysis unit can analyze the user's data from multiple angles and provide highly accurate analysis results according to individual needs.

[0032] The generation unit generates advice based on the analysis results obtained by the analysis unit. The generation unit generates, for example, advice on makeup, hairstyle, and body shape improvement that is optimal for the user. The generation unit uses the generation AI to generate appropriate makeup, hairstyle, and body shape improvement advice for the user based on the analysis results. Specifically, the generation AI generates optimal makeup colors and styles, hairstyle change suggestions, and exercise and diet advice for body shape improvement based on the user's facial features, hairstyle, and body type. For example, the generation AI suggests optimal foundation and eyeshadow colors according to the shape of the face and skin color, and provides specific advice on cuts and colors for hairstyles based on the facial contours and hair type. In addition, for body shape improvement, the generation AI suggests effective exercises and meal plans based on the user's current body type and goals. The generation AI can learn from past data and success cases and provide the most effective advice for the user. Furthermore, the generation unit can receive user feedback and continuously improve the accuracy and effectiveness of the advice. As a result, the generation unit can provide high-quality advice that is individually customized for the user and support the user in achieving their goals.

[0033] The providing unit provides the advice generated by the generating unit. The providing unit provides the generated advice to the user, for example. Specifically, the providing unit displays the generated advice through an application or website used by the user. The user can log in to the application or website and check the advice on his / her own dashboard. The providing unit can use graphics or videos to display the advice in a visually easy-to-understand manner. For example, for makeup advice, a video showing specific steps and before-and-after images can be displayed to serve as a reference for the user when actually trying it out. For hairstyle advice, a 3D model is used to simulate different hairstyles, helping the user to select a style that suits him / her. Furthermore, for advice on improving one's body shape, exercise videos and details of meal plans are provided to support the user so that the advice can be easily put into practice in daily life. The providing unit can collect user feedback and evaluate the effectiveness and satisfaction of the advice. As a result, the providing unit can provide effective and practical advice to the user and support the user in achieving his / her goals.

[0034] The reception unit can receive image data and text data input by the user. The image data and text data include, but are not limited to, formats such as JPEG, PNG, TXT, and DOC. The reception unit receives, for example, image data and text data input by the user. For example, when the user inputs image data in JPEG format, the reception unit can receive the data. Also, when the user inputs text data in TXT format, the reception unit can receive the data. This makes it possible to receive image data and text data input by the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input image data and text data input by the user to AI and have the AI ​​execute the reception of the data.

[0035] The analysis unit can analyze the image or data of the user and compare it with the image or data of the target person. The image or data of the target person includes, but is not limited to, for example, a photo of a celebrity, ideal body type data, etc. The analysis unit, for example, analyzes the image or data of the user and compares it with the image or data of the person the user wants to become. For example, the analysis unit analyzes the face photo of the user and compares it with the face photo of the celebrity. The analysis unit can also analyze the body type data of the user and compare it with the ideal body type data. This makes it possible to analyze the image or data of the user and compare it with the image or data of the person the user wants to become. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the image data of the user to AI and have the AI ​​execute the analysis of the image.

[0036] The generation unit can generate appropriate makeup, hairstyle, and body shape improvement advice for the user based on the analysis result. Appropriate makeup, hairstyle, and body shape improvement advice include, but are not limited to, for example, how to use cosmetics, hairstyle suggestions, and exercise plans. The generation unit generates, for example, makeup, hairstyle, and body shape improvement advice that is optimal for the user based on the analysis result. For example, the generation unit analyzes the facial features of the user and suggests makeup and hairstyle that suits the person the user wants to become. The generation unit can also analyze the user's body shape data and suggest an exercise plan for body shape improvement. This makes it possible to generate makeup, hairstyle, and body shape improvement advice that is optimal for the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the analysis result to AI and cause the AI ​​to generate advice.

[0037] The providing unit can provide the generated advice to the user. The providing unit, for example, provides the generated advice to the user. The generated advice includes, for example, a makeup method, a hairstyle, an exercise plan for improving a body shape, and the like, but is not limited to such examples. For example, the providing unit provides the generated makeup method to the user. The providing unit can also provide the generated hairstyle to the user. Furthermore, the providing unit can also provide the generated exercise plan for improving a body shape to the user. Thereby, the generated advice can be provided to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated advice to AI and cause the AI ​​to execute the provision of the advice.

[0038] The generation unit can perform learning to improve the accuracy of the generation AI. Learning to improve the accuracy of the generation AI includes, for example, supervised learning, reinforcement learning, a training data set, and the like, but is not limited to such examples. The generation unit performs learning to improve the accuracy of the generation AI, for example. For example, the generation unit improves the accuracy of the generation AI using supervised learning. In addition, the generation unit can also improve the accuracy of the generation AI using reinforcement learning. Furthermore, the generation unit can also improve the accuracy of the generation AI using a training data set. This makes it possible to perform learning to improve the accuracy of the generation AI. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input learning data of the generation AI to the AI ​​and cause the AI ​​to execute learning.

[0039] The providing unit can collect user feedback and use it to improve the accuracy of the generation AI. User feedback includes, for example, questionnaires, reviews, usage history, etc., but is not limited to such examples. The providing unit, for example, collects user feedback and uses it to improve the accuracy of the generation AI. For example, the providing unit collects user feedback through questionnaires. The providing unit can also collect user feedback through reviews. Furthermore, the providing unit can also collect user feedback through usage history. This makes it possible to collect user feedback and use it to improve the accuracy of the generation AI. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input user feedback data to AI and cause the AI ​​to collect feedback.

