System

Abstract

An object of the system according to the embodiment is to recommend an AI application most suitable for a user and explain how to use the AI application and features of the AI application.SOLUTION: A system according to an embodiment includes an input unit, an analysis unit, a recommendation unit, and an explanation unit. The input unit inputs a need of a user. The analysis unit analyzes the information input by the input unit. The recommendation unit recommends the AI application based on the information analyzed by the analysis unit. The explanation section explains how to use the AI application recommended by the recommendation section and features of the AI application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to find the best AI app for you from the growing number of available apps, and it was difficult to choose which one to use.

[0005] The system according to the embodiment aims to recommend the most suitable AI app to the user and explain how to use it and its features. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a recommendation unit, and an explanation unit. The input unit inputs user needs. The analysis unit analyzes the information input by the input unit. The recommendation unit recommends an AI app based on the information analyzed by the analysis unit. The explanation unit explains how to use and the features of the AI ​​app recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend the most suitable AI app to the user and explain how to use it and its features. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI portal app according to an embodiment of the present invention is a system that allows users to easily find and efficiently use the AI ​​app that best suits them. The AI ​​portal app allows users to input their needs and objectives, analyzes the information, recommends the most suitable AI app, and explains how to use and its features. For example, if a user is looking for an AI app that specializes in a specific function, the AI ​​portal app recommends an AI app that specializes in that function and provides detailed explanations of how to use and its features. This allows users to easily find and efficiently use the AI ​​app that best suits them. The AI ​​portal app allows users to easily find and efficiently use the AI ​​app that best suits them. For example, a user looking for an AI app that specializes in a specific function can use the AI ​​portal app to find the optimal app and understand how to use it. This solves the problem of how difficult it is to find and use the optimal AI app amid the proliferation of AI apps and services.

[0029] An AI portal app according to an embodiment includes an input unit, an analysis unit, a recommendation unit, and an explanation unit. The input unit inputs user needs. User needs include, but are not limited to, technical needs, business needs, and personal needs. For example, if a user is looking for an AI app specialized in a specific function, the input unit can input the user's specific request. The analysis unit analyzes the information input by the input unit. The analysis can be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit analyzes information based on the user's needs and objectives and generates data for recommending an optimal AI app. The recommendation unit recommends an optimal AI app based on the information analyzed by the analysis unit. The recommendation can be performed based on, but is not limited to, criteria such as the user's past behavior history and the behavior patterns of similar users. For example, if a user is looking for an AI app specialized in a specific function, the recommendation unit recommends an AI app specialized in that function. The explanation unit explains how to use and the features of the AI ​​app recommended by the recommendation unit. The explanation is provided by, for example, text explanation, visual explanation, audio explanation, etc., but is not limited to these examples. The explanation unit, for example, provides a detailed explanation of how to use and features of an AI app specialized for a specific function, supporting the user in easily selecting the appropriate AI app. As a result, the AI ​​portal app according to the embodiment recommends the optimal AI app based on the user's needs and explains how to use and features of the recommended AI app, allowing the user to efficiently select the appropriate AI app.

[0030] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display as candidates the needs and purposes that the user has frequently input in the past. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest needs and purposes to be used in a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history.

[0031] The input unit can filter input content based on the user's current situation and environment at the time of input. For example, when the user is at work, the input unit can prioritize displaying work-related needs and objectives. Furthermore, when the user is at home, the input unit can prioritize displaying home-related needs and objectives. Furthermore, when the user is traveling, the input unit can prioritize displaying travel-related needs and objectives. This allows the user to input more appropriate information by providing input content that is appropriate for the user's current situation and environment.

[0032] The input unit can select the optimal input means depending on the user's input method when inputting. For example, if the user selects voice input, the input unit inputs the user's needs and purpose using voice recognition technology. Furthermore, if the user selects text input, the input unit can also support keyboard input. Furthermore, if the user selects image input, the input unit can also analyze the user's needs and purpose using image recognition technology. This improves input efficiency by providing the optimal means depending on the user's input method.

[0033] The input unit can prioritize input of highly relevant information taking into consideration the user's geographical location information when inputting information. For example, when the user is in a specific area, the input unit can prioritize displaying AI apps related to that area. Furthermore, when the user is traveling, the input unit can prioritize displaying AI apps related to the travel destination. Furthermore, when the user is at home, the input unit can prioritize displaying home-related AI apps. This allows the user to input more appropriate information by providing highly relevant information based on the user's geographical location information.