[0040] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, analyzes the user's past input history and selects the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also automatically display input candidates by referring to data that the user has input in the past. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period from the user's past input history. This makes it possible to select the optimal input method by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to AI and cause the AI ​​to select the input method.

[0041] The reception unit can perform filtering based on the user's current living situation and field of interest when inputting an image or data. The reception unit performs filtering based on the user's current living situation and field of interest when inputting an image or data, for example. For example, when a user inputs a current living situation, an input option suitable for that situation is provided. The reception unit can also preferentially display related input options based on the user's field of interest. Furthermore, the reception unit can also filter input data based on the user's living situation and field of interest and suggest appropriate candidates. As a result, appropriate input is possible by filtering based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and field of interest to AI and have the AI ​​perform filtering.

[0042] The reception unit can preferentially input highly relevant data in consideration of the geographical location information of the user when inputting an image or data. For example, the reception unit preferentially inputs highly relevant data in consideration of the geographical location information of the user when inputting an image or data. For example, if the user is in a specific area, data related to the area is preferentially input. The reception unit can also provide related input options based on the geographical location information of the user. Furthermore, the reception unit can also suggest optimal input data based on the current location of the user. This allows highly relevant data to be preferentially input by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to execute preferential input of data.

[0043] The reception unit can analyze the user's social media activity and input related data when the image or data is input. The reception unit can analyze the user's social media activity and input related data when the image or data is input, for example. For example, topics of interest are extracted from the user's social media activity and related data is input. The reception unit can also suggest input data based on information shared by the user on social media. Furthermore, the reception unit can also analyze the user's social media activity and provide optimal input data. As a result, related data can be input by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the user's social media activity data to AI and cause the AI ​​to execute data input.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, a detailed analysis is performed for data with high importance. Also, a concise analysis can be performed for data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the data to an AI and cause the AI ​​to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of data during analysis. For example, a face recognition algorithm can be applied to data related to makeup. A hairstyle analysis algorithm can also be applied to data related to hairstyle. Furthermore, a body shape analysis algorithm can be applied to data related to body shape improvement. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to AI and cause AI to apply the analysis algorithm.

[0046] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the latest data is analyzed preferentially. Data submitted earlier can also be analyzed later. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission. This enables more efficient analysis by determining the analysis priority based on the time of data submission. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to AI and have AI execute the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of data during analysis. For example, highly relevant data is preferentially analyzed. Also, less relevant data can be analyzed later. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of data. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI and cause AI to adjust the order of analysis.

[0048] The generation unit can adjust the level of detail of the generation based on the importance of the advice at the time of generation. The generation unit, for example, adjusts the level of detail of the generation based on the importance of the advice at the time of generation. For example, a detailed explanation is provided for advice with high importance. Also, a concise explanation can be provided for advice with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the generation according to the importance of the advice. As a result, more appropriate advice can be provided by adjusting the level of detail of the generation based on the importance of the advice. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the importance of the advice to AI and cause the AI ​​to adjust the level of detail of the generation.

[0049] The generation unit can apply different generation algorithms depending on the category of advice at the time of generation. The generation unit applies different generation algorithms depending on the category of advice at the time of generation, for example. For example, a face recognition algorithm can be applied to advice on makeup. A hairstyle analysis algorithm can also be applied to advice on hairstyle. Furthermore, a body type analysis algorithm can be applied to advice on body shape improvement. In this way, by applying different generation algorithms depending on the category of advice, more appropriate advice can be provided. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the category of advice to AI and cause the AI ​​to apply the generation algorithm.

[0050] The generation unit can determine the priority of generation based on the submission time of the advice at the time of generation. The generation unit, for example, determines the priority of generation based on the submission time of the advice at the time of generation. For example, the latest advice is generated preferentially. Also, advice submitted earlier can be generated later. Furthermore, the generation unit can dynamically adjust the priority of generation based on the submission time. This enables more efficient advice generation by determining the priority of generation based on the submission time of the advice. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time of the advice to AI and cause the AI ​​to determine the priority of generation.

[0051] The generation unit can adjust the order of generation based on the relevance of the advice at the time of generation. The generation unit, for example, adjusts the order of generation based on the relevance of the advice at the time of generation. For example, highly relevant advice is preferentially generated. Also, less relevant advice can be generated later. Furthermore, the generation unit can dynamically adjust the order of generation based on the relevance of the advice. In this way, by adjusting the order of generation based on the relevance of the advice, more appropriate advice can be provided. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance of the advice to AI and cause the AI ​​to adjust the order of generation.

[0052] The providing unit can select the optimal providing method by referring to the user's past feedback at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past feedback at the time of providing. For example, the providing unit preferentially selects a providing method that the user has preferred in the past. The providing unit can also dynamically adjust the providing method based on the user's past feedback. Furthermore, the providing unit can also suggest the optimal providing method from the user's past feedback. In this way, the optimal providing method can be selected by referring to the user's past feedback. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into AI and cause the AI ​​to select the providing method.

[0053] The providing unit can adjust the method of providing advice based on the user's current living situation and field of interest at the time of providing. For example, the providing unit adjusts the method of providing advice based on the user's current living situation and field of interest at the time of providing. For example, when the user inputs the current living situation, advice suitable for that situation is provided. The providing unit can also provide related advice preferentially based on the user's field of interest. Furthermore, the providing unit can dynamically adjust the method of providing advice based on the user's living situation and field of interest. As a result, more appropriate advice can be provided by adjusting the method of providing advice based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's living situation and field of interest into AI and cause AI to adjust the method of providing advice.

[0054] The providing unit can select the optimal providing method in consideration of the device information of the user at the time of providing. For example, the providing unit selects the optimal providing method in consideration of the device information of the user at the time of providing. For example, when the user is using a smartphone, advice according to the screen size is provided. In addition, when the user is using a tablet, advice optimized for a large screen can be provided. Furthermore, when the user is using a smartwatch, concise and highly visible advice can be provided. In this way, the optimal providing method can be selected by considering the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to AI and cause the AI ​​to select the providing method.

[0055] The providing unit can provide advice in multiple languages ​​according to the language setting of the user at the time of providing. The providing unit, for example, provides advice in multiple languages ​​according to the language setting of the user at the time of providing. For example, the language of the advice is automatically set based on the language setting of the user's device. In addition, when the user uses multiple languages, a language switching function can be provided. Furthermore, when the user selects a specific language, advice can be provided in that language. As a result, more appropriate advice can be provided by providing advice in multiple languages ​​according to the language setting of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting to AI and cause the AI ​​to provide advice in multiple languages.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can analyze the user's past behavior history and collect data for providing optimal advice. For example, it records what kind of makeup or hairstyle the user has tried in the past and what exercise plan the user has performed, and generates new advice based on this information. The reception unit can also analyze what advice the user has accepted in the past and what advice he or she has ignored, and provide advice tailored to the user's preferences. Furthermore, the reception unit can evaluate the effectiveness of advice based on the user's past behavior history and provide effective advice with priority. This makes it possible to provide more personalized advice by utilizing the user's past behavior history.

[0058] The analysis unit can monitor the user's health condition and adjust advice based on the health condition. For example, the analysis unit can analyze the user's heart rate and sleep patterns, and if the user's health condition is not good, can suggest a reasonable exercise plan. The analysis unit can also analyze the user's food records and suggest a nutritionally balanced meal plan. Furthermore, the analysis unit can analyze the user's stress level and provide advice on relaxation methods and stress reduction. This allows more appropriate advice to be provided based on the user's health condition.

[0059] The generating unit can generate advice that matches the lifestyle of the user. For example, if the user is a busy businessman, a short and effective exercise plan can be proposed. Also, if the user is a housewife who values ​​time at home, advice on makeup and hairstyles that can be easily done at home can be provided. Furthermore, if the user likes outdoor activities, the generating unit can provide advice on makeup, hairstyles, and body shape improvement that are suitable for the outdoors. This makes it possible to provide more practical advice that matches the lifestyle of the user.

[0060] The providing unit can collect user feedback in real time and instantly adjust the content of the advice. For example, if the user is not satisfied with the advice provided to him / her, new advice is generated based on the feedback. The providing unit can also monitor the results of the user's execution of the advice in real time and modify the advice as necessary. Furthermore, the providing unit can improve the method of providing advice based on the user's feedback and provide more effective advice. This makes it possible to provide more appropriate advice by utilizing the user's feedback.

[0061] The providing unit can analyze the usage status of the user's device and provide advice at optimal timing. For example, the advice is provided during a time period when the user frequently uses the smartphone. The providing unit can also provide advice by avoiding a time period when the user does not use the device. Furthermore, the providing unit can also provide related advice when the user is using a specific application. This makes it possible to provide advice at a more effective timing based on the usage status of the user's device.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The reception unit receives an image or data of a user. The image or data of a user includes, for example, a photograph, a video, text data, etc. The reception unit receives image data or text data input by the user. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis unit uses an image analysis algorithm and data mining technology to analyze the image and data of the user and compares it with the image and data of the person the user wants to become. Step 3: The generator generates advice based on the analysis results obtained by the analyzer. The generator uses a generation AI to generate advice on makeup, hairstyle, and body shape improvement that is optimal for the user. Step 4: The providing unit provides the advice generated by the generating unit. The providing unit provides the generated advice to the user.

[0064] (Example 2) The advice providing system according to the embodiment of the present invention is a system in which the user inputs an image or data of the person the user wants to be and the user's current image or data, and the generation AI provides advice such as makeup, hairstyle, and body shape improvement that suits the user. In this advice providing system, the user inputs an image or data of the person the user wants to be and the user's current image or data, and the generation AI analyzes these data and provides the user with advice on makeup, hairstyle, and body shape improvement that is optimal for the user. This advice becomes more accurate by improving the accuracy of the generation AI, and the user's "desire to be" is realized in the shortest and most optimal way. For example, the user inputs a face photo and body type data of the person the user wants to be, and a face photo and body type data of the user. The generation AI analyzes these data and generates makeup, hairstyle, and body shape improvement advice that is optimal for the user. The generated advice is provided to the user. For example, the generation AI proposes a makeup method, a hairstyle style, and an exercise plan for body shape improvement, etc., and the like. This allows the user to know a specific method for realizing his or her "desire to be". This allows the advice providing system to analyze the user's image and data and provide optimal advice.