[0034] The input unit can analyze the user's social media activities at the time of input and input relevant information. For example, the input unit can suggest related AI apps based on information shared by the user on social media. The input unit can also analyze the user's social media activities and input relevant needs and goals. The input unit can also suggest related AI apps based on the activities of the user's friends on social media. This allows the user to input more appropriate information by providing relevant information based on the user's social media activities.

[0035] The input unit can customize the input method by reflecting the user's past feedback when inputting. For example, the input unit can suggest the optimal input method based on the user's past feedback. The input unit can also preferentially display a specific input method based on the user's past feedback. The input unit can also analyze the user's past feedback and customize the input interface. This improves input efficiency by customizing the input method based on the user's past feedback.

[0036] The analysis unit can improve the accuracy of the analysis by referring to the user's past needs and goals during analysis. The analysis unit improves the accuracy of the analysis, for example, based on the needs and goals entered by the user in the past. The analysis unit can also recommend the most suitable AI app by referring to the user's past needs and goals. The analysis unit can also analyze the user's past needs and goals and improve the analysis algorithm. In this way, the accuracy of the analysis is improved by referring to the user's past needs and goals.

[0037] The analysis unit can customize the analysis content based on the user's current situation and environment during analysis. For example, if the user is at work, the analysis unit can prioritize analysis of work-related needs and purposes. Also, if the user is at home, the analysis unit can prioritize analysis of home-related needs and purposes. Also, if the user is traveling, the analysis unit can prioritize analysis of travel-related needs and purposes. This allows for more appropriate analysis information to be provided by providing analysis content that is tailored to the user's current situation and environment.

[0038] The analysis unit can improve the analysis algorithm by reflecting user feedback during analysis. For example, the analysis unit improves the analysis algorithm based on feedback provided by the user. The analysis unit can also improve the accuracy of the analysis by referring to past user feedback. The analysis unit can also analyze user feedback and optimize the analysis algorithm. In this way, the analysis accuracy is improved by improving the analysis algorithm based on user feedback.

[0039] The analysis unit can adjust the analysis content by taking into account the user's geographical location information during analysis. For example, if the user is in a specific area, the analysis unit can prioritize analyzing AI apps related to that area. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing AI apps related to the travel destination. Furthermore, if the user is at home, the analysis unit can prioritize analyzing home-related AI apps. This allows for more appropriate information to be analyzed by providing highly relevant information based on the user's geographical location information.

[0040] The analysis unit can analyze the user's social media activities during analysis and analyze related information. For example, the analysis unit can analyze related AI apps based on information shared by the user on social media. The analysis unit can also analyze the user's social media activities and analyze related needs and purposes. The analysis unit can also analyze related AI apps based on the activities of the user's friends on social media. This allows for more appropriate information to be analyzed by providing related information based on the user's social media activities.

[0041] The analysis unit can customize the analysis method by reflecting the user's past feedback during analysis. The analysis unit customizes the analysis method based on, for example, feedback provided by the user. The analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. The analysis unit can also analyze the user's feedback and optimize the analysis method. In this way, the accuracy of the analysis is improved by customizing the analysis method based on the user's past feedback.

[0042] The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation history when making recommendations. For example, the recommendation unit improves the accuracy of recommendations based on AI apps that have been recommended to the user in the past. The recommendation unit can also recommend the most suitable AI app by referring to the user's past recommendation history. The recommendation unit can also analyze the user's past recommendation history and improve the recommendation algorithm. In this way, the accuracy of recommendations is improved by referring to the user's past recommendation history.

[0043] The recommendation unit can customize the recommendation content based on the user's current situation and environment at the time of recommendation. For example, if the user is at work, the recommendation unit can prioritize recommending work-related AI apps. Also, if the user is at home, the recommendation unit can prioritize recommending home-related AI apps. Also, if the user is traveling, the recommendation unit can prioritize recommending travel-related AI apps. This makes it possible to recommend more appropriate AI apps by providing recommendation content that is tailored to the user's current situation and environment.

[0044] The recommendation unit can improve the recommendation algorithm by reflecting user feedback when making recommendations. For example, the recommendation unit improves the recommendation algorithm based on feedback provided by the user. The recommendation unit can also improve the accuracy of recommendations by referring to the user's past feedback. The recommendation unit can also analyze the user's feedback and optimize the recommendation algorithm. In this way, the recommendation algorithm can be improved based on the user's feedback, thereby improving the accuracy of recommendations.