[0065] The advice providing system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an image or data of a user. The image or data of the user includes, for example, a photo, a video, text data, and the like, but is not limited to such examples. The reception unit receives, for example, image data or text data input by the user. The analysis unit analyzes the data received by the reception unit. The analysis unit analyzes the image or data of the user using, for example, an image analysis algorithm or a data mining technique, and compares it with the image or data of the person the user wants to become. The generation unit generates advice based on the analysis result obtained by the analysis unit. The generation unit generates, for example, advice on makeup, hairstyle, and body shape improvement that is optimal for the user. The generation unit generates, for example, advice on makeup, hairstyle, and body shape improvement that is optimal for the user using a generation AI based on the analysis result. The provision unit provides the advice generated by the generation unit. The provision unit provides, for example, the generated advice to the user. As a result, the advice providing system according to the embodiment can analyze the image or data of the user and provide optimal advice.

[0066] The reception unit receives images and data of the user. Examples of the images and data of the user include, but are not limited to, photos, videos, and text data. The reception unit receives, for example, image data and text data input by the user. Specifically, the user can upload images and data through a dedicated application or website using a smartphone or a personal computer. The reception unit has a function of safely receiving these data and storing them in a database. Furthermore, the reception unit checks the format and quality of the data and performs preprocessing so that the analysis unit can process the data efficiently. For example, the reception unit adjusts the resolution of the image data and standardizes the format of the text data. Furthermore, the reception unit can encrypt and anonymize the data to protect the privacy of the user. This allows the user to provide his or her data with peace of mind, improving the reliability of the entire system.

[0067] The analysis unit analyzes the data received by the reception unit. The analysis unit, for example, uses an image analysis algorithm or data mining technology to analyze the user's image and data, and compares them with the image and data of the person the user wants to be. Specifically, the image analysis algorithm is used to analyze the user's facial features, hairstyle, body type, etc. in detail. For example, face recognition technology is used to identify the position and shape of the eyes, nose, and mouth, and the outline and color of the hairstyle are analyzed. In addition, data mining technology is used to extract preferences and goals from the user's text data and compare them with the data of the person the user wants to be. Furthermore, the analysis unit can utilize past data and statistical information to analyze the user's features and trends, and provide more accurate analysis results. For example, the analysis unit refers to data of users who have similar goals in the past, and generates advice based on success and failure examples. In addition, the analysis unit uses AI to detect patterns and trends in the data, and provides basic information for providing the user with optimal advice. As a result, the analysis unit can analyze the user's data from multiple angles and provide highly accurate analysis results according to individual needs.

[0068] The generation unit generates advice based on the analysis results obtained by the analysis unit. The generation unit generates, for example, advice on makeup, hairstyle, and body shape improvement that is optimal for the user. The generation unit uses the generation AI to generate appropriate makeup, hairstyle, and body shape improvement advice for the user based on the analysis results. Specifically, the generation AI generates optimal makeup colors and styles, hairstyle change suggestions, and exercise and diet advice for body shape improvement based on the user's facial features, hairstyle, and body type. For example, the generation AI suggests optimal foundation and eyeshadow colors according to the shape of the face and skin color, and provides specific advice on cuts and colors for hairstyles based on the facial contours and hair type. In addition, for body shape improvement, the generation AI suggests effective exercises and meal plans based on the user's current body type and goals. The generation AI can learn from past data and success cases and provide the most effective advice for the user. Furthermore, the generation unit can receive user feedback and continuously improve the accuracy and effectiveness of the advice. As a result, the generation unit can provide high-quality advice that is individually customized for the user and support the user in achieving their goals.

[0069] The providing unit provides the advice generated by the generating unit. The providing unit provides the generated advice to the user, for example. Specifically, the providing unit displays the generated advice through an application or website used by the user. The user can log in to the application or website and check the advice on his / her own dashboard. The providing unit can use graphics or videos to display the advice in a visually easy-to-understand manner. For example, for makeup advice, a video showing specific steps and before-and-after images can be displayed to serve as a reference for the user when actually trying it out. For hairstyle advice, a 3D model is used to simulate different hairstyles, helping the user to select a style that suits him / her. Furthermore, for advice on improving one's body shape, exercise videos and details of meal plans are provided to support the user so that the advice can be easily put into practice in daily life. The providing unit can collect user feedback and evaluate the effectiveness and satisfaction of the advice. As a result, the providing unit can provide effective and practical advice to the user and support the user in achieving his / her goals.

[0070] The reception unit can receive image data and text data input by the user. The image data and text data include, but are not limited to, formats such as JPEG, PNG, TXT, and DOC. The reception unit receives, for example, image data and text data input by the user. For example, when the user inputs image data in JPEG format, the reception unit can receive the data. Also, when the user inputs text data in TXT format, the reception unit can receive the data. This makes it possible to receive image data and text data input by the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input image data and text data input by the user to AI and have the AI ​​execute the reception of the data.

[0071] The analysis unit can analyze the image or data of the user and compare it with the image or data of the target person. The image or data of the target person includes, but is not limited to, for example, a photo of a celebrity, ideal body type data, etc. The analysis unit, for example, analyzes the image or data of the user and compares it with the image or data of the person the user wants to become. For example, the analysis unit analyzes the face photo of the user and compares it with the face photo of the celebrity. The analysis unit can also analyze the body type data of the user and compare it with the ideal body type data. This makes it possible to analyze the image or data of the user and compare it with the image or data of the person the user wants to become. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the image data of the user to AI and have the AI ​​execute the analysis of the image.

[0072] The generation unit can generate appropriate makeup, hairstyle, and body shape improvement advice for the user based on the analysis result. Appropriate makeup, hairstyle, and body shape improvement advice include, but are not limited to, for example, how to use cosmetics, hairstyle suggestions, and exercise plans. The generation unit generates, for example, makeup, hairstyle, and body shape improvement advice that is optimal for the user based on the analysis result. For example, the generation unit analyzes the facial features of the user and suggests makeup and hairstyle that suits the person the user wants to become. The generation unit can also analyze the user's body shape data and suggest an exercise plan for body shape improvement. This makes it possible to generate makeup, hairstyle, and body shape improvement advice that is optimal for the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the analysis result to AI and cause the AI ​​to generate advice.

[0073] The providing unit can provide the generated advice to the user. The providing unit, for example, provides the generated advice to the user. The generated advice includes, for example, a makeup method, a hairstyle, an exercise plan for improving a body shape, and the like, but is not limited to such examples. For example, the providing unit provides the generated makeup method to the user. The providing unit can also provide the generated hairstyle to the user. Furthermore, the providing unit can also provide the generated exercise plan for improving a body shape to the user. Thereby, the generated advice can be provided to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated advice to AI and cause the AI ​​to execute the provision of the advice.

[0074] The generation unit can perform learning to improve the accuracy of the generation AI. Learning to improve the accuracy of the generation AI includes, for example, supervised learning, reinforcement learning, a training data set, and the like, but is not limited to such examples. The generation unit performs learning to improve the accuracy of the generation AI, for example. For example, the generation unit improves the accuracy of the generation AI using supervised learning. In addition, the generation unit can also improve the accuracy of the generation AI using reinforcement learning. Furthermore, the generation unit can also improve the accuracy of the generation AI using a training data set. This makes it possible to perform learning to improve the accuracy of the generation AI. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input learning data of the generation AI to the AI ​​and cause the AI ​​to execute learning.

[0075] The providing unit can collect user feedback and use it to improve the accuracy of the generation AI. User feedback includes, for example, questionnaires, reviews, usage history, etc., but is not limited to such examples. The providing unit, for example, collects user feedback and uses it to improve the accuracy of the generation AI. For example, the providing unit collects user feedback through questionnaires. The providing unit can also collect user feedback through reviews. Furthermore, the providing unit can also collect user feedback through usage history. This makes it possible to collect user feedback and use it to improve the accuracy of the generation AI. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input user feedback data to AI and cause the AI ​​to collect feedback.

[0076] The reception unit can estimate the user's emotion and adjust the input timing of the image or data based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the input timing of the image or data based on the estimated user's emotion. For example, when the user is relaxed, the input screen is displayed slowly and a detailed explanation is provided. Also, when the user is in a hurry, the input screen can be displayed quickly and only the minimum necessary information can be requested. Furthermore, when the user is stressed, the input screen can be simplified and the input procedure can be minimized. This allows more appropriate input by adjusting the input timing based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to an AI and cause the AI ​​to execute emotion estimation.

[0077] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, analyzes the user's past input history and selects the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also automatically display input candidates by referring to data that the user has input in the past. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period from the user's past input history. This makes it possible to select the optimal input method by analyzing the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to AI and cause the AI ​​to select the input method.

[0078] The reception unit can perform filtering based on the user's current living situation and field of interest when inputting an image or data. The reception unit performs filtering based on the user's current living situation and field of interest when inputting an image or data, for example. For example, when a user inputs a current living situation, an input option suitable for that situation is provided. The reception unit can also preferentially display related input options based on the user's field of interest. Furthermore, the reception unit can also filter input data based on the user's living situation and field of interest and suggest appropriate candidates. As a result, appropriate input is possible by filtering based on the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and field of interest to AI and have the AI ​​perform filtering.

[0079] The reception unit can estimate the user's emotion and determine the priority of data to be input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of data to be input based on the estimated user's emotion. For example, when the user is relaxed, detailed data input is requested with priority. Also, when the user is in a hurry, the minimum necessary data input can be requested with priority. Furthermore, when the user is stressed, simple data input can be requested with priority. This enables more appropriate data input by determining the priority of data based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to an AI and cause the AI ​​to execute the determination of the priority of the data.

[0080] The reception unit can preferentially input highly relevant data in consideration of the geographical location information of the user when inputting an image or data. For example, the reception unit preferentially inputs highly relevant data in consideration of the geographical location information of the user when inputting an image or data. For example, if the user is in a specific area, data related to the area is preferentially input. The reception unit can also provide related input options based on the geographical location information of the user. Furthermore, the reception unit can also suggest optimal input data based on the current location of the user. This allows highly relevant data to be preferentially input by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and cause the AI ​​to execute preferential input of data.

[0081] The reception unit can analyze the user's social media activity and input related data when the image or data is input. The reception unit can analyze the user's social media activity and input related data when the image or data is input, for example. For example, topics of interest are extracted from the user's social media activity and related data is input. The reception unit can also suggest input data based on information shared by the user on social media. Furthermore, the reception unit can also analyze the user's social media activity and provide optimal input data. As a result, related data can be input by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the user's social media activity data to AI and cause the AI ​​to execute data input.