[0045] The recommendation unit can adjust the recommendation content by taking into account the user's geographical location information when making a recommendation. For example, if the user is in a specific area, the recommendation unit can prioritize recommending AI apps related to that area. Also, if the user is traveling, the recommendation unit can prioritize recommending AI apps related to the travel destination. Also, if the user is at home, the recommendation unit can prioritize recommending home-related AI apps. This allows the system to provide more appropriate information by recommending highly relevant AI apps based on the user's geographical location information.

[0046] The recommendation unit can analyze the user's social media activities and recommend related information when making a recommendation. For example, the recommendation unit can recommend related AI apps based on information shared by the user on social media. The recommendation unit can also analyze the user's social media activities and recommend related needs or purposes. The recommendation unit can also recommend related AI apps based on the activities of the user's friends on social media. This makes it possible to provide more appropriate information by recommending related AI apps based on the user's social media activities.

[0047] The recommendation unit can customize the recommendation method by reflecting the user's past feedback when making a recommendation. The recommendation unit customizes the recommendation method based on, for example, feedback provided by the user. The recommendation unit can also improve the accuracy of recommendations by referring to the user's past feedback. The recommendation unit can also analyze the user's feedback and optimize the recommendation method. In this way, the recommendation method can be customized based on the user's past feedback, thereby improving the accuracy of recommendations.

[0048] The explanation unit can improve the accuracy of the explanation by referring to the user's past usage history when providing an explanation. The explanation unit can improve the accuracy of the explanation based on, for example, the history of AI apps that the user has used in the past. The explanation unit can also provide an optimal explanation by referring to the user's past usage history. The explanation unit can also analyze the user's past usage history and improve the explanation method. In this way, by improving the accuracy of the explanation based on the user's past usage history, more appropriate explanations can be provided.

[0049] The explanation unit can customize the explanation content based on the user's current situation and environment when providing the explanation. For example, if the user is at work, the explanation unit can prioritize providing work-related explanations. Also, if the user is at home, the explanation unit can prioritize providing home-related explanations. Also, if the user is traveling, the explanation unit can prioritize providing travel-related explanations. This allows for more appropriate explanations by providing explanation content that is appropriate to the user's current situation and environment.

[0050] The explanation unit can improve the explanation method by reflecting the user's feedback during explanation. For example, the explanation unit improves the explanation method based on the feedback provided by the user. The explanation unit can also improve the accuracy of the explanation by referring to the user's past feedback. The explanation unit can also analyze the user's feedback and optimize the explanation method. In this way, the explanation method can be improved based on the user's feedback, thereby improving the accuracy of the explanation.

[0051] The explanation unit can adjust the content of the explanation by taking into account the user's geographical location information when providing an explanation. For example, if the user is in a specific area, the explanation unit can provide an explanation related to that area. If the user is traveling, the explanation unit can also provide an explanation related to the travel destination. If the user is at home, the explanation unit can also provide an explanation related to the home. This allows for providing more relevant information by providing an explanation that is highly relevant based on the user's geographical location information.

[0052] The explanation unit can analyze the user's social media activity and explain related information when providing an explanation. The explanation unit can provide a related explanation based on, for example, information shared by the user on social media. The explanation unit can also analyze the user's social media activity and provide a related explanation. The explanation unit can also provide a related explanation by referring to the activity of the user's friends on social media. This allows for more appropriate explanations by providing related information based on the user's social media activity.

[0053] The explanation unit can customize the explanation method by reflecting the user's past feedback when giving an explanation. The explanation unit customizes the explanation method based on, for example, feedback provided by the user. The explanation unit can also improve the accuracy of the explanation by referring to the user's past feedback. The explanation unit can also analyze the user's feedback and optimize the explanation method. In this way, the explanation method can be customized based on the user's past feedback, thereby improving the accuracy of the explanation.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The input unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, if the user is in a specific area, AI apps related to that area can be displayed with priority. Also, if the user is traveling, AI apps related to the travel destination can be displayed with priority. Furthermore, if the user is at home, AI apps related to home use can be displayed with priority. This allows more appropriate information to be input by providing highly relevant information based on the user's geographical location information.

[0056] The analysis unit can improve the accuracy of the analysis by referring to the user's past needs and goals during analysis. For example, the accuracy of the analysis can be improved based on the needs and goals entered by the user in the past. It can also recommend the most suitable AI app by referring to the user's past needs and goals. It can also analyze the user's past needs and goals and improve the analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the user's past needs and goals.

[0057] The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation history when making recommendations. For example, the recommendation unit can improve the accuracy of recommendations based on AI apps that have been recommended to the user in the past. It can also recommend the most suitable AI app by referring to the user's past recommendation history. It can also analyze the user's past recommendation history and improve the recommendation algorithm. In this way, the accuracy of recommendations can be improved by referring to the user's past recommendation history.

[0058] The explanation unit can improve the accuracy of the explanation by referring to the user's past usage history when providing an explanation. For example, the explanation unit can improve the accuracy of the explanation based on the history of AI apps that the user has used in the past. The explanation unit can also provide the most appropriate explanation by referring to the user's past usage history. Furthermore, the explanation unit can analyze the user's past usage history and improve the explanation method. This makes it possible to provide more appropriate explanations by improving the accuracy of the explanation based on the user's past usage history.

[0059] The input unit can analyze the user's social media activity during input and input relevant information. For example, it can suggest related AI apps based on information the user has shared on social media. It can also analyze the user's social media activity and input relevant needs and goals. It can also suggest related AI apps based on the activity of the user's friends on social media. This allows the input of more appropriate information by providing relevant information based on the user's social media activity.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The input section inputs user needs. User needs can include technical needs, business needs, personal needs, etc. For example, if a user is looking for an AI app that specializes in a specific function, they can input their specific request. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the information based on the user's needs and objectives and generates data to recommend the most suitable AI app. Step 3: The recommendation unit recommends the most suitable AI app based on the information analyzed by the analysis unit. Recommendations are made based on criteria such as the user's past behavioral history and the behavioral patterns of similar users. For example, if a user is looking for an AI app that specializes in a specific function, an AI app that specializes in that function will be recommended. Step 4: The explanation section explains how to use and the features of the AI ​​app recommended by the recommendation section. The explanation can be done using text, visual, audio, or other methods. For example, it can provide a detailed explanation of how to use and the features of an AI app that specializes in a specific function, helping users to easily select the right app to use.

[0062] (Example 2) An AI portal app according to an embodiment of the present invention is a system that allows users to easily find and efficiently use the AI ​​app that best suits them. The AI ​​portal app allows users to input their needs and objectives, analyzes the information, recommends the most suitable AI app, and explains how to use and its features. For example, if a user is looking for an AI app that specializes in a specific function, the AI ​​portal app recommends an AI app that specializes in that function and provides detailed explanations of how to use and its features. This allows users to easily find and efficiently use the AI ​​app that best suits them. The AI ​​portal app allows users to easily find and efficiently use the AI ​​app that best suits them. For example, a user looking for an AI app that specializes in a specific function can use the AI ​​portal app to find the optimal app and understand how to use it. This solves the problem of how difficult it is to find and use the optimal AI app amid the proliferation of AI apps and services.

[0063] An AI portal app according to an embodiment includes an input unit, an analysis unit, a recommendation unit, and an explanation unit. The input unit inputs user needs. User needs include, but are not limited to, technical needs, business needs, and personal needs. For example, if a user is looking for an AI app specialized in a specific function, the input unit can input the user's specific request. The analysis unit analyzes the information input by the input unit. The analysis can be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit analyzes information based on the user's needs and objectives and generates data for recommending an optimal AI app. The recommendation unit recommends an optimal AI app based on the information analyzed by the analysis unit. The recommendation can be performed based on, but is not limited to, criteria such as the user's past behavior history and the behavior patterns of similar users. For example, if a user is looking for an AI app specialized in a specific function, the recommendation unit recommends an AI app specialized in that function. The explanation unit explains how to use and the features of the AI ​​app recommended by the recommendation unit. The explanation is provided by, for example, text explanation, visual explanation, audio explanation, etc., but is not limited to these examples. The explanation unit, for example, provides a detailed explanation of how to use and features of an AI app specialized for a specific function, supporting the user in easily selecting the appropriate AI app. As a result, the AI ​​portal app according to the embodiment recommends the optimal AI app based on the user's needs and explains how to use and features of the recommended AI app, allowing the user to efficiently select the appropriate AI app.

[0064] The AI ​​portal app further includes an input unit that estimates a user's emotions and adjusts the display method of the input interface based on the estimated user emotions. For example, when the user is stressed, the input unit provides a simple interface and minimizes input steps. Furthermore, when the user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the input unit can prioritize voice input, allowing the user to quickly input their needs and objectives. This allows the input interface to be adjusted according to the user's emotions, allowing the user to input without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The input unit can analyze the user's past input history and suggest the optimal input method. For example, the input unit can automatically display as candidates the needs and purposes that the user has frequently input in the past. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest needs and purposes to be used in a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history.