[0082] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, a detailed analysis result is provided. Also, when the user is in a hurry, a concise analysis result can be provided. Furthermore, when the user is stressed, a visually easy-to-understand analysis result can be provided. As a result, by adjusting the expression method of the analysis based on the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to an AI and cause the AI ​​to adjust the expression method of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, a detailed analysis is performed for data with high importance. Also, a concise analysis can be performed for data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the data to an AI and cause the AI ​​to adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of data during analysis. For example, a face recognition algorithm can be applied to data related to makeup. A hairstyle analysis algorithm can also be applied to data related to hairstyle. Furthermore, a body shape analysis algorithm can be applied to data related to body shape improvement. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to AI and cause AI to apply the analysis algorithm.

[0085] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. For example, when the user is relaxed, a detailed analysis is performed. Also, when the user is in a hurry, a brief analysis can be performed. Furthermore, when the user is stressed, a visually easy-to-understand analysis can be performed. This allows for more appropriate analysis by adjusting the length of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to an AI and cause the AI ​​to adjust the length of the analysis.

[0086] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. For example, the latest data is analyzed preferentially. Data submitted earlier can also be analyzed later. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission. This enables more efficient analysis by determining the analysis priority based on the time of data submission. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to AI and have AI execute the determination of the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of data during analysis. For example, highly relevant data is preferentially analyzed. Also, less relevant data can be analyzed later. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of data. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI and cause AI to adjust the order of analysis.

[0088] The generation unit can estimate the user's emotion and adjust the expression method of the advice to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the advice to be generated based on the estimated user's emotion. For example, when the user is relaxed, detailed advice is provided. Also, when the user is in a hurry, concise advice can be provided. Furthermore, when the user is stressed, visually easy-to-understand advice can be provided. As a result, by adjusting the expression method of the advice based on the user's emotion, more appropriate advice can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the expression method of the advice.

[0089] The generation unit can adjust the level of detail of the generation based on the importance of the advice at the time of generation. The generation unit, for example, adjusts the level of detail of the generation based on the importance of the advice at the time of generation. For example, a detailed explanation is provided for advice with high importance. Also, a concise explanation can be provided for advice with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the generation according to the importance of the advice. As a result, more appropriate advice can be provided by adjusting the level of detail of the generation based on the importance of the advice. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the importance of the advice to AI and cause the AI ​​to adjust the level of detail of the generation.

[0090] The generation unit can apply different generation algorithms depending on the category of advice at the time of generation. The generation unit applies different generation algorithms depending on the category of advice at the time of generation, for example. For example, a face recognition algorithm can be applied to advice on makeup. A hairstyle analysis algorithm can also be applied to advice on hairstyle. Furthermore, a body type analysis algorithm can be applied to advice on body shape improvement. In this way, by applying different generation algorithms depending on the category of advice, more appropriate advice can be provided. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the category of advice to AI and cause the AI ​​to apply the generation algorithm.

[0091] The generation unit can estimate the user's emotion and adjust the length of the advice to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the advice to be generated based on the estimated user's emotion. For example, when the user is relaxed, detailed advice is provided. Also, when the user is in a hurry, concise advice can be provided. Furthermore, when the user is stressed, visually easy-to-understand advice can be provided. In this way, by adjusting the length of the advice based on the user's emotion, more appropriate advice can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the length of the advice.

[0092] The generation unit can determine the priority of generation based on the submission time of the advice at the time of generation. The generation unit, for example, determines the priority of generation based on the submission time of the advice at the time of generation. For example, the latest advice is generated preferentially. Also, advice submitted earlier can be generated later. Furthermore, the generation unit can dynamically adjust the priority of generation based on the submission time. This enables more efficient advice generation by determining the priority of generation based on the submission time of the advice. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time of the advice to AI and cause the AI ​​to determine the priority of generation.

[0093] The generation unit can adjust the order of generation based on the relevance of the advice at the time of generation. The generation unit, for example, adjusts the order of generation based on the relevance of the advice at the time of generation. For example, highly relevant advice is preferentially generated. Also, less relevant advice can be generated later. Furthermore, the generation unit can dynamically adjust the order of generation based on the relevance of the advice. In this way, by adjusting the order of generation based on the relevance of the advice, more appropriate advice can be provided. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance of the advice to AI and cause the AI ​​to adjust the order of generation.

[0094] The providing unit can estimate the user's emotion and adjust the advice providing method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the advice providing method based on the estimated user's emotion. For example, when the user is relaxed, detailed advice is provided. Also, when the user is in a hurry, concise advice can be provided. Furthermore, when the user is stressed, visually easy-to-understand advice can be provided. As a result, more appropriate advice can be provided by adjusting the advice providing method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the advice providing method.

[0095] The providing unit can select the optimal providing method by referring to the user's past feedback at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past feedback at the time of providing. For example, the providing unit preferentially selects a providing method that the user has preferred in the past. The providing unit can also dynamically adjust the providing method based on the user's past feedback. Furthermore, the providing unit can also suggest the optimal providing method from the user's past feedback. In this way, the optimal providing method can be selected by referring to the user's past feedback. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into AI and cause the AI ​​to select the providing method.

[0096] The providing unit can adjust the method of providing advice based on the user's current living situation and field of interest at the time of providing. For example, the providing unit adjusts the method of providing advice based on the user's current living situation and field of interest at the time of providing. For example, when the user inputs the current living situation, advice suitable for that situation is provided. The providing unit can also provide related advice preferentially based on the user's field of interest. Furthermore, the providing unit can dynamically adjust the method of providing advice based on the user's living situation and field of interest. As a result, more appropriate advice can be provided by adjusting the method of providing advice based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's living situation and field of interest into AI and cause AI to adjust the method of providing advice.