[0066] The input unit can filter input content based on the user's current situation and environment at the time of input. For example, when the user is at work, the input unit can prioritize displaying work-related needs and objectives. Furthermore, when the user is at home, the input unit can prioritize displaying home-related needs and objectives. Furthermore, when the user is traveling, the input unit can prioritize displaying travel-related needs and objectives. This allows the user to input more appropriate information by providing input content that is appropriate for the user's current situation and environment.

[0067] The input unit can select the optimal input means depending on the user's input method when inputting. For example, if the user selects voice input, the input unit inputs the user's needs and purpose using voice recognition technology. Furthermore, if the user selects text input, the input unit can also support keyboard input. Furthermore, if the user selects image input, the input unit can also analyze the user's needs and purpose using image recognition technology. This improves input efficiency by providing the optimal means depending on the user's input method.

[0068] The input unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, when the user is feeling stressed, the input unit can prioritize displaying important needs and goals. Furthermore, when the user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the input unit can provide concise input options to enable quick input. This allows important information to be input preferentially by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The input unit can prioritize input of highly relevant information taking into consideration the user's geographical location information when inputting information. For example, when the user is in a specific area, the input unit can prioritize displaying AI apps related to that area. Furthermore, when the user is traveling, the input unit can prioritize displaying AI apps related to the travel destination. Furthermore, when the user is at home, the input unit can prioritize displaying home-related AI apps. This allows the user to input more appropriate information by providing highly relevant information based on the user's geographical location information.

[0070] The input unit can analyze the user's social media activities at the time of input and input relevant information. For example, the input unit can suggest related AI apps based on information shared by the user on social media. The input unit can also analyze the user's social media activities and input relevant needs and goals. The input unit can also suggest related AI apps based on the activities of the user's friends on social media. This allows the user to input more appropriate information by providing relevant information based on the user's social media activities.

[0071] The input unit can customize the input method by reflecting the user's past feedback when inputting. For example, the input unit can suggest the optimal input method based on the user's past feedback. The input unit can also preferentially display a specific input method based on the user's past feedback. The input unit can also analyze the user's past feedback and customize the input interface. This improves input efficiency by customizing the input method based on the user's past feedback.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and recommends the most appropriate AI app. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and recommend the most appropriate AI app. Furthermore, if the user is stressed, the analysis unit can perform a simple analysis and recommend the most appropriate AI app. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The analysis unit can improve the accuracy of the analysis by referring to the user's past needs and goals during analysis. The analysis unit improves the accuracy of the analysis, for example, based on the needs and goals entered by the user in the past. The analysis unit can also recommend the most suitable AI app by referring to the user's past needs and goals. The analysis unit can also analyze the user's past needs and goals and improve the analysis algorithm. In this way, the accuracy of the analysis is improved by referring to the user's past needs and goals.

[0074] The analysis unit can customize the analysis content based on the user's current situation and environment during analysis. For example, if the user is at work, the analysis unit can prioritize analysis of work-related needs and purposes. Also, if the user is at home, the analysis unit can prioritize analysis of home-related needs and purposes. Also, if the user is traveling, the analysis unit can prioritize analysis of travel-related needs and purposes. This allows for more appropriate analysis information to be provided by providing analysis content that is tailored to the user's current situation and environment.

[0075] The analysis unit can improve the analysis algorithm by reflecting user feedback during analysis. For example, the analysis unit improves the analysis algorithm based on feedback provided by the user. The analysis unit can also improve the accuracy of the analysis by referring to past user feedback. The analysis unit can also analyze user feedback and optimize the analysis algorithm. In this way, the analysis accuracy is improved by improving the analysis algorithm based on user feedback.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, making it possible to display results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The analysis unit can adjust the analysis content by taking into account the user's geographical location information during analysis. For example, if the user is in a specific area, the analysis unit can prioritize analyzing AI apps related to that area. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing AI apps related to the travel destination. Furthermore, if the user is at home, the analysis unit can prioritize analyzing home-related AI apps. This allows for more appropriate information to be analyzed by providing highly relevant information based on the user's geographical location information.

[0078] The analysis unit can analyze the user's social media activities during analysis and analyze related information. For example, the analysis unit can analyze related AI apps based on information shared by the user on social media. The analysis unit can also analyze the user's social media activities and analyze related needs and purposes. The analysis unit can also analyze related AI apps based on the activities of the user's friends on social media. This allows for more appropriate information to be analyzed by providing related information based on the user's social media activities.

[0079] The analysis unit can customize the analysis method by reflecting the user's past feedback during analysis. The analysis unit customizes the analysis method based on, for example, feedback provided by the user. The analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. The analysis unit can also analyze the user's feedback and optimize the analysis method. In this way, the accuracy of the analysis is improved by customizing the analysis method based on the user's past feedback.

[0080] The recommendation unit can estimate the user's emotions and adjust the recommendation algorithm based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can make detailed recommendations and suggest the most suitable AI app. Also, if the user is in a hurry, the recommendation unit can make quick recommendations and suggest the most suitable AI app. Also, if the user is stressed, the recommendation unit can make simple recommendations and suggest the most suitable AI app. This improves the accuracy of recommendations by adjusting the recommendation algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation history when making recommendations. For example, the recommendation unit improves the accuracy of recommendations based on AI apps that have been recommended to the user in the past. The recommendation unit can also recommend the most suitable AI app by referring to the user's past recommendation history. The recommendation unit can also analyze the user's past recommendation history and improve the recommendation algorithm. In this way, the accuracy of recommendations is improved by referring to the user's past recommendation history.

[0082] The recommendation unit can customize the recommendation content based on the user's current situation and environment at the time of recommendation. For example, if the user is at work, the recommendation unit can prioritize recommending work-related AI apps. Also, if the user is at home, the recommendation unit can prioritize recommending home-related AI apps. Also, if the user is traveling, the recommendation unit can prioritize recommending travel-related AI apps. This makes it possible to recommend more appropriate AI apps by providing recommendation content that is tailored to the user's current situation and environment.

[0083] The recommendation unit can improve the recommendation algorithm by reflecting user feedback when making recommendations. For example, the recommendation unit improves the recommendation algorithm based on feedback provided by the user. The recommendation unit can also improve the accuracy of recommendations by referring to the user's past feedback. The recommendation unit can also analyze the user's feedback and optimize the recommendation algorithm. In this way, the recommendation algorithm can be improved based on the user's feedback, thereby improving the accuracy of recommendations.

[0084] The recommendation unit can estimate the user's emotions and adjust the display method of the recommendation results based on the estimated user emotions. For example, if the user is nervous, the recommendation unit can provide a simple, highly visible display method. If the user is relaxed, the recommendation unit can also provide a display method that includes detailed information. If the user is in a hurry, the recommendation unit can also provide a display method that focuses on the main points. This allows the display method of the recommendation results to be adjusted according to the user's emotions, making it possible to display results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The recommendation unit can adjust the recommendation content by taking into account the user's geographical location information when making a recommendation. For example, if the user is in a specific area, the recommendation unit can prioritize recommending AI apps related to that area. Also, if the user is traveling, the recommendation unit can prioritize recommending AI apps related to the travel destination. Also, if the user is at home, the recommendation unit can prioritize recommending home-related AI apps. This allows the system to provide more appropriate information by recommending highly relevant AI apps based on the user's geographical location information.

[0086] The recommendation unit can analyze the user's social media activities and recommend related information when making a recommendation. For example, the recommendation unit can recommend related AI apps based on information shared by the user on social media. The recommendation unit can also analyze the user's social media activities and recommend related needs or purposes. The recommendation unit can also recommend related AI apps based on the activities of the user's friends on social media. This makes it possible to provide more appropriate information by recommending related AI apps based on the user's social media activities.

[0087] The recommendation unit can customize the recommendation method by reflecting the user's past feedback when making a recommendation. The recommendation unit customizes the recommendation method based on, for example, feedback provided by the user. The recommendation unit can also improve the accuracy of recommendations by referring to the user's past feedback. The recommendation unit can also analyze the user's feedback and optimize the recommendation method. In this way, the recommendation method can be customized based on the user's past feedback, thereby improving the accuracy of recommendations.

[0088] The explanation unit can estimate the user's emotions and adjust the way the explanation is expressed based on the estimated user's emotions. For example, if the user is nervous, the explanation unit can provide a simple, highly visible explanation. If the user is relaxed, the explanation unit can also provide an explanation including detailed information. If the user is in a hurry, the explanation unit can also provide a concise explanation that focuses on the main points. This allows the explanation to be easily understood by adjusting the way the explanation is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The explanation unit can improve the accuracy of the explanation by referring to the user's past usage history when providing an explanation. The explanation unit can improve the accuracy of the explanation based on, for example, the history of AI apps that the user has used in the past. The explanation unit can also provide an optimal explanation by referring to the user's past usage history. The explanation unit can also analyze the user's past usage history and improve the explanation method. In this way, by improving the accuracy of the explanation based on the user's past usage history, more appropriate explanations can be provided.