[0097] The providing unit can estimate the user's emotion and adjust the order of providing advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the order of providing advice based on the estimated user's emotion. For example, when the user is relaxed, detailed advice can be provided preferentially. Also, when the user is in a hurry, brief advice can be provided preferentially. Furthermore, when the user is stressed, visually easy-to-understand advice can be provided preferentially. In this way, by adjusting the order of providing advice based on the user's emotion, more appropriate advice can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the order of providing advice.

[0098] The providing unit can select the optimal providing method in consideration of the device information of the user at the time of providing. For example, the providing unit selects the optimal providing method in consideration of the device information of the user at the time of providing. For example, when the user is using a smartphone, advice according to the screen size is provided. In addition, when the user is using a tablet, advice optimized for a large screen can be provided. Furthermore, when the user is using a smartwatch, concise and highly visible advice can be provided. In this way, the optimal providing method can be selected by considering the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to AI and cause the AI ​​to select the providing method.

[0099] The providing unit can provide advice in multiple languages ​​according to the language setting of the user at the time of providing. The providing unit, for example, provides advice in multiple languages ​​according to the language setting of the user at the time of providing. For example, the language of the advice is automatically set based on the language setting of the user's device. In addition, when the user uses multiple languages, a language switching function can be provided. Furthermore, when the user selects a specific language, advice can be provided in that language. As a result, more appropriate advice can be provided by providing advice in multiple languages ​​according to the language setting of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's language setting to AI and cause the AI ​​to provide advice in multiple languages.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reception unit can analyze the user's past behavior history and collect data for providing optimal advice. For example, it records what kind of makeup or hairstyle the user has tried in the past and what exercise plan the user has performed, and generates new advice based on this information. The reception unit can also analyze what advice the user has accepted in the past and what advice he or she has ignored, and provide advice tailored to the user's preferences. Furthermore, the reception unit can evaluate the effectiveness of advice based on the user's past behavior history and provide effective advice with priority. This makes it possible to provide more personalized advice by utilizing the user's past behavior history.

[0102] The analysis unit can monitor the user's health condition and adjust advice based on the health condition. For example, the analysis unit can analyze the user's heart rate and sleep patterns, and if the user's health condition is not good, can suggest a reasonable exercise plan. The analysis unit can also analyze the user's food records and suggest a nutritionally balanced meal plan. Furthermore, the analysis unit can analyze the user's stress level and provide advice on relaxation methods and stress reduction. This allows more appropriate advice to be provided based on the user's health condition.

[0103] The generating unit can generate advice that matches the lifestyle of the user. For example, if the user is a busy businessman, a short and effective exercise plan can be proposed. Also, if the user is a housewife who values ​​time at home, advice on makeup and hairstyles that can be easily done at home can be provided. Furthermore, if the user likes outdoor activities, the generating unit can provide advice on makeup, hairstyles, and body shape improvement that are suitable for the outdoors. This makes it possible to provide more practical advice that matches the lifestyle of the user.

[0104] The providing unit can collect user feedback in real time and instantly adjust the content of the advice. For example, if the user is not satisfied with the advice provided to him / her, new advice is generated based on the feedback. The providing unit can also monitor the results of the user's execution of the advice in real time and modify the advice as necessary. Furthermore, the providing unit can improve the method of providing advice based on the user's feedback and provide more effective advice. This makes it possible to provide more appropriate advice by utilizing the user's feedback.

[0105] The providing unit can analyze the usage status of the user's device and provide advice at optimal timing. For example, the advice is provided during a time period when the user frequently uses the smartphone. The providing unit can also provide advice by avoiding a time period when the user does not use the device. Furthermore, the providing unit can also provide related advice when the user is using a specific application. This makes it possible to provide advice at a more effective timing based on the usage status of the user's device.

[0106] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide more accurate advice. If the user is in a hurry, a concise analysis can be performed to provide quick advice. Furthermore, if the user is feeling stressed, the analysis accuracy can be adjusted to avoid placing a burden on the user. In this way, more appropriate advice can be provided by adjusting the analysis accuracy based on the user's emotions.

[0107] The generation unit can estimate the user's emotion and adjust the content of the advice to be generated based on the estimated user's emotion. For example, if the user is relaxed, detailed advice can be provided. Also, if the user is in a hurry, concise advice can be provided. Furthermore, if the user is feeling stressed, advice on relaxation methods and stress reduction can be provided. In this way, by adjusting the content of the advice based on the user's emotion, more appropriate advice can be provided.

[0108] The providing unit can estimate the user's emotion and adjust the format of the advice provided based on the estimated user's emotion. For example, when the user is relaxed, detailed text-format advice can be provided. When the user is in a hurry, concise bullet-point format advice can be provided. Furthermore, when the user is stressed, visually easy-to-understand graphic format advice can be provided. In this way, by adjusting the format of the advice provided based on the user's emotion, more appropriate advice can be provided.