[0090] The explanation unit can customize the explanation content based on the user's current situation and environment when providing the explanation. For example, if the user is at work, the explanation unit can prioritize providing work-related explanations. Also, if the user is at home, the explanation unit can prioritize providing home-related explanations. Also, if the user is traveling, the explanation unit can prioritize providing travel-related explanations. This allows for more appropriate explanations by providing explanation content that is appropriate to the user's current situation and environment.

[0091] The explanation unit can improve the explanation method by reflecting the user's feedback during explanation. For example, the explanation unit improves the explanation method based on the feedback provided by the user. The explanation unit can also improve the accuracy of the explanation by referring to the user's past feedback. The explanation unit can also analyze the user's feedback and optimize the explanation method. In this way, the explanation method can be improved based on the user's feedback, thereby improving the accuracy of the explanation.

[0092] The explanation unit can estimate the user's emotions and adjust the length of the explanation based on the estimated user emotions. For example, if the user is nervous, the explanation unit can provide a short, to-the-point explanation. If the user is relaxed, the explanation unit can also provide a detailed explanation. If the user is in a hurry, the explanation unit can also provide a concise, quick explanation. This allows the explanation to be easily understood by adjusting the length of the explanation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The explanation unit can adjust the content of the explanation by taking into account the user's geographical location information when providing an explanation. For example, if the user is in a specific area, the explanation unit can provide an explanation related to that area. If the user is traveling, the explanation unit can also provide an explanation related to the travel destination. If the user is at home, the explanation unit can also provide an explanation related to the home. This allows for providing more relevant information by providing an explanation that is highly relevant based on the user's geographical location information.

[0094] The explanation unit can analyze the user's social media activity and explain related information when providing an explanation. The explanation unit can provide a related explanation based on, for example, information shared by the user on social media. The explanation unit can also analyze the user's social media activity and provide a related explanation. The explanation unit can also provide a related explanation by referring to the activity of the user's friends on social media. This allows for more appropriate explanations by providing related information based on the user's social media activity.

[0095] The explanation unit can customize the explanation method by reflecting the user's past feedback when giving an explanation. The explanation unit customizes the explanation method based on, for example, feedback provided by the user. The explanation unit can also improve the accuracy of the explanation by referring to the user's past feedback. The explanation unit can also analyze the user's feedback and optimize the explanation method. In this way, the explanation method can be customized based on the user's past feedback, thereby improving the accuracy of the explanation. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, recommendation unit, and explanation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14 and inputs the user's needs. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal AI app based on the analyzed information. The explanation unit is realized by the output device 40 of the smart device 14 and explains how to use and the features of the recommended AI app. Furthermore, the input unit with an emotion estimation function estimates the user's emotion using, for example, the camera 42 or microphone 38B of the smart device 14 and adjusts the input interface. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned input unit, analysis unit, recommendation unit, and explanation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214 and inputs the user's needs. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal AI app based on the analyzed information. The explanation unit is realized by the speaker 240 of the smart glasses 214 and explains how to use and the features of the recommended AI app. Furthermore, the input unit with an emotion estimation function estimates the user's emotion using, for example, the camera 42 and microphone 238 of the smart glasses 214 and adjusts the input interface. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned input unit, analysis unit, recommendation unit, and explanation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314 and inputs the user's needs. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal AI app based on the analyzed information. The explanation unit is realized by the speaker 240 of the headset-type terminal 314 and explains how to use and the features of the recommended AI app. Furthermore, the input unit with an emotion estimation function estimates the user's emotion using, for example, the camera 42 or microphone 238 of the headset-type terminal 314 and adjusts the input interface. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, recommendation unit, and explanation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414 and inputs the user's needs. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal AI app based on the analyzed information. The explanation unit is realized by the speaker 240 of the robot 414 and explains how to use and the features of the recommended AI app. Furthermore, the input unit with an emotion estimation function estimates the user's emotions using, for example, the camera 42 and microphone 238 of the robot 414 and adjusts the input interface.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to display the results in a way that is easy for the user to understand.

[0098] The recommendation unit can estimate the user's emotions and adjust the recommendation algorithm based on the estimated user emotions. For example, if the user is relaxed, detailed recommendations can be made and the most suitable AI app can be suggested. If the user is in a hurry, quick recommendations can be made and the most suitable AI app can be suggested. Furthermore, if the user is stressed, simple recommendations can be made and the most suitable AI app can be suggested. This improves the accuracy of recommendations by adjusting the recommendation algorithm according to the user's emotions.

[0099] The explanation unit can estimate the user's emotions and adjust the way the explanation is presented based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible explanation can be provided. If the user is relaxed, an explanation including detailed information can be provided. Furthermore, if the user is in a hurry, a concise explanation that focuses on the main points can be provided. In this way, by adjusting the way the explanation is presented according to the user's emotions, it is possible to provide an explanation that is easy for the user to understand.

[0100] The input unit can estimate the user's emotions and prioritize input content based on the estimated user's emotions. For example, if the user is feeling stressed, important needs and goals can be displayed with priority. If the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, simple input options can be provided to enable quick input. In this way, by prioritizing input content according to the user's emotions, important information can be input with priority.

[0101] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed and the most suitable AI app can be recommended. If the user is in a hurry, a quick analysis can be performed and the most suitable AI app can be recommended. Furthermore, if the user is feeling stressed, a simple analysis can be performed and the most suitable AI app can be recommended. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotions.

[0102] The input unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, if the user is in a specific area, AI apps related to that area can be displayed with priority. Also, if the user is traveling, AI apps related to the travel destination can be displayed with priority. Furthermore, if the user is at home, AI apps related to home use can be displayed with priority. This allows more appropriate information to be input by providing highly relevant information based on the user's geographical location information.

[0103] The analysis unit can improve the accuracy of the analysis by referring to the user's past needs and goals during analysis. For example, the accuracy of the analysis can be improved based on the needs and goals entered by the user in the past. It can also recommend the most suitable AI app by referring to the user's past needs and goals. It can also analyze the user's past needs and goals and improve the analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the user's past needs and goals.

[0104] The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation history when making recommendations. For example, the recommendation unit can improve the accuracy of recommendations based on AI apps that have been recommended to the user in the past. It can also recommend the most suitable AI app by referring to the user's past recommendation history. It can also analyze the user's past recommendation history and improve the recommendation algorithm. In this way, the accuracy of recommendations can be improved by referring to the user's past recommendation history.

[0105] The explanation unit can improve the accuracy of the explanation by referring to the user's past usage history when providing an explanation. For example, the explanation unit can improve the accuracy of the explanation based on the history of AI apps that the user has used in the past. The explanation unit can also provide the most appropriate explanation by referring to the user's past usage history. Furthermore, the explanation unit can analyze the user's past usage history and improve the explanation method. This makes it possible to provide more appropriate explanations by improving the accuracy of the explanation based on the user's past usage history.

[0106] The input unit can analyze the user's social media activity during input and input relevant information. For example, it can suggest related AI apps based on information the user has shared on social media. It can also analyze the user's social media activity and input relevant needs and goals. It can also suggest related AI apps based on the activity of the user's friends on social media. This allows the input of more appropriate information by providing relevant information based on the user's social media activity.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The input section inputs user needs. User needs can include technical needs, business needs, personal needs, etc. For example, if a user is looking for an AI app that specializes in a specific function, they can input their specific request. Step 2: The analysis unit analyzes the information input by the input unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the information based on the user's needs and objectives and generates data to recommend the most suitable AI app. Step 3: The recommendation unit recommends the most suitable AI app based on the information analyzed by the analysis unit. Recommendations are made based on criteria such as the user's past behavioral history and the behavioral patterns of similar users. For example, if a user is looking for an AI app that specializes in a specific function, an AI app that specializes in that function will be recommended. Step 4: The explanation section explains how to use and the features of the AI ​​app recommended by the recommendation section. The explanation can be done using text, visual, audio, or other methods. For example, it can provide a detailed explanation of how to use and the features of an AI app that specializes in a specific function, helping users to easily select the right app to use.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] 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.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] 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.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] 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.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 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. an input unit for inputting user needs; an analysis unit that analyzes the information input by the input unit; a recommendation unit that recommends an AI application based on the information analyzed by the analysis unit; and an explanation unit that explains how to use and features of the AI ​​app recommended by the recommendation unit. A system characterized by:

2. The input unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

3. The input unit Analyzes the user's past input history and suggests input methods 2. The system of claim 1.

4. The input unit Filter input as you type based on the user's current situation or environment 2. The system of claim 1.

5. The input unit Select an input method according to the user's input method when inputting 2. The system of claim 1.

6. The input unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

7. The input unit Prioritize relevant information by taking into account the user's geographic location as they type 2. The system of claim 1.

8. The input unit Analyze your social media activity as you type and populate relevant information 2. The system of claim 1.

Images