[0109] The providing unit can estimate the user's emotions and adjust the frequency of providing advice based on the estimated user's emotions. For example, when the user is relaxed, advice can be provided frequently. Also, when the user is in a hurry, the frequency of providing advice can be reduced. Furthermore, when the user is feeling stressed, the frequency of providing advice can be adjusted to avoid placing a burden on the user. In this way, by adjusting the frequency of providing advice based on the user's emotions, more appropriate advice can be provided.

[0110] The providing unit can estimate the user's emotions and adjust the order in which advice is provided based on the estimated user's emotions. For example, when the user is relaxed, detailed advice can be provided preferentially. Also, when the user is in a hurry, brief advice can be provided preferentially. Furthermore, when the user is stressed, visually easy-to-understand advice can be provided preferentially. In this way, by adjusting the order in which advice is provided based on the user's emotions, more appropriate advice can be provided.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The reception unit receives an image or data of a user. The image or data of a user includes, for example, a photograph, a video, text data, etc. The reception unit receives image data or text data input by the user. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis unit uses an image analysis algorithm and data mining technology to analyze the image and data of the user and compares it with the image and data of the person the user wants to become. Step 3: The generator generates advice based on the analysis results obtained by the analyzer. The generator uses a generation AI to generate advice on makeup, hairstyle, and body shape improvement that is optimal for the user. Step 4: The providing unit provides the advice generated by the generating unit. The providing unit provides the generated advice to the user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the user's image and data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis result. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated advice to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] 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.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0124] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives an image or data of a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis result. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated advice to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives an image and data of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis result. The provision unit is realized, for example, by the display 343 of the headset type terminal 314 and provides the generated advice to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] 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.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives an image and data of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the received data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis result. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated advice to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0173] 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.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0176] 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.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A reception unit that receives an image or data of a user; an analysis unit that analyzes the data accepted by the acceptance unit; a generation unit that generates advice based on the analysis result obtained by the analysis unit; a providing unit that provides the advice generated by the generating unit. A system characterized by: (Appendix 2) The reception unit is Accepts image and text data entered by the user 2. The system of claim 1, (Appendix 3) The system described in Appendix 1, characterized in that the analysis unit analyzes an image or data of a user and compares it with an image or data of a target person. (Appendix 4) The system described in Appendix 1, characterized in that the generation unit generates appropriate makeup, hairstyle, and body improvement advice for the user based on the analysis results. (Appendix 5) The providing unit is Providing generated advice to the user 2. The system of claim 1, (Appendix 6) The generation unit is Learn to improve the accuracy of generative AI 2. The system of claim 1, (Appendix 7) The providing unit is Collect user feedback to help improve the accuracy of generative AI 2. The system of claim 1, (Appendix 8) The reception unit is Estimate the user's emotions and adjust the timing of image and data input based on the estimated user emotions. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past input history and select the optimal input method 2. The system of claim 1, (Appendix 10) The reception unit is Filter images and data as they are entered based on the user's current life situation and areas of interest 2. The system of claim 1, (Appendix 11) The reception unit is Infer user emotions and prioritize input data based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The reception unit is When entering images or data, the system takes into account the user's geographic location information and prioritizes entering data that is more relevant. 2. The system of claim 1, (Appendix 13) The reception unit is When you enter images or data, it analyzes your social media activity and populates the relevant data. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when data were submitted 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 20) The generation unit is Estimate the user's emotions and adjust the way advice is expressed based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The generation unit is At generation time, adjust the verbosity of the generated advice based on its importance 2. The system of claim 1, (Appendix 22) The generation unit is At generation time, different generation algorithms are applied depending on the category of advice. 2. The system of claim 1, (Appendix 23) The generation unit is The system estimates the user's emotions and adjusts the length of the advice to be generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 24) The generation unit is At generation time, prioritize generation based on advice submission age 2. The system of claim 1, (Appendix 25) The generation unit is At generation time, adjust the order of generation based on advice relevance 2. The system of claim 1, (Appendix 26) The providing unit is Inferring a user's emotions and adjusting the way advice is provided based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The providing unit is When providing the service, the optimal method of providing the service is selected by referring to the user's past feedback. 2. The system of claim 1, (Appendix 28) The providing unit is At the time of delivery, the advice is tailored based on the user's current life situation and interests 2. The system of claim 1, (Appendix 29) The providing unit is The system estimates the user's emotions and adjusts the order of advice provided based on the estimated user emotions. 2. The system of claim 1, (Appendix 30) The providing unit is When providing information, the optimal method of providing the information is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 31) The providing unit is When provided, advice is available in multiple languages ​​depending on the user's language settings 2. The system of claim 1, [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives an image or data of a user; an analysis unit that analyzes the data accepted by the acceptance unit; a generation unit that generates advice based on the analysis result obtained by the analysis unit; a providing unit that provides the advice generated by the generating unit. A system characterized by:

2. The reception unit is Accepts image and text data entered by the user 2. The system of claim 1 .

3. The analysis unit is Analyzing the user's image or data and comparing it with the image or data of a target person 2. The system of claim 1 .

4. The generation unit is Based on the analysis results, the system generates appropriate makeup, hairstyle, and body shape improvement advice for the user.

2. The system of claim 1 .

5. The generation unit is Learn to improve the accuracy of generative AI 2. The system of claim 1 .

6. The providing unit is Collect user feedback to help improve the accuracy of generative AI 2. The system of claim 1 .

7. The reception unit is Estimate the user's emotions and adjust the timing of image and data input based on the estimated user emotions.

2. The system of claim 1 .

